Tech For Good - Thought Leadership

Balancing AI growth with the data centre sector’s sustainability challenge

Terry Storrar — Fri, 28 Nov 2025 15:28:37 +0000

Like many countries worldwide, the UK is investing billions in AI infrastructure as part of its ambition to become an AI superpower. This government-led strategy is being accompanied by a steady stream of industry announcements, with the likes of Microsoft and Google committing hundreds of millions of pounds to fund new data centres.

Over the next five years, in fact, the number of UK data centres is predicted to grow by around 100, an increase of nearly 20%. The key driver of this massive growth is, of course, AI, which is driving unprecedented demand for compute capacity.

Fundamentally, AI workloads are pushing compute density to new levels, driving far higher energy consumption than traditional applications. Recent research from Gartner is predicting the energy demand for data centres to rise by 16% in 2025 alone and doubling by 2030. The resulting heat output is increasing reliance on advanced cooling, including water-based systems that still add pressure to limited resources.

As capacity increases at this pace, so too does scrutiny of the sector’s energy use, carbon output and compliance with tightening sustainability expectations. Google’s proposed new Essex facility alone is expected to emit as much carbon as 500 short-haul flights every week, but it also represents just a fraction of the total environmental impact expected from the industry as a whole. Indeed, if energy consumption predictions turn out to be correct, the amount of electricity required by AI systems could equate to nearly half of total global data centre power consumption by the end of this year.

An industry under scrutiny

This puts operators in a difficult position, as they face conflicting pressures to deliver the infrastructure needed to embrace the AI boom while also complying with stricter sustainability regulations and meeting customer demand for greener facilities.

These issues are far from new, with the data centre industry under environmental scrutiny long before AI took off in spectacular fashion just three years ago. In recent years, regulators have been actively increasing oversight, with rules such as the Energy Efficiency Directive (EED) requiring operators to report energy-efficiency KPIs and demonstrate the use of renewables.

Customers are also applying pressure, with sustainability and certifications such as ISO 14001:2015 now among the leading criteria in data centre procurement decisions. The result is an extremely challenging environment where operators must show continuous progress rather than one-off compliance.

The United Nations Environment Programme (UNEP) has warned that AI has an environmental problem, identifying data centres as a focal point due to the scale of power and cooling required. As adoption accelerates, operators must support rising performance expectations without creating unsustainable strain on their infrastructure or local energy supply.

Improving environmental performance

So, what is being done to try and balance both priorities? Among the main initiatives are new cooling methods, including water-cooled servers and fully water-cooled racks, designed to more efficiently manage energy resources

There is also growing use of renewable power sources as operators work to reduce reliance on the legacy generation and neutralise carbon output. Google is an interesting example of this approach, having matched 100% of its annual global electricity consumption with renewable energy since 2017. This doesn’t mean that every Google data centre server is physically powered by renewable energy in real time, but the company purchases the same amount of renewable electricity needed to power its data centre infrastructure.

These and other industry initiatives are essential, but there is a long way to go. According to the International Energy Agency (IEA), data centres currently source about 27% of their electricity from renewables, and these sources are expected to provide enough additional electricity to raise that share to about 50% by 2030.

Public awareness and demand for positive change also means sustainability is becoming a competitive differentiator, with operators actively promoting their green credentials and those of their wider supply chain. The most forward-thinking providers now compete on sustainability performance as much as on availability or security.

A complex ecosystem

If these issues weren’t enough to contend with, data sovereignty, which dictates where and how data can be stored and processed, has become significantly more important, with world events and regulatory developments influencing data-location requirements. Regional storage mandates add constraints to how and where operators can build or scale sustainable infrastructure. So much so that, according to Gartner, more than 75% of businesses outside the US will have a digital sovereignty strategy in place by 2030.

In response, initiatives such as the European Cloud Campus illustrate the industry’s push to create sovereign environments that support AI and cloud workloads while keeping data within regulated regions. Projects such as this will help data centre operators develop AI and cloud applications that work on data stored and processed within Europe, strengthening the European Commission’s commitment to creating a robust, sovereign European cloud.

Clearly, there are no overnight solutions to these various important challenges. Significant progress is being made, but the industry must continue to prioritise its shared sustainability values. To deliver on the industry’s potential and responsibilities alike, failure is not an option.

Terry Storrar

Terry Storrar is Managing Director of Leaseweb UK. With over 25 years of experience in the technology sector, Terry is a proven leader, business founder, and turnaround specialist who drives growth and innovation at Leaseweb, a global provider of cloud hosting solutions. Terry leads the UK operations and oversees the strategic direction, customer engagement, and team development of the organisation.

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Leading the charge in allergy diagnostics

Santhosh Nair — Wed, 19 Nov 2025 12:22:24 +0000

As the seasons change, bringing warm days and the promise of outdoor adventures, millions of Europeans brace themselves for the onset of allergy symptoms¹. For many, these symptoms are more than just a minor inconvenience; they significantly impact daily life. Yet, without an accurate diagnosis, countless individuals continue to suffer unnecessarily.

At Thermo Fisher Scientific, we’re working to make the world healthier, cleaner, and safer. Our cutting-edge in-vitro diagnostics empower those suffering from allergies to receive precise diagnoses, ensuring they get the right test at the right time.

The economic burden of allergies is staggering, with costs ranging from €55 to €151 billion annually across Europe². These costs encompass absenteeism, healthcare, and other related expenses. Symptoms can vary from mild annoyances, like a runny nose, to life-threatening anaphylaxis, forcing individuals to live in constant fear of their next reaction.

Accurate diagnosis is transformative. Over 50 years ago, researchers from Uppsala University discovered the immunoglobulin E (IgE) antibody, revolutionizing our understanding of allergic reactions. This breakthrough led to the development of specific IgE diagnostic tests, benefiting millions worldwide.

Our allergen sensitization tests can detect specific IgE antibodies to over 550 different allergens, helping clinicians pinpoint their patients’ specific triggers³
. These tests also help clinicians to evaluate the severity of allergies, informing personalized treatment plans. Fast, automated, and easy to perform, our tests provide precise results to identify sensitization to allergic triggers⁴, enabling better-informed decisions on treatment and management.

We cultivate hundreds of potential allergens on our farm in southern Sweden, harvesting pollen, dust mites, and other sensitizing substances. This unique approach contributes to our reputation as the gold standard in in-vitro allergy diagnostics.

Diagnosis is just the beginning. It is the key to managing, treating, and even preventing allergies. It empowers patients and clinicians to make informed decisions sooner, breaking the cycle of uncertainty.

Our immunodiagnostics business has consistently pushed the boundaries of innovation and excellence. Our commitment to advancing allergy diagnostics has not only driven our success but has also made a tangible difference in the lives of countless individuals. Our strategic vision and unwavering dedication inspire our teams to strive for excellence every day.

Let’s continue to lead the charge in allergy diagnostics, making a tangible difference in the lives of those affected.

Join us in our mission to transform lives. Connect with us on LinkedIn and be part of the journey towards a healthier, allergy-free future.

Santhosh Nair

Santhosh Nair is President, ImmunoDiagnostics Division at Thermo Fisher Scientific, guiding teams that create advanced tests for allergy and autoimmune conditions. With more than twenty years of global leadership experience across life sciences, diagnostics, medical devices and software, he has driven major product launches and complex business transformations. He is committed to developing high-performing teams and building impactful, mission-focused innovations.

1. Allergy UK. Statistics and figures. Available at: https://www.allergyuk.org/about-allergy/statistics-and-figures/. Last accessed: September 2025.︎

2. Zuberbier T, Lotvall J, Simoens S, Subramanian SV, Church MK. Economic burden of inadequate management of allergic diseases in the € European Union: a GA2 LEN review. Allergy 2014; 69: 1275–1279.︎

3. Thermo Fisher Scientific. Whole allergen. ImmunoCAP Specific IgE single allergens. Available at: https://www.thermofisher.com/phadia/gb/en/our-solutions/immunocap-allergy-solutions/specific-ige-single-allergens/whole-allergen.html. Last accessed: September 2025.︎

4. Casas, M., Esteban, Á., González-Muñoz, M., Labrador-Horrillo, M., Pascal, M. and Teniente-Serra, A. (2020) VALIDA project: Validation of allergy in vitro diagnostics assays (Tools and recommendations for the assessment of in vitro tests in the diagnosis of allergy). Advances in Laboratory Medicine / Avances en Medicina de Laboratorio, Vol. 1 (Issue 4), pp. 20200051. https://doi.org/10.1515/almed-2020-0051 .︎

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Rebuilding trust in the NHS: How AI can help restore confidence in care

Ric Thompson — Thu, 16 Oct 2025 10:04:18 +0000

When people think about the NHS, they think about trust. For 75 years, it has been one of the most respected institutions in British life — a symbol of care, compassion and public good. But today, that trust is under strain. Rising demand, long waiting lists, exhausted staff and constrained budgets mean that patients are finding it harder to access the care they need, when they need it.

This is not just a crisis of resourcing, but a crisis of confidence. Recent surveys show that public satisfaction with the NHS has fallen to its lowest level on record, with just 21% satisfied. And for frontline clinicians, the pressure is unrelenting: general practice alone sees over 300 million patient consultations each year, with many GPs reporting that demand has become unsustainable.

Yet, amidst these challenges, there are reasons for optimism. Technology, used responsibly and in partnership with clinicians, is beginning to show how it can relieve pressure, restore confidence, and deliver tangible improvements for both patients and staff. After years of fragmented digital systems and pilot fatigue, the foundations are finally in place – interoperable platforms, clinician-led design, and evidence-based AI that supports rather than replaces human judgement.

Technology as a force for social good

The conversation around digital health often focuses on systems and efficiency. But what we are really talking about is social impact. When a working parent is able to book an appointment online and avoid missing a day’s wages, that is social impact. And when a doctor feels confident that digital tools are keeping patients safe while managing demand, that too is social impact.

Technology is not a silver bullet — it cannot train new doctors or rebuild hospitals — but it can free up precious time, reduce administrative burdens, and help clinicians focus on what matters most: patient care.

AI in action: Managing demand safely

Artificial Intelligence (AI) is often portrayed as futuristic, but in health care, it is already here — not in the form of robots replacing doctors, but in the form of intelligent tools that assist with safe decision-making and workflow.

One area where AI is making a real difference is in primary care triage. From October 2025, NHS England requires all GP practices to keep their online consultation systems open during core hours. While designed to improve access, many practices worry this will overwhelm staff with late-day requests and increase the risk of missed urgent cases.

The NHS does not need another grand vision statement. What it needs are practical, proven tools that make life better for patients and staff right now.

AI-enabled platforms are already proving that they can help. Take PATCHS, developed by NHS clinicians and used in hundreds of practices across England. By using natural language processing and machine learning, the system can read and categorise patient requests, flag urgent cases, and route routine requests into safe workflows. Early NHS-backed evaluations suggest such tools can cut waiting times by up to 70%, while also saving GPs hours of paperwork each week.

The key here is not that AI replaces clinical judgement, but that it enhances it — giving doctors and practice staff more confidence, and patients faster, safer access to care. The same principles are now being explored across urgent care and outpatient settings, showing how AI can strengthen – not strain – the clinician-patient relationship.

Co-design with clinicians

For digital tools to succeed in the NHS, they must be co-designed with the people who use them every day. Too many past initiatives have failed because technology was imposed from the outside, without regard for clinical workflow.

The best solutions today are those built hand-in-hand with GPs, nurses, and patients themselves. To use the same example again, PATCHS was created by practicing doctors in Manchester who understand the realities of general practice. Its success stems from that grounding: the AI was trained not just on generic data, but on real NHS patient journeys, and it continues to evolve through clinician feedback.

This principle of co-design should be non-negotiable. If we want technology to deliver social good, it cannot be developed in isolation. It must be rooted in the lived experience of those delivering and receiving care.

Rebuilding trust through transparency

AI in health care raises legitimate questions: How accurate is it? How safe? Who is accountable if it makes a mistake? These are not trivial concerns.

That is why transparency and evidence are critical. Randomised controlled trials, peer-reviewed studies, and ongoing monitoring are essential to build confidence among clinicians and the public. When a system is proven to be safe, when its accuracy is clear, and when its limits are openly acknowledged, then trust can begin to be rebuilt.

Technology companies must embrace this responsibility. The NHS cannot deliver its long-term plan without innovation — but innovation must be backed by evidence, ethics and openness.

A timely moment

We stand at a pivotal moment. On one hand, the NHS faces some of the greatest challenges in its history. On the other, digital tools are more powerful and more available than ever before.

The question is not whether technology should play a role, but how we ensure that role is positive, responsible, and aligned with public benefit. Used well, AI can help GPs see more patients safely, reduce waiting times, and alleviate the strain on overstretched teams. Used poorly, it risks eroding trust further.

The opportunity is to choose the former path — to show how technology can be a force for good in health, just as it has been in countless other areas of society.

Conclusion

The NHS does not need another grand vision statement. What it needs are practical, proven tools that make life better for patients and staff right now. AI-enabled solutions are already delivering exactly that.

The role of technology partners is to step up, not as vendors, but as collaborators — working with clinicians to co-design safe systems, with policymakers to scale responsibly, and with patients to rebuild trust.

If we get this right, technology will not just help the NHS recover – it will help renew its founding promise: care that is universal, trusted, and for the public good.

Ric Thompson

Ric Thompson is Senior Vice President for Health & Care at OneAdvanced, where he drives strategy, product innovation, and growth across one of the UK’s largest health technology organisations. With over 25 years’ experience in healthcare and technology, Ric has been a key leader in NHS digital transformation, from pioneering clinical document management solutions with Docman to developing integrated care platforms and AI-driven innovations aligned with the NHS 10-year plan.

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The digital heart revolution from Grey’s Anatomy to reality

Dr Mark Palmer — Thu, 25 Sep 2025 09:07:16 +0000

When Grey’s Anatomy first showed a holographic heart hovering over an operating table, it felt like science fiction. Today, it is rapidly becoming the future of cardiology. Cardiovascular disease remains the leading cause of death globally, accounting for nearly a third of all fatalities. Yet the way we study, diagnose, and treat the heart is being transformed by in silico medicine – advanced computer simulations that allow us to recreate one of the body’s most vital and complex organs.

The human heart is uniquely challenging to study. How do you safely investigate an organ that beats around 100,000 times a day, over 3 billion times in a lifetime? Traditional approaches rely heavily on animal models and human clinical trials – both essential, but limited in scope, costly, and sometimes ethically fraught. In contrast, virtual heart models allow researchers and clinicians to observe the unobservable, test the untestable, and experiment without endangering a single patient.

Beyond anatomy - digital twins of the human heart

Every heart is unique – from its geometry and rhythm to the way blood flows through its chambers to the complex systemic interactions that modulate its behaviour. This individuality means that personalised cardiac disease management, also called precision cardiology, may significantly improve the efficacy of treatment of cardiovascular disease. The promise of in silico medicine lies in creating a digital twin, a personalised, virtual copy of a patient’s heart that mirrors their physiology in astonishing detail.

These digital hearts can be treated, stressed, repaired, or pushed to failure in ways no physician could ever attempt on a living patient. The insights gained enable doctors to predict how a heart will respond to specific interventions – whether a new drug, a surgical procedure, or the implantation of a device –before the treatment is attempted in the clinical theatre.

From television fiction to clinical reality, the digital heart represents one of the most significant frontiers in modern medicine.

The ability to run simulations of a patient specific digital twin can help clinicians make better informed decisions, improve preoperative planning, and enhance outcomes. This is particularly valuable when treating complex conditions such as congenital heart disease or heart failure, where patient variability presents challenges for conventional approaches.

Reducing the time frame for patient specific heart modelling

Recent advances in AI and simulation technologies are dramatically accelerating the process of creating digital twins of the heart. What once took weeks of painstaking modelling can now be achieved much quicker. These tools can capture detailed geometric changes of the heart during a heartbeat. When combined with measurements of the electrical activation, they provide critical insights into heart functions, from electrical activation to tissue contraction and blood flow, enabling researchers and clinicians to generate patient specific heart models with unprecedented speed and accuracy.

For example, here at Ansys we have developed the PyAnsys Heart platform, which combines AI with advanced simulation to help streamline the modelling process. By reducing timelines from weeks to minutes, PyAnsys Heart illustrates how digital twins are already moving from concept to reality, offering a glimpse of what the future of personalised treatment planning could look like.

Do you need a PhD in computational mechanics to run a heart model?

As with most technological breakthroughs, adoption can be slowed if tools are too complex for everyday use. But advances in standardisation and AI driven automation are making sophisticated heart modelling far more accessible. Returning to the PyAnsys Heart example, the platform uses AI to simplify and automate the modelling process, turning what was once a highly specialised task into something that can generate accurate and detailed visuals in a matter of minutes. This allows cardiologists, cardiac surgeons, electrophysiologists and their clinical support staff to apply their expertise and derive insights from the simulation process without needing advanced simulation training.

A new era for regulation and clinical trials

One of the most profound changes in recent years has been the growing acceptance of simulation as regulatory evidence. Both the US Food and Drug Administration (FDA) and the European Medical Device Regulation (MDR) now recognise in silico modelling as part of submissions for new cardiovascular therapies and devices.

This marks a significant turning point. By validating simulation as a credible source of evidence, regulators are enabling companies and researchers to reduce reliance on large, lengthy, and sometimes risky human trials. In addition, in silico clinical trials can combine data from bench testing, pre-clinical, and clinical studies and run virtually, across thousands of digital patients, to test safety, efficacy, and performance.

These trials offer several advantages. They are faster, more cost effective, and inclusive of patient diversity in ways traditional trials struggle to achieve. For example, digital populations can be designed to reflect rare conditions or demographic groups that are often underrepresented in real world studies.

Engineering the beating heart

The complexity of modelling the heart is immense. It requires simulating not only the intricate geometry of valves, arteries, and chambers, but also the physics of blood flow, the biomechanics of tissue, and the electrochemistry of cardiac rhythm.

The heart is not just a pump. It is an electromechanical system, where signal conduction and mechanics are intimately connected. Capturing this requires advanced, coupled, multiphysics modelling – ideally one tool capable of representing fluid dynamics, structural mechanics, and electrophysiology simultaneously.

This approach is already driving innovation in devices such as artificial hearts, pacemakers, and stents. For example, a total artificial heart must mimic the natural heart’s performance under a wide range of physiological conditions, from resting to intense exercise. Virtual testing allows engineers to simulate these scenarios comprehensively, ensuring safety and performance before a device is implanted in a patient.

From prevention to personalisation

Looking forward, digital twins of the heart will revolutionise not only treatment but also prevention. Imagine a patient at risk of heart disease being given a digital model of their heart, updated regularly with data from wearable sensors or imaging scans. Their physician could monitor changes in real time, testing lifestyle adjustments or treatments on the virtual heart before recommending them. Patients could also understand the consequences of their decisions and how to mitigate them in their daily lives.

Such personalised, proactive care would move us away from reactive medicine – intervening only once disease has progressed – and towards continuous, preventative management of cardiovascular health.

The ethical frontier

With such transformative potential come important ethical considerations. Patient data privacy, equitable access to advanced digital tools, and ensuring models are free from bias will all be critical as we move towards widespread adoption. Regulators, clinicians, engineers, and ethicists will need to collaborate to ensure that the benefits of in silico medicine are shared fairly and responsibly.

The future of cardiology

From television fiction to clinical reality, the digital heart represents one of the most significant frontiers in modern medicine. In silico modelling is no longer confined to research labs – it is being recognised by regulators, trusted by clinicians, and increasingly used to improve outcomes for patients worldwide.

On this World Heart Day, it is worth reflecting that while cardiovascular disease remains the planet’s biggest killer, we are now armed with tools that could change the trajectory of heart health for generations to come. The holographic heart is no longer a fantasy – it is the digital twin beating on the horizon of cardiology.

Dr. Mark Palmer

Dr. Mark Palmer is Lead Chief Technologist for Healthcare, Ansys Technical Fellow, Ansys, now part of Synopsys. A Distinguished Scientist, Dr. Palmer works across the core business units, customer excellence, sales, marketing, and research and development to advance technologies of strategic importance for the healthcare sector. Dr. Palmer is a recognised expert in the field with more than 30 keynote or invited lectures and over 40 journal publications and refereed conference papers.

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In pursuit of greener streams, sustainable ad tech is reshaping media as we know it

Frank Maguire — Thu, 18 Sep 2025 09:47:27 +0000

Every digital interaction has a footprint. When someone scrolls through a newsfeed, streams their favourite series, or buys a hat they saw in an ad, an invisible chain of servers, exchanges, and devices powers the experience. Each step consumes energy and collectively contributes to carbon emissions, accelerating the climate crisis. The advertising industry is in fact one of the biggest silent contributors, accounting for 2.5 per cent of global CO₂ emissions. The supply chain is complex, energy-intensive, and somewhat hidden from the public consciousness.

Zero Waste Week was another opportunity to reflect on the importance of reducing waste in all forms, especially digital. In advertising, waste manifests as irrelevant impressions, opaque supply chains, and inefficient programmatic flows. These are not just operational issues; they are carbon issues. If we are serious about sustainability, we must confront them head-on.

The advertising industry has increasingly been taking steps to tackle these hidden costs with trailblazing media solutions and initiatives. The Global Media Sustainability Framework is a notable example of how the industry is now geared towards ensuring consistency and transparency across the ecosystem. Encouraging businesses to decarbonise their operations and accurately promote sustainable products, services, and behaviours.

In recent years we’ve seen progress across the supply chain. Scope3 is earning recognition for helping the advertising industry measure carbon emissions. DSPs like StackAdapt, Yahoo and FreeWheel have implemented solutions to tackle bid request duplication, one of digital advertising’s biggest sustainability challenges. Tools like our Equativ GreenPMPs allow advertisers to reduce emissions from their campaigns by removing high-emitting sites , turning transparency into actionable decision-making. By showing precisely where emissions occur across campaigns and connecting investments to carbon removal projects, advertisers have the insight they need to act responsibly. Sustainability, in this sense, is not a trade-off with performance but rather enhances efficiency and strengthens trust.

Beyond emissions, sustainability in advertising requires us all to embrace accountability. The industry has long wrestled with reputational challenges, from promoting high-carbon activities to inadvertently funding low-quality or misleading content. As recent discussions around greenwashing and “green-hushing” show, transparency is critical. If advertisers hide their environmental impact—or exaggerate it—they undermine credibility. Open reporting, traceable supply chains, and principled media buying are not just ethical imperatives; they are essential to restoring public trust and aligning the industry with global climate goals.

By combining transparency, innovation, and principled decision-making, we can transform an industry often seen as part of the problem into one of the first to reach net-zero

Innovation extends into streaming and connected TV as well. Server-side ad insertion, optimised infrastructure, and smarter delivery pipelines can drastically reduce the carbon footprint of data-intensive media. When Horizon Media mandated that all their campaigns shift to GreenPMPs, they cut campaign emissions by over 50% while improving performance. These results prove that eco-efficient advertising is indeed possible and proving to be commercially viable.

When assessing the current state of affairs, I’ve seen momentum gathering across the industry, as many brands, agencies and tech providers are applying carbon-conscious strategies to routine advertising practices. Instances of collaboration between stakeholders in the advertising supply chain to advance sustainability efforts worldwide are also more commonplace. There’s definitely awareness of a shared responsibility to reduce carbon emissions and realise greener supply chains.

However, there’s still a lot of ground to be made.

Last year, António Guterres delivered a strong critique of the media’s role in the climate crisis, chastising fossil fuel advertisers for operating as ‘enablers of planetary destruction. To receive that message in a constructive manner, sustainable media must be embedded into the DNA of advertising, not merely treated as an optional add-on.

This is about more than tools and technology; there’s also a human element and personal enthusiasm. My concern for the environment began as a child in the 1980s. My sisters and I monitored our family’s recycling and water use, instilling habits that shaped the rest of my life. That early awareness evolved into a professional mission: if digital advertising has an outsized impact on emissions, we have a duty to act, innovate, and share solutions across the ecosystem. This commitment has inspired initiatives like the upcoming Green Media Summitand continues to guide our efforts toward making every impression as responsible as it is effective.

Across the pond and worldwide, recent grassroots awareness events like Zero Waste Week and high-level platforms like the upcoming United Nations Climate Summit, equally represent opportunities for people to address their environmental impact. Their purpose has always been bigger than the timeframe we busily cram them into. More than a tribute, these are timely wake-up calls challenging us to measure impact and embrace accountability.

By combining transparency, innovation, and principled decision-making, we can transform an industry often seen as part of the problem into one of the first to reach net-zero. The opportunity is here, the tools are developing, and the path is becoming clearer. What is needed now and always is the collective will to realise it.

Frank Maguire

Frank Maguire is SVP Product Marketing & Sustainability at Equativ. With a career at the intersection of digital advertising and sustainability, Frank has spearheaded research into how people engage with advertising, helping brands and agencies adapt to shifting media behaviours. A passionate advocate for sustainable innovation, he has completed a Sustainability certificate at Harvard Business School and has been instrumental in pioneering industry-first initiatives, including the launch of GreenPMPs and the Green Media Summit.

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Campus rewired: How smarter networks are powering higher learning

Tom Whittle — Thu, 18 Sep 2025 08:34:41 +0000

Reliable, secure networking is the backbone of digital transformation across higher education. The education sector now ranks fifth globally for cybercrime incidents, underlining why strong cybersecurity is no longer optional; it’s essential. Meanwhile, over 65% of Gen Z frequently use more than one device at a time, placing greater demand on campus infrastructure than ever before.

Modern universities face mounting pressure to attract top academic talent, standout students, and world-class research teams. A secure, scalable network not only enables this but also actively supports long-term growth and reputation.

To get it right, critical components like wireless connectivity, automated device management, and robust cybersecurity must work together, not in silos. Seamless integration into existing IT systems is equally vital, especially when so much sensitive research and data is at stake. Whether it’s students, staff, or researchers logging in, the experience must be frictionless and protected from end to end.

Designing networks that deliver

As universities increasingly rely on digital platforms for research, learning, and administration, developing a secure and high-performance networking infrastructure has become essential to support their growing demands and safeguard critical data.

Higher education leaders increasingly see resilient, high-speed networks as the essential foundation for digital transformation strategies that improve teaching, streamline operations, and strengthen research collaboration.

By creating a bespoke networking infrastructure that fits perfectly within the university ecosystem, IT managers can ensure that they can continue innovating and creating an up-to-date, advanced networking system to attract new talent to their university.

How AI and automation are rethinking campus connectivity

AI and automation can transform higher education networks, driving smarter resource management, bolstering security, and enabling seamless connectivity to meet the evolving demands of modern campuses.

The growing use of IoT devices, such as sensors, printers, and other connected devices rely on Wi-Fi capabilities, the performance of which can be supported by AI-driven analytics to not only identify efficiency and performance issues but also to manage security risks. This is where AI and automation can influence a university’s networking capability. By leveraging AI-driven analytics and automated processes, institutions can optimise network performance, predict, and prevent potential issues, all leading to an enhanced user experience.

For instance, Oxford University recently introduced a new Juniper Mist Wi-Fi network with AI and automation capabilities. This new network benefits from analysing data from all Mist solutions to deliver personalised monitoring, diagnostic reports, and configuration recommendations, ensuring optimised network performance and bandwidth strength at all access points across the campus.

A fully developed networking system delivers value across the entire campus. It ensures students and staff remain connected at every stage of their academic journey, while enabling more personalised learning experiences and greater student independence.

Alongside recognising performance and efficiency issues, the use of automation and AI operations-focused toolsets can help support and strengthen the security of the networking infrastructure. This enhanced intelligence allows the management of which users can access the network, from which device and what services they can use, helping minimise security threats to the system. An increased visibility allows IT teams to create additional security context, keeping both staff and student data private.

Connecting and empowering the campus of tomorrow

For university alumni, reliable and fast network connectivity is essential. Enabling seamless access to online resources, collaborative tools, and learning experiences is paramount in fostering academic success and engagement.

In today’s digital world, users are extremely demanding; students and staff do not want to have different logins for different locations, and they expect a centralised networking framework. Empowering students with self-service, private wireless LAN connectivity allows individuals to have private connectivity instantly, offering more automation and ownership over their education.

For students and lecturers, having a centralised network means easy access to the library, course and research materials, which creates a better university experience. But for university admin teams, their expectations are slightly different, so IT teams must be equipped to cater for all individuals within the university network.

Establishing a high-performance network equipped with AI and automated capabilities eases the workload on administration staff. IT teams no longer need to focus on time-consuming data collection, unnecessary troubleshooting, and other repetitive tasks. IT teams can equip the network to detect issues, collect data and recommend solutions, either pre-emptively or immediately once an issue arises.

This added foresight allows the IT staff to focus more on university innovation and overall alignment with business strategy.

The right network doesn’t just connect people, it drives a stronger, smarter, more connected campus community.

Future-proofing higher education through smarter IT networks

Like any emerging technology, investing in high-performance networks requires resources. However, universities that prioritise advanced infrastructure gain far more in return, from accelerating innovation to opening fresh opportunities and improving staff retention.

A fully developed networking system delivers value across the entire campus. It ensures students and staff remain connected at every stage of their academic journey, while enabling more personalised learning experiences and greater student independence. It also underpins hybrid learning models, making them smoother and more effective. For lecturers and researchers, a strong, secure network provides the foundation for groundbreaking work, offering easy collaboration with the wider research community and safeguarding sensitive data every step of the way.

Tom Whittle

Tom Whittle is Solutions architect – Networking at Telent. With a broad range of experience across industry verticals and technologies, Tom designs transformation solutions across: LAN, WLAN, WAN, DC, Security and Systems stacks with a focus on leveraging technology and services to empower customer business. Recent projects Tom has supported include: University of Bristol Modern Network Transformation and University of Oxford next generation AI-Driven Wireless LAN.

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Putting ethical AI at the heart of public service

Tony Holmes — Fri, 12 Sep 2025 14:53:18 +0000

The UK public sector is integrating AI tools to turbocharge its stalling productivity. Government departments are trialling the AI assistant Humphrey, expanding digital skills training for civil servants and long-term strategies like the NHS 10-Year Plan put AI and technology at the heart of transformation. Yet while adoption is moving fast, regulation is lagging.

The UK has yet to create its own AI regulations, but with controversy around data use, copyright, and creators’ rights, a UK AI Act is expected within the year. The EU’s AI Actand Code of Practice offer a glimpse of what regulation could look like: a framework that prioritises accountability and transparency.

Regulation is a human capability challenge. Without the skills and culture to apply regulation, even the most carefully written laws will fail. As the UK moves towards enhancing digital skills in the public sector and developing its own AI regulation, it needs to ensure that ethics are engrained in the public sector’s efforts to integrate AI responsibly.

Why the public sector must lead on AI ethics

As the provider of public services that have a direct impact on citizens’ lives, often in high-stakes areas like healthcare, policing, and social benefits, it is essential that the public sector leads in ethical AI practices and eventually, regulatory compliance. Unlike private companies, mistakes or biases in public sector AI can erode trust in government, create inequities or even cause harm to vulnerable populations.

Without ethical AI literacy among civil servants, AI risks amplifying bias. In healthcare, AI trained on skewed datasets could misdiagnose patients from minority backgrounds. In policing, predictive algorithms can disproportionately target certain communities. And in welfare, automated decision-making can deny support to eligible claimants.

Taking inspiration from the EU’s regulation

The EU AI Act is the world’s most comprehensive legal framework on AI, including strict obligations for companies that are providing AI systems within the EU. The accompanying Code of Practice is a guiding document, a set of non-legally binding guidelines designed to help companies demonstrate compliance in areas like transparency, copyright and safety.

The development of this Code was contentious with tech companies who warned it would stifle innovation. Meta refused to sign the code with their Chief Global Affairs Officer, Joel Kaplan saying the Code’s ‘over-reach will throttle the development and deployment of frontier AI models in Europe’. Similarly, while Google signed the Code, it warned it went too far.

Despite the controversy, the Code is likely to shape global standards. The UK, which has yet to set out its own AI regulations, could adopt many of its principles to maintain public trust while fostering innovation. The EU’s approach demonstrates that governments can convene industry around common standards that put responsible use at the centre.

The public sector should aim to be a model user of AI: transparent in its processes, accountable in its decisions and proactive in identifying risks

On the other hand, the US’ recent AI Action Plan sets out a starkly different approach to regulation. It calls for cutting the red tape, makes no mention of AI and copyright law and prioritises innovation and speed over responsibility.

The UK should avoid both extremes. Over-regulation risks stagnation and wasted opportunity, especially in a public sector already struggling with productivity. Under-regulation risks bias, eroded trust and ethical failures that could set back adoption for years.

The opportunity lies in a middle ground: regulation that enables innovation while embedding trust, ethics, and transparency from the outset.

AI ethical literacy matters

In lieu of formal regulations, the UK can start building the right culture by embedding ethics into every digital upskilling programme. Civil servants shouldn’t just learn how AI works, but also when to use it, how to govern it and why responsibility matters. This will prepare the public sector to comply when regulation does arrive because even the best frameworks will fail if staff lack the skills to implement them.

The government has recognised the urgency of digital skills and introduced an upskilling plan for 7,000 Senior Civil Servants and launching the NHS Digital Academyto educate NHS staff on basic digital and data competence.

However, progress is uneven with only 21% of Senior Civil Servants feeling confident in digital and data essentials. The public sector also relies on outside contractors for digital skills, with 55% of digital and data spending in 2023 going to external providers, creating barriers to institutional knowledge.

As AI becomes embedded in essential services, we need a workforce capable of spotting AI errors or ethical risks. Without that capability, we could embed discrimination and erode trust at the very moment AI adoption accelerates. Integrating ethical literacy now will allow the public sector to adapt quickly when regulation arrives, rather than rush to retrofit new behaviours later.

The way forward

AI regulation in the UK must be paired with investment in public sector capability, in both technical proficiency and ethical literacy. The public sector should aim to be a model user of AI: transparent in its processes, accountable in its decisions and proactive in identifying risks.

The UK has an opportunity to lead globally by showing that AI can be adopted quickly and responsibly by setting a standard for trust-based innovation that other nations follow. But to seize it, ethics and capability must be treated as core infrastructure, not optional extras.

If the UK can combine the EU’s accountability-first approach with serious skills development, it could lead the world in public sector AI adoption, not only in speed, but in trust.

Tony Holmes

Tony Holmes is Practice Lead for Solutions Architects in the Public Sector at Pluralsight. A seasoned technology professional, Tony’s journey in the tech world spans over two decades, during which he has worked with some of the world’s most forward-thinking technology organisations, including the BBC, Digital Equipment Corporation, Microsoft Research, Opsware, Hewlett Packard, and Oracle.

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Global health aid is broken: Can tech fix it before 2030?

Stanley Olisa — Thu, 04 Sep 2025 10:31:03 +0000

Half the world still live without access to essential health services. Thats the stark warning from the World Health Organisation. When illness strikes across Africa and Asia, families are forced into impossible choices: pay crippling out-of-pocket costs or go without treatment altogether. For too many, the result is fatal.

From pneumonia in Bangladesh to malaria in Kenya and maternal complications in Nigeria, the story is consistent. The poorest are locked out of healthcare systems designed to leave them behind. But a new tech-driven model is starting to change that.

A digital lifeline

Helpster, a global non-profit platform, is leveraging technology to close life-threatening healthcare gaps. Its model is simple but radical: connect vulnerable patients directly with donors, hospitals and volunteers in real time. The goal is to bypass the bottlenecks of traditional aid and get treatment to those who need it most, when they need it most.

The platforms data from Africa and Asia reveal just how urgent the crisis is, and how tech innovation can be part of the solution.

The conditions claiming the most lives

Pneumonia remains one of the leading killers of children under five in Bangladesh, accounting for 12% of child deaths. In rural Kenya, severe malaria dominates case records, with more than 3.4 million new cases and 11,800 deaths each year. One seven-year-old girl endured three days of fever before Helpster matched her family with a donor. The $109 treatment cost was impossible to cover. Without digital intervention, she would not have survived.

In Nigeria, the challenge is even larger. With 68 million malaria cases and 194,000 deaths recorded in 2021 alone, the country accounts for nearly a third of global malaria deaths. Alongside this, obstetric emergencies such as pre-eclampsia and placenta praevia regularly appear in Helpsters case logs.

Healthcare at catastrophic cost

Behind these statistics are families who simply cannot pay.

In Bangladesh, rural households earn around $155–175 per month. Treating pneumonia costs $170. A breech delivery can cost $265.

In Kenya, severe malaria treatment averages $109, while pneumonia is $133. Just 28% of Kenyans have health insurance, and even then many say it is unaffordable.

In Nigeria, treatment for malaria or surgical conditions ranges from ₦20,000 ($25) to ₦180,000 ($230). Yet fewer than 10% of Nigerians have insurance, leaving 190 million to pay out of pocket.

When half of Nigerians earn just ₦63,126 ($41) a month, according to 2025 data, these costs are catastrophic.

Barriers that keep care out of reach

For families in Bangladesh, Kenya and Nigeria, the obstacles to healthcare are strikingly similar. The first is economic. With households bearing most of the cost, even a basic hospital stay can wipe out a months income.

Distance is another barrier. In rural areas, hospitals may be hours away, reached only by dirt roads or unreliable transport. For a mother in labour or a child with a high fever, those delays can be fatal.

The shortage of medical staff deepens the crisis. Doctors and nurses cluster in urban centres, while rural facilities operate with skeletal teams. Many who are trained leave for better prospects abroad, draining already fragile systems.

Social and cultural factors also hold people back. Women often need permission to seek care, while marginalised groups are treated with suspicion or neglect. And when families finally reach hospitals, corruption and empty drug shelves too often greet them instead of treatment.

Digital health services are expanding but the poorest remain excluded, a cruel irony of progress.

Scoring poverty, prioritising urgency

This is where Helpsters model shows its strength. By applying digital poverty and urgency scores, the platform can prioritise cases based on household income, family size, location and living conditions.

In Bangladesh, the average poverty score is 118/250 with an urgency score of 22/26. In Kenya, the average is 158/250 with the same urgency score. In Nigeria, the average is 147/250 with an urgency score of 18/26. A score above 180 signals extreme poverty. Scores above 22 flag life-threatening emergencies. Most Helpster-supported families fall dangerously close to both.

This data-driven system ensures the most urgent and vulnerable patients are matched with donors first.

A scalable global model

The wider backdrop is bleak. Low- and middle-income countries face a $176 billion healthcare financing gap by 2030. Every $1 invested in maternal and child health yields up to $10 in returns, but global health funding fell 21% between 2024 and 2025. WHO itself faces a $1.9 billion shortfall.

This is why innovation matters. By cutting through bureaucracy, Helpster is demonstrating how technology can stretch limited resources further. Its model of real-time case matching is already saving lives but its real potential lies in scale.

As climate change, displacement and poverty converge, the need for digital-first, community-powered healthcare platforms has never been greater.

Helpster shows that when technology is combined with compassion, inequality doesnt have to be inevitable. The challenge now is speed: how quickly can this model reach every child, every mother, every family still locked out of care?

Join the movement here and help rewrite the future of healthcare access.

Stanley Olisa is the PR Manager for Helpster Charity, a global tech-driven nonprofit easing healthcare access for vulnerable populations across Africa and Asia.

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Women’s Equality Day: Encouraging Empowerment

Richard Durrant — Tue, 26 Aug 2025 10:22:48 +0000

Despite being celebrated for over 100 years, Women’s Equality Day remains as relevant as ever. The day is rooted in the celebration of when the 19th Amendment was added to the US Constitution in 1920, officially granting American women the right to vote. Globally, it is now an opportunity to recognise and honour women’s suffrage, as well as inspiring a call to action for accelerating women’s equality.

Progress since 1920 is undeniable, yet the work is far from done. In the 2024 Fortune 500, only 9% of CEO roles were held by women, showing how under-representation persists even at the very top. Furthermore, the gender pay gap in the UK has narrowed but remains stubborn, with research suggesting it may take up to 40 years to close.

And the story is no different for women in tech, with many still striving for equality. It’s important to take the time to hear from these women, and learn from their experiences of making it in the industry.

Culture of empowerment

The workplace culture at an organisation can make all the difference to how women experience work, so it’s undeniably crucial to ensure this culture is one of positivity and empowerment. Becky Wallace, Head of People at LearnUpon, recognises this: “As leaders, we have the privilege of creating a culture where everyone belongs. When we talk openly about biases and give our teams the tools to challenge and tackle them, we foster an inclusive learning environment where everyone has the opportunity to grow and thrive.”

“At LearnUpon, learning is at the heart of everything we do, and we’re proud of the inclusive culture we have created,” adds Wallace. “We know that we are more successful as a company when everyone brings their unique perspectives so these conversations not only educate, they help create an open space where every voice is heard, valued, and respected. Women’s Equality Day is a reminder of just how much a difference women make in the workforce and how important it is to keep promoting gender equality through education and empowerment.”

Jessica Shorten Hood, Product Manager C&I at Epson UK, echoes this sentiment: “Just as important as the solutions we deliver is the culture that drives them. When women are empowered to lead, innovate, and contribute at every level, we create not only stronger teams but better outcomes for our customers. Being a woman in tech comes with challenges, but it also presents an opportunity to help redefine leadership and open doors for others. Representation matters. Not just for equity, but because it leads to better ideas, better solutions, and better results.”

It’s argued by Louise Clements, Global Chief Marketing Officer at WorkJam, that if employers want to truly champion gender equality, they must have “open cultures where different approaches and perspectives are embraced.” Clements also emphasises how networking with other women in other teams or departments creates “wonderful, supportive working environments”. She adds: “Women’s Equality Day is a powerful and meaningful reminder of the significant progress women have made – and yet, how far we still have to go. To me, it’s both a celebration of achievements and a call to action: to amplify female voices, embrace inclusivity, and continue advancing equality.”

Encouraging diversity

Discussions around equality often also come hand-in-hand with diversity, with both being just as important to foster success. “Organisations should continue to hire from diverse talent pools”, emphasises Charis Thomas, Chief Product Officer at Aqilla. “We know that greater diversity leads to increased revenue, fosters new thinking, and drives innovation. Change is happening, driven by the increasing need for diverse roles in Tech and Finance.

Thomas continues: “I’ve worked in Tech/Finance for just over two decades, often in environments where accountants and developers dominate – making it largely male-dominated. Initially, being in the minority can feel intimidating, but focusing on delivering quality work helps create commonality. Like anyone, you eventually find your tribe.”

Lisa Healey, Sales Director at Node4, agrees that fostering an inclusive and supportive environment for women is something that companies should strive for: “Diversity in the workplace is essential, as it brings a wide range of perspectives, opinions, and approaches to problem-solving. Women should feel empowered to engage fully, share their ideas on projects, and contribute meaningfully to their organisations.

However, despite being encouraged by the positive progression that has been made in the technology sector, Healey still voices some concern: “We are moving in the right direction but the reality is that there is still more we can do. Many women still encounter obstacles and challenges due to their gender and equally have their own limiting beliefs, whether this be through an experience or that way in which businesses encourage them. I always advise young women starting a career in tech to seek out strong mentors who can support their growth and progression. Having someone who can guide you through the complexities of your career, advocate for you and provide expert insight is invaluable.”

Acting assertively

Along with empowerment, diversity, and mentorship, another piece of advice offered by Rebekah Lawrence, Senior Technical Writer at StorMagic, is for women to act assertively in their roles: “My main message to women in the workplace would be: don’t be afraid to assert yourself. Call out sexist language or behaviour, while understanding that you need to educate the speaker as often it isn’t a conscious attitude. And actively connect with other people who inspire you – whatever their gender is – and learn from them.”

Lawrence also explains how seeing more women in positions of power can make a difference: “As a woman born in the 1960s I was privy to a time where girls were excluded from so many things. But now, nearly sixty years later, I find myself working for a company with a female CEO. And while some of the current political and social discourse is disturbing, her leadership gives me hope for the future, particularly because she embraces her feminine side in her role.”

By listening to the women who are working across the tech industry, we can use past experiences to accelerate achieving gender equality in the future. There is no better advice than that from those who have seen first-hand what it takes to work in such a historically male-dominated industry, and hopefully this can be used to encourage and empower the next generation of female talent in technology.

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Teaching the future: How AI can empower every student

Justin Louder — Thu, 21 Aug 2025 09:42:20 +0000

AI is reshaping the world of work, and today’s students are preparing to lead in that transformation. In higher education, AI is no longer just a tool for learning. It is part of the process, and the skillset students need to thrive. The challenge for educators is not to shield students from AI, but to equip them to use it wisely, reflect on it critically, and grow with it in an ethical manner.

Equitable learning: Reaching every student with AI

AI is unlocking new ways to reach every student. Adaptive tools personalise instruction, enabling students to learn at their own pace and receive support when needed. This shift requires more than just deploying new tools. Institutions must be intentional about how AI is introduced to enhance access rather than widening existing divides. By integrating AI into the platforms students already use, institutions can level the playing field—especially for those who may not have access to premium tools. This approach helps address inequities in both availability and meaningful participation. Premium AI models may not be available to every student, raising concerns about equity. Institutions can help bridge this gap by integrating AI into widely used and universally accessible campus platforms. But access alone isn’t enough. It’s critical to help AI support all learners equitably, regardless of their background or financial situation.

Institutions must also recognise the limits of access without guidance. Faculty and staff play a central role in making AI-powered learning effective. Support for instructors, especially those less comfortable with digital tools, can help make sure that AI benefits all learners, not just the tech-savvy.

Engaging students as active, ethical problem-solvers

AI is helping turn classrooms into spaces for innovation and exploration. Students can engage in interactive conversations with AI personas from history or science to gain deeper insights. They can dive into immersive simulations that explore real-world issues, such as climate change or social justice, in ways that prompt reflection and action. Gamified environments offer low-risk opportunities to practice skills, build resilience, and learn from mistakes.

These experiences encourage students to move beyond passive learning. Instead, they explore, test, and iterate, developing habits of curiosity and critical thinking. They also help students connect emotionally to the material, which can lead to deeper learning and greater motivation.

Today’s students are not just growing up with AI. They are preparing to lead with it. Higher education must help them develop the insight, discipline, and integrity needed to do so.

Ethical engagement means helping students understand how to use AI responsibly. They should be encouraged to examine the sources and limitations of AI-generated content, consider its implications, and think critically about its role in shaping knowledge and decisions. Educators can support this by designing assignments that promote thoughtful analysis, accountability, and a clear understanding of ethical standards in digital spaces.

Developing the skills to lead in an AI economy

AI literacy isn’t just about knowing how to use the tools—it’s about understanding their impact, the responsibility they carry, and how they shape our decisions. Students must develop the skills to evaluate AI’s outputs, recognise bias, and make ethical decisions.

AI is also helping students build the human skills that matter most in a tech-driven world. These include:

- Critical thinking: Evaluating information, challenging assumptions, and making sound judgments.
- Collaborative and cross-disciplinary thinking: Using AI to work with others on complex, real-world challenges.
- Creative innovation: Designing new solutions that serve communities and solve problems.
- Digital ethics: Understanding issues like data privacy, consent, and the broader societal implications of emerging technologies.

Prompt engineering is another skill gaining attention. It involves crafting the right questions to elicit meaningful and accurate responses from generative AI. Students who learn this are better positioned to harness AI as a tool for insight and creativity, not just task completion.

These technical abilities must be paired with what are often called human skills. Collaboration, creativity, ethical reasoning, and adaptability remain essential in any profession. AI can support these by helping students iterate faster, test ideas in new ways, and reflect more deeply on the outcomes.

To support these outcomes, assessments need to evolve. Standardised tests and rote memorisation are no longer sufficient. Instead, authentic assessments—such as projects, presentations, and simulations—offer a more accurate picture of what students can actually do. These methods reward application over repetition and are more aligned with workplace expectations.

Educators are beginning to develop and apply these new assessment models in response to the growing influence of AI. Academic success in a digital world matters—but our real responsibility is preparing leaders who will shape a more just, inclusive, and thoughtful society, with technology as one tool among many.

Today’s students are not just growing up with AI. They are preparing to lead with it. Higher education must help them develop the insight, discipline, and integrity needed to do so.

The goal isn’t only academic success in a digital world. It is preparing future leaders to shape a more fair, inclusive, and sustainable society, powered by technology and guided by human values.

Justin Louder

Justin Louder is AVP for Academic Innovation at Anthology. An experienced Associate Provost and Ed Tech Leader with a demonstrated history of working in the higher education industry, Justin is skilled in educational technology, online learning, instructional technology, training, adult education, grant writing, OER, and K-12 education.

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Extinct creatures. Explosive ideas. Innovation needs creativity

Bas Korsten — Thu, 07 Aug 2025 10:16:49 +0000

When people think of creativity, they tend to picture art, music and often advertising. Science and technology isn’t an immediate association. Yet some of the most impactful scientific breakthroughs of our time are only making real-world progress because of the creative ideas that help them connect with the public.

Take the Mammoth Meatball, the first meatball made from the DNA of the long-extinct Woolly Mammoth. Some might dismiss this as a PR stunt. But a strategic PR stunt can have real world impact. The Mammoth Meatball was a carefully designed, creative provocation aimed at sparking a global conversation about the future of food. Created using resurrected mammoth DNA and lab-grown meat technology, the meatball wasn’t made to be eaten. It was made to be noticed. And it was – with media coverage in over 65 countries and an estimated reach of 2.3 billion people.

The campaign not only went viral; it shifted cultured meat from a niche biotech experiment into a matter of mainstream interest. It catalysed government engagement, unlocked fresh funding opportunities, and reignited debate around how we can feed the planet more sustainably. In the UK, it contributed to increased public understanding and political discussion of cultured meat as a viable alternative to traditional agriculture.

It’s a powerful example that technological progress alone is not enough. In fields like biotech, climate science, and synthetic food, real-world impact depends on cultural momentum and that’s what creativity unlocks. Breaking out of the lab and into the world takes more than innovation – it requires the ability to tell a compelling story, stir emotion, and translate complex science into something tangible, relatable, and inspiring.

From DNA to Demand: making the abstract real

Over the past few years, I’ve had the privilege of working on projects that sit at the intersection of creativity and scientific exploration. From mammoth meatballs to T-Rex leather, it’s not about gimmicks, it’s about bold, thought-provoking ideas. We’re demonstrating how storytelling can play a meaningful role in shaping public perception and policy around innovation.

We’ve actually been exploring how data, tech and creativity can work together for a while. Before the term Generative AI was part of the public psyche, in 2016, we looked at if and how AI could create original and meaningful art. We worked with data and computer scientists to create The Next Rembrandt – a new painting made entirely using data from Rembrandt’s original works. The idea wasn’t just to see if we could do it, but to open up his art and the way Rembrandt created to a new generation and show what AI can do in a way that people could relate to. It brought emerging technology into public conversation – not through theory, but through something people could actually see and feel: a 3D printed painting.

Because the truth is, many scientists are solving for tomorrow. But too often, they’re doing so in silos – disconnected from the audiences their work is meant to benefit. That’s where creativity comes in. It acts as a bridge: helping people understand, feel, and imagine futures they might otherwise ignore or resist.

Creativity doesn’t just sell culture, it can help accelerate science. It can make the unfamiliar familiar. It can help people imagine better futures and more importantly, want to build them.

We saw this again with the launch of T-Rex leather – a prototype material made with information from resurrected dinosaur DNA. While the leather itself isn’t headed to mass production (yet), the project’s real purpose was to start conversations. What should we be resurrecting? Who decides what’s ethically acceptable in synthetic biology? Can science help us build a more responsible – not just more advanced – society? The campaign sparked this debate globally, reaching an audience of over 800 million.

These aren’t easy questions. But they are necessary ones. And they won’t get asked – or answered – unless the public feels invested. That’s why storytelling matters. Not just to sell an idea, but to help people emotionally connect with it.

Telling stories that matter

We don’t need everyone to understand the science behind CRISPR, cell cultivation or carbon capture. We need them to care. And caring starts with being able to imagine and believe in what the future might look like.

In advertising the goal is always to create something that captures people’s attention and that they might care about. I’d argue that’s just as relevant to science and innovation. If you’re a researcher, founder, or policymaker working on the next big breakthrough, you can’t just rely on the facts. You need a narrative. A symbol. A feeling. Something that invites people in.

Creativity helps you get there. It transforms data into drama. It takes the obscure and makes it obvious. It turns the invisible – DNA strands, molecules, climate models – into something we can see, touch, and talk about. It unlocks demand not by dumbing things down, but by making them emotionally resonant.

Of course, there’s a responsibility here too. When creativity intersects with science, the goal isn’t to overhype or distort. It’s to translate. To respect the integrity of the research while also recognising that humans don’t make decisions based on logic alone. We respond to stories. We’re moved by meaning.

Creativity that demands a response

If we want society to embrace the solutions we so desperately need – from more ethical food systems to climate adaptation technologies – then those solutions need to be brought to life in ways that feel real, urgent, and inspiring.

This is not a new idea. The best science communicators have always been storytellers at heart – from David Attenborough to today’s generation of science-influencers on TikTok. But in a world of ever-fragmenting attention, it’s never been more important to capture the imagination early – to get people talking before the headline is written for you.

Creativity doesn’t just sell culture, it can help accelerate science. It can make the unfamiliar familiar. It can help people imagine better futures and more importantly, want to build them.

Bas Korsten

Bas Korsten is Global Chief Creative Officer, Innovation at VML, known for blending creativity with science and technology. He’s behind award-winning campaigns like The Next Rembrandt and The Mammoth Meatball, which sparked global conversations around AI and cultured meat. With 56 Cannes Lions (including 6 Grand Prix) and a D&AD Black Pencil, Bas has helped redefine how storytelling drives innovation, public engagement, and policy change across biotech, climate science, and synthetic biology.

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Safe AI in university prepares students for work

Rob Telfer — Tue, 05 Aug 2025 13:26:51 +0000

Excitement about AI is matched in equal measure by scepticism, uncertainty, even fear. Seemingly before anyone knew what was happening, students were experimenting with free online AI tools and concerns about cheating and plagiarism emerged. Yet, AI is prevalent in the workplace, and enterprises are embracing its potential to increase productivity and innovation. Higher education has a central role to play in preparing students for a world of work with AI. Yet, they must adopt AI safely, understand its limitations and implement responsibly.

AI gains momentum in the workplace

Technology moves quickly, and AI moves quickest of all. In Q4 2024, KPMG reported that 68% of leaders will invest $50 to $250 million in GenAI over the next 12 months, up from 45% in less than a year.

They anticipate transformational change from such a substantial investment; in Q2 2025, more than eight out of ten (82%) leaders agreed AI will change their industry’s competitive landscape.

They expect productivity, profitability, performance and quality improvements as a result, but they have a skills hurdle to overcome first. Over half (59%) say they have technical skills gaps and 47% that the workforce is resistant to change.

It is never easy to pivot to new technologies, for workers to learn new skills and operate in a different way. But enterprises may reasonably expect that tomorrow’s graduates – their future employees – will bring new abilities to the workplace that they can unite with the established workforce’s expertise to create a formidable team.

AI pressure on education

This expectation puts pressure on higher education establishments to grasp AI’s potential. Indeed, graduates think they should. Seventy percent in one survey said basic training in GenAI should be part of courses, yet more than half (55%) said their degrees didn’t prepare them for AI in the workplace. Another survey tells us only 36% of students received training in AI skills from their university.

The VP for Information Technology at the University of Michigan has called for tuition that builds AI literacy, saying no student should graduate without at least one core course in AI or substantial exposure to AI tools.

Yet, AI has had a rocky start in education. Institutions with reservations that students would pass AI-generated work off as their own may feel vindicated by news of almost 7,000 cases of AI-based cheating in UK universities in 2023/24.

Higher education, like all organisations and indeed individuals, must recognise AI’s limitations, without dismissing its potential. All online tools have their purpose; it is a case of recognising AI’s and enforcing appropriate boundaries for its safe and beneficial use.

To take on this risk, higher education must stand to gain significantly from AI. Of course, it doesn’t have much of a choice. As already explored, AI is out there, in the world – our workplaces and our home lives. Universities take their responsibility to equip students for the future extremely seriously, as well they might. To neglect AI is to neglect employability and enlarge a skills gap that can only damage the UK economically on the world stage.

It isn’t all doom and gloom, though. Far from it. Whilst shouty headlines play up AI’s downside, evidence quietly mounts for its upside. An upside that includes helping students to learn effectively, so they are more likely to perform well and achieve good learning outcomes.

Evidence that AI can benefit students and educators

A Harvard study under peer review compared students learning with AI with those learning without and found higher levels of engagement, motivation and efficiency, alongside enhanced learning gains, in the AI group.

A college in Indiana explored using AI to identify online behaviour patterns that indicated students who were at risk of failing. They identified 16,000 individuals and quickly put support in place for them. This resulted in 3,000 of the contacted students averting failure and achieving a grade C or above. Since the initial pilot, the college estimates the project has helped over 34,000 students.

Using AI positively in this way helps notoriously time-poor educators. Demands on them extend beyond classroom teaching to lesson planning, grading, one-to-one student interaction and a host of administrative tasks – all before they’ve given any attention to maintaining their own subject knowledge and development.

AI can help here by being a real time-saver, lightening the educator’s load of marking, administration and lesson planning. All of which gives tutors more time for tuition and supporting students, aided by advanced data insights such as those the Indiana college drew on, that AI can provide.

Research has shown that teachers using AI for grading work can save an average three and a half hours a week, and those using it to assist in lesson planning can save another three hours a week.

Anything that streamlines activities, automates repetitive tasks or cuts the time it takes to achieve something is a bonus. It can free up time from inputting data and sending out emails to spend on personalised tutoring to benefit students.

In the right hands, AI can enrich rather than corrupt the learning experience. It can help develop dynamic content, such as simulations and interactive quizzes, to capture and keep students’ attention.

It can help personalise learning by tailoring materials according to individual learning styles and needs, for optimised outcomes. Importantly, it can also assist in adapting learning content for accessibility.

How to integrate AI responsibly

These positive aspects of AI in teaching and learning present a compelling counterargument to the risks AI brings, but it must be integrated responsibly.

It’s no good producing AI-literate educators and students if they don’t know how to assess AI with a critical eye and use it ethically. As universities evolve their strategies to equip graduates for jobs that will use AI, they must safeguard their academic standards.

Institutions must challenge AI-related misconduct, but to do so they need unambiguous policies and guidelines that make it clear misconduct has occurred.

Students and tutors need training to recognise bias that can be present in AI-generated responses and not to implicitly trust that content. AI can assist in brainstorming and gathering information – it is ideally placed to scan, retrieve and analyse large data sets – but its output shouldn’t be presented as original work. There are issues of plagiarism, copyright infringement and inaccuracy to understand and protect against.

‘Conversations’ with AI tools shouldn’t be mistaken for private interactions. Confidential and personal data should be protected, which means keeping it out of large language models.

Higher education, like all organisations and indeed individuals, must recognise AI’s limitations, without dismissing its potential. All online tools have their purpose; it is a case of recognising AI’s and enforcing appropriate boundaries for its safe and beneficial use.

AI exploded onto the scene and early adopters were experimenting with it whilst rules and procedures were catching up. As AI now becomes widespread across universities, educators and decision makers have a vital role to play in adopting it responsibly and helping tutors and students unlock its full potential. Transparent, principled implementation is a must for education to achieve an important objective – a next generation of AI-literate professionals, equipped to use this exciting technology effectively and ethically.

Rob Telfer

Rob Telfer is Director of Higher Education at D2L, a leading learning management organisation and the originator of the Brightspace Learning Management System, used by millions of teachers, learners & users daily.

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Tech versus toxins: Cleaning Europe’s rivers

Daryl McCloskey — Thu, 31 Jul 2025 08:34:40 +0000

In February 2025, the European Commission released the ‘Water Framework Directive Report’ following the collection of data from around 90% of the EU’s surface water and groundwater bodies. Just 39.5% of surface water bodies meet good ecological standards, with mercury and other pollutants found in 19% of Europe’s rivers. Built up over 50 years of historic pollution, these chemicals are present in large quantities, causing damage to precious ecosystems, such as wetlands and estuaries.

In England, despite a reduction target of 40% set by the Environment Agency, water company-reported pollution incidents have increased by 30%. In 2024, Surfers Against Sewage received over 1,800 sickness reports, highlighting the rising prominence and health consequences of hazardous chemicals in our water supply.

The threat of PFAS

PFAS (Per- and polyfluoroalkyl substances), or ‘forever chemicals’, pose the largest threat to European water systems. Their current prevalence in waterways is due to their wide uses in industrial applications and consumer goods, such as non-stick cookware and waterproof clothing.

Given PFAS break down very slowly over time, they remain in the water supply for decades, contributing to adverse health outcomes for those using it. Studies have linked even low levels of exposure to serious health effects, including cancer, liver damage, and immune system disruption.

Due to their resistance to degradation, PFAS have been found in rivers and groundwater across Europe, with some regions reporting levels far exceeding safety thresholds. In English rivers, 81 of 105 sites exceeded the EU and UK’s proposed environmental quality standards for PFAS. It’s these chemicals that exacerbate ecological harm and threaten biodiversity.

The business risk

The presence of chemical pollution can have far-reaching consequences for industries reliant on clean water. Businesses within agriculture and food production, for example, face potential fines and reputational damage if they unknowingly make use of contaminated water. For EU Member States, the European Green Deal initially set an ambitious target to reduce the use and risks of chemical pesticides by 50% by 2030. However, this legislative proposal was withdrawn in early 2024 due to significant opposition from farming groups and member states. Nonetheless, the original goal highlights the increasing shift toward stricter regulation and the potential for future fines.

Without decisive action, the risks of pollution and water scarcity will only grow, endangering ecosystems and public health.

To ensure future-proof compliance, companies in and outside the region should proactively prepare for regulation, building waste-management strategies that recognises the danger of polluted water. In the EU, the Urban Wastewater Treatment Directive, requires EU countries to protect human health and the environment from the effects of untreated urban wastewater, demonstrating a governmental and collective approach to cleaning rivers and lakes. In the UK, The Water (Special Measures) Bill was introduced to give regulators the authority to take tougher and faster action against water companies damaging the environment and failing their customers. Despite the substantial investment in monitoring and treatment systems required, these regulations underscore the importance of compliance with the directives. Beyond compliance, businesses can adopt innovative practices and technologies that reduce pollution and improve water quality. In doing so, companies can promote a cleaner environment while enhancing their reputations.

The role of software versus toxins

As businesses face mounting pressure to meet compliance and regulatory demands, leveraging technology can significantly streamline chemical management and wastewater treatment. By making it easier for employees to create relevant and compliant chemical risk assessments, the right solution can be vital to ensuring that chemical waste is handled properly. This includes ensuring easy access to safety data sheets (SDS), enabling full analysis of chemical risks, and substituting the chemicals most hazardous to people and the planet.

For employees dealing with chemicals, training is crucial. Once they understand the risks that arise from handling chemicals, solutions like chemical reporting software ensure best practice in chemical compliance with a built-in, intuitive chemical inventory that keeps track of all relevant data in one place. Employees can use these tools to analyse chemical risks digitally, rather than relying on outdated methods or general experience. Chemical exposure software plays a critical role in managing workplace exposures and supports reducing exposures through efficient tools.

As part of a broader technology ecosystem, ESG software and reporting tools can complement chemical safety solutions, offering additional capabilities for businesses to track and report on their environmental, social, and governance (ESG) performance. Together with chemical safety software, ESG tools help organisations maintain comprehensive compliance, monitor their sustainability efforts, and mitigate environmental impacts.

This not only ensures safety but also contributes to environmental sustainability by reducing the impact of chemical waste. By helping businesses navigate complex regulatory requirements and enhance workplace safety, these solutions enable proactive chemical management while also reducing the long-term environmental impact of industrial activities.

The future for cleaner rivers

The European Commission’s report highlights the urgent need for action to address water pollution. While regulatory measures have been instrumental in mitigating pollution, businesses must play their part in improving waste management and chemical safety.

Through digitalising their chemical safety processes, industries can ensure compliance, reduce environmental harm, and contribute to the broader goal of protecting Europe’s waterways. Without decisive action, the risks of pollution and water scarcity will only grow, endangering ecosystems and public health.

Daryl McCloskey

Daryl McCloskey is Solutions Consultant at EcoOnline, delivering innovative environment, health and safety, chemical management and ESG/sustainability technology solutions to forward-thinking leaders around the world.

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Creating the ‘AI native’ generation: The role of digital skills in education

Erin Gajdalo — Tue, 15 Jul 2025 10:15:26 +0000

The use of AI is no longer a hypothetical in education in the UK, it’s a national priority. The Labour government has shown its commitment to developing AI skills, with a plethora of recent investments focused on young people. Among them is a pledge to invest £187 million in TechFirst, a nationwide programme designed to embed digital skills in classrooms and communities to equip one million young people for the future.

Boosting AI skills has the potential to drive economic growth and productivity and create jobs, but ambition must be matched with effective delivery. We must ensure AI is integrated into education in a way that encourages students to maintain critical thinking skills, skeptically assess AI outputs, and use it responsibly and ethically. Education should also inspire future tech talent and prepare them for the workplace.

An “AI learning arc” is at the heart of this effort—a long-term AI education approach that begins in primary school, deepens through higher education, and continues into the workplace. This arc will ensure sustained, relevant digital literacy as technology continues to evolve and change.

There are extreme examples—like a teacherless school in Texas making headlines—but the future of AI in education is more likely a blended model. We should expect to see AI being used to help with tasks like brainstorming ideas and collating research while the human element of creativity and critical thinking remains at the core.

Today’s opportunity is to ensure students grow up as AI natives. Not only confident users of AI, but informed and ethical participants in a world increasingly shaped by it.

AI is reshaping the future of work

The AI sector is growing 30 times faster than the rest of the economy and is projected to be worth £800 billion by 2035. Demand for AI skills at work has grown 21% annually since 2019 and research from the Department for Science, Innovation and Technology (DSIT) shows that by 2035, around 10 million workers will use AI in their roles with 3.9 million in AI-centric jobs. Pluralsight’s recent AI skills report found that 95% of organisations already see AI skills as a hiring factor.

Against this backdrop, avoiding AI in the classroom won’t protect students, it actually risks leaving them ill-equipped for the jobs they are trying to secure in the future. The integration of AI into education isn’t just about keeping pace with technological change, it’s about ensuring young people are employable in an economy increasingly underpinned by it.

What should AI in education look like?

The integration of AI into the school curriculum should not be seen as a threat to learning or creative thinking; it presents a valuable opportunity if we integrate it with purpose.

For example, teaching students how to effectively interact with large language models (LLMs)—through prompting, evaluating outputs, and applying them to real-world tasks—can nurture critical thinking, AI literacy, and problem-solving. These are key skills for the workforce of the future, where AI is becoming deeply embedded across industries and employers want to increase productivity by eliminating manual tasks.

Teaching AI responsibly

Meaningful integration also requires teachers to focus on teaching responsible AI use. Encouraging tools like ChatGPT in schools must involve clear guardrails and thoughtful supervision to ensure they support rather than undermine critical thinking. In doing so, students gain an important skill: learning how to use AI thoughtfully.

AI literacy includes understanding how to question machine-generated content, recognise bias, and protect personal and sensitive data. These skills are essential for becoming informed, empowered digital citizens.

Students who learn how to use AI now will build the competencies that industries want and need for years to come.

Embedding AI in education should never mean encouraging shortcuts or undermining critical thinking. Instead, it’s a chance to put digital ethics at the heart of learning—preparing students to use these tools with good judgment, reflection, and responsibility.

Clear national guidance will be essential. The UK’s upcoming international summit on generative AI in education in 2026 will be a pivotal moment to set out ethical standards and best practices for schools worldwide.

An AI learning arc

The government’s investments, from TechYouth to adult reskilling programmes, reflect a growing recognition that AI skills must be developed at all stages of life, forming the foundation of an AI learning arc.

But realising this vision requires a shift in how we think about education entirely. Learning can no longer be seen as something frontloaded in youth, it must become a continuous, lifelong commitment. In a world where digital skills can become obsolete in just a few years, ongoing education is essential. In fact, the World Economic Forum predicts that by 2030 more than half of today’s workforce will need reskilling due to the rapid pace of technological change.

Building the AI learning arc means weaving AI literacy throughout the entire educational journey. This could start with simple tutorials about AI and introductions to tools in primary school, expanding into project-based learning in secondary school, moving into introductory development and machine learning courses in higher education, and then ongoing skills-based learning in the professional world.

We can already see international models developing this approach in higher education. Ohio State University, for example, has recently launched an ‘AI fluency initiative’. This is a commitment to integrate AI tools into all undergraduate courses, ensuring that students know how to apply AI tools to their respective fields.

Building an AI talent pipeline

AI fluency is only one part of the picture. Amid a global skills gap, we also need to capture the imaginations of young people to work in tech.

To achieve this, AI and technology education must be accessible, meaningful, and aspirational. That requires coordinated action from schools, industry, and government to promote the real-world impact of digital skills and create clearer, more inspiring pathways into tech careers and expose students how AI is applied in various professions.

Early exposure to AI can do far more than build fluency, it can spark curiosity, confidence and career ambition towards high-value sectors like data science, engineering and cybersecurity—areas where the UK must lead. Exposure to the technologies that underpin these industries at an early age makes tech careers feel tangible, not theoretical.

Final thoughts

Students who learn how to use AI now will build the competencies that industries want and need for years to come. But this will form the first stage of a broader AI learning arc where learning and upskilling become a lifelong mindset, not a single milestone.

As we begin to incorporate AI into education, we need to ensure we equip the AI-native generation to thrive with the skills, judgment, and drive to shape the future, not hope to inherit one that looks similar to today.

Erin Gajdalo

Erin Gajdalo, is Chief Executive Officer at Pluralsight. As a leading provider of online tech skills training and workforce development solutions for businesses and individuals, Pluralsight helps clients around the world successfully navigate complex digital transformations.

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AI remains underused in the race to Net Zero – it’s time for change

Stig Martin Fiska — Mon, 14 Jul 2025 09:30:06 +0000

Artificial intelligence (AI) can accelerate progress towards Net Zero. Yet it remains underused in sustainability efforts, often confined to pilots and small-scale projects rather than being embedded in the systems where it can deliver the greatest impact.

This is especially clear in marine industries. Oceans play a crucial role in regulating the climate and supporting global trade, but are increasingly under strain from pollution, habitat loss and industrial expansion. AI can help address these challenges. It is already being used to monitor water quality, track biodiversity and support smarter design of infrastructure. These tools are not theoretical; they’re in use today. But the process of adoption is slow and disconnected. So, what’s holding things back?

Environmental data must be treated as infrastructure

One of the core challenges lies in the data. AI systems need consistent, high-quality data to function effectively. But in many marine and coastal settings, environmental data is often patchy, siloed or hard to access.

For example, river flow data is critical to understanding flood risk, pollution and overall water quality. However, it is often missing or incomplete, particularly in smaller or remote catchments. Traditional monitoring methods rely on expensive equipment and are limited in coverage, but recent developments in AI are helping to bridge these gaps.

Reaching environmental goals and maintaining the ocean’s essential functions depends on timely, data-led and predictive decision-making. AI can support that, if it is treated as a strategic priority rather than an experiment.

A new open-source deep learning model, developed by Cognizant, estimates daily river flows by combining historic weather data with catchment characteristics without the need for physical sensors. Trained on decades of open datasets from hundreds of UK basins, it can generate rapid insights even in remote or previously unmonitored areas. This makes large-scale, cost-effective environmental assessments more accessible for those managing flood risk, water quality and ecosystem health.

The same principles apply in coastal and marine contexts. If we treat environmental data as critical infrastructure – investing in shared standards, open models and interoperable platforms – AI can become much more scalable. FAIR data principles (Findable, Accessible, Interoperable, Reusable) provide a solid starting point, but broader alignment is still needed to encourage widespread real-world adoption.

Moving from pilots to platforms

AI is already in use across different parts of the marine sector. Current models can detect harmful algal blooms, track biodiversity using environmental DNA and predict the impact of infrastructure on local ecosystems. Deep learning systems are forecasting water quality and tracing pollution to its sources. In engineering, generative AI is helping design marine structures that support biodiversity. For example, the European Space Agency’s (ESA) Sentinel satellites, combined with AI, are being used to map cyanobacteria blooms and marine litter, enabling large-scale coastal water quality monitoring that was previously unfeasible.

These examples show what’s possible, but most applications remain isolated. When insights stay locked within individual projects or departments, the broader benefits are lost. To realise the full value of AI, sectors need to work together. The same models used to monitor coastal waters, for example, could also inform port operations, infrastructure planning and conservation efforts.

This will require investment in shared infrastructure and collaboration between policymakers, industry and environmental experts. But the technology is already mature enough to support it.

Policy must support action

Policy frameworks have a major role to play in shaping the uptake of environmental AI. Many governments have set out strong sustainability targets, but support for deployment is often limited. Without clear standards or guidelines for how AI can be used, organisations hesitate to adopt it at scale.

What is needed is practical alignment. This includes regulatory standards for environmental AI tools, guidelines on data governance and incentives that encourage cross-sector collaboration. In marine contexts, policies that support the repurposing of existing assets, such as decommissioned energy platforms, can help align sustainability and economic priorities.

For instance, AI is already being used to assess the viability of converting oil and gas rigs into offshore wind bases, carbon capture hubs or aquaculture farms. Rather than removing ageing assets, digital twins and predictive models can help safely extend their life for green use cases, which benefits both the climate and the economy. With clearer frameworks and strategic funding, successful pilots can scale into long-term systems.

It’s time to act

Some commentators believe environmental AI is not yet ready, that we need more data, more consensus and better tools. But the solutions already exist, and many are affordable and scalable today. Waiting carries its own risks, especially as marine ecosystems face growing pressure.

AI is well suited to adaptive learning. Deploying systems now, even if imperfect, allows them to evolve and improve through real-world use. The more data they gather, the more accurate and useful they become.

Reaching environmental goals and maintaining the ocean’s essential functions depends on timely, data-led and predictive decision-making. AI can support that, if it is treated as a strategic priority rather than an experiment. The case for action is clear. Now is the time to scale it.

Stig Martin Fiska

Stig Martin Fiska is Global Head of Cognizant Ocean. Having co-founded several successful companies, starting at age 16, he now advises C-level leaders on digital transformation, AI, sales, and business development, often acting as a change agent across organisations.

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Informed choice or chance and accident? How did you end up in your career?

Stuart Walker — Wed, 09 Jul 2025 09:11:48 +0000

Simple question, but if you’re anything like me the answer is a winding road of often, in
fact mostly, accidental choices, opportunities, mistakes and chance events.

Now this is all well and good I tell myself as I reflect on my first teenage paper round and local supermarket job. But sooner or later shouldn’t I have fleshed out some sacred
vocational pathway that honed my skills towards an ultimate calling that optimised my
potential?

What about you? Reading this, are you reflecting on an intentional and carefully
developed pathway full of self-actualisation and accelerating potential?

My first memory of considering this question was at school; I’d decided I wanted to be a cameraman. Our class had just got back from being in the audience of a local TV nature programme, spent time observing the behind-the-scenes work of the recording team. I felt inspired. But in the years that followed that inspiration dimmed, my school experience was partly, and at times mostly, an exercise in survival. I’d ended up with solid GCSEs and A Levels that were, in terms of my choices, as much about whether I thought I could actually “do” the subject, (and also whether I liked the teacher).

Similarly, as I chose University, intern and ultimate graduate opportunities, a feeling that these were all sensible, but in many ways accidental choices that I’d use to add to my CV to help my eventual life’s calling at some later point in time. Here I am writing this, 25 years later, still unsure if I ever properly assessed perhaps one of the most important questions of all of our lives. How about you? Was your approach along similar lines to mine? Did you feel you had what you needed to make informed choices?

Now don’t get me wrong, I don’t feel like I’m writing this from a position of failure. I’m
very proud of the roles, achievements and perhaps most importantly, people I’ve
worked with since, albeit there’s been some mistakes which I’ve tried to learn from, but still regret. Not to mention that some of the best opportunities I have had, came about to some extent by chance.

There’s a more serious point in all of this, how do we help support our schools, colleges
and support organisations in helping young people working on their own pathways?
How do we give more young people more opportunities, chances and help inform their
choices? In my view, this is much harder now.

A couple of points from the Future of Jobs Report by the World Economic Forum:

• Two fifths (39%) of workers existing skill sets will be transformed or become
outdated over the 2025-30 period.

Are young people future proofed in studying what they need for the future then? Or
learning things that will soon become outdated? Or to some extent has it always
been the case that things we learn at school get gradually updated over our lives?

• AI and Big data top the list of fastest-growing skills.

How many young people (or adults for that matter) know someone with careers in
these areas or have a sense of whether they might like a role with these skills?

Should we redouble efforts to grow engagement between industry, employers and
education institutions to help give learners more informed choices? This can be
simple to say, but complex to make happen. There is some inspirational effort happening out there though in my view.

• Broadening digital access: 60% of employers are expecting it to transform their
business by 2030.

Let’s not forget the Digital Poverty Alliance report that estimates 1 in 5 young people
are in digital poverty. If more and more careers involve digital, are young people
sufficiently versed in digital skills and have access to reach these opportunities?
So where am I going with all this? What helps ? And how can I help?

As you can probably tell from reading this, I don’t view myself as some shining
example to help inspire progress. But one thing I have been fortunate enough to
develop over the last 10 years is a wide range of examples of trailblazing efforts and
people working constantly to help make a difference with some of these challenges.
I aim to help share their / your insight.

So what’s your own experience of these things? Are you involved in work to grow
skills, experience and inform choice for young people that others might benefit from
hearing about? What’s working? How do we make progress?

I’ll be putting together a series of these articles (with your help) to try to feature
perspectives well beyond my own. I’d love to hear from you, please do reach out.

References:
https://www.weforum.org/publications/the-future-of-jobs-report-2025
https://digitalpovertyalliance.org/digital-poverty-in-the-uk-a-socio-economic-assessment-of-the-implications-of-digital-poverty-in-the-uk/

Stuart Walker

With over 25 years experience in tech and 10 years experience in Education and Edtech, Stuart Walker is the Founder of Next Level Edu and former Head of Education UK & Ireland at Intel.

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The future of healthcare: agentic automation as agents for change

Matt Hogarth — Mon, 07 Jul 2025 10:35:15 +0000

In light of the £3.25 billion Transformation Fund announced in the 2025 Spring Statement and structural reforms to NHS England aimed at improving efficiency, the UK healthcare system has a unique opportunity to embrace cutting-edge technologies.

Agentic automation is just one such innovative solution which offers unprecedented potential to streamline operations, enhance patient care and alleviate pressure on frontline services. As the NHS undergoes sweeping changes and the government doubles down on AI-driven innovation, now is the moment to integrate agentic automation as a core driver of healthcare advancement.

The value of agentic automation in healthcare

In the UK, the NHS continues to operate against a backdrop of staff shortages, administrative burdens and a patient wait list standing at 7.5 million. The case for digital transformation to drive greater efficiency then has never been more urgent.

Agentic automation is a powerful solution for alleviating these pressures, driving productivity through cost savings and resource optimisation. Research has shown that Robotic Process Automation (RPA) can enable complex organisations to achieve a 30-50% productivity boost. By automating routine administrative tasks, such as appointment scheduling, patient data management and claims processing, agentic systems enable healthcare staff to prioritise higher-value, patient-centred activities. This shift not only boosts internal efficiencies but also improves care quality.

But why agentic over traditional automation? Where traditional automation is predefined, agentic agents combine AI, automation and orchestration for more adaptive outcomes. These systems are not simply tools for automation; they are intelligent agents capable of making informed decisions, learning and acting within their environment to optimise efficiency.

By acting as autonomous decision makers, agentic systems can help already short-staffed and over-burdened UK healthcare providers plan, adapt and optimise processes with minimal human oversight. This autonomy allows for more responsive and scalable solutions, thereby enhancing operational efficiency, reducing human error and mitigating delays in critical processes.

Automation in action across the NHS

Staff across the NHS are beginning to embed automation into everyday clinical routines. For example, automated technologies are optimising the scanning process, improving efficiency and accuracy in how scans are conducted. These innovations allow for precise and accurate detection of medical conditions, enabling clinicians to make well-informed decisions.

Building on the momentum, AI-driven automation is becoming a cornerstrone of the NHS’s approach to digital governance. AI-powered document management solution MyStaff, has been designed to simplify access to over 2,500 policies, procedures and patient care guidelines. Since implemementation at Mid and South Essex NHS Foundation Trust, average document retrieval times have dropped from 10 minutes to just 30 seconds, unlocking 140,000 additional hours for patient care. With 90% of the Trust’s workforce actively using the platform, MyStaff is setting a new benchmark for how automation can streamline operations, enhance digital efficiency, and strengthen governance across the NHS.

Embracing the agentic future

Agentic automation is set to transform healthcare by empowering, not replacing, those driving its operations. By augmenting human expertise with intelligent agents, healthcare organisations can streamline operational efficiency, minimise errors and realise significant cost savings. This collaboration alleviates the burden on professionals, allowing them to focus on quality care and delivering a better patient experience .

By leveraging these technologies, we are paving the way for a smarter, more efficient healthcare system aligned with the UK Government’s three key healthcare initiatives focused on moving care from hospital to community, sickness to prevention and analogue to digital. Agentic automation is central to advancing this digital transformation, directly supporting the government’s vision toward a fully digitalised healthcare system.

However, automation alone is not a silver bullet. Improving clinical workflows requires ongoing innovation and adaptation to new challenges. As we move forward, AI agents will play a pivotal role in driving this transformation and reshaping the way healthcare is delivered.

Matt Hogarth

Matt Hogarth is Director of Healthcare UKI at UiPath, with more than two decades of experience in healthcare IT. Passionate about creating meaningful change within the NHS, he focuses on leveraging technology to support patients and empower front-line staff. Matt specialises in steering digital transformation efforts that improve care delivery and boost operational efficiency across the region.

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Digital accessibility is here to stay: How the EAA can be a catalyst for positive change

Sil Segal — Mon, 23 Jun 2025 09:33:17 +0000

Digital accessibility has moved to the forefront of business thinking this year as companies ready themselves for the introduction of new regulatory requirements.

With the deadline for compliance with the European Accessibility Act (EAA) set for June 28, many companies conducting business within the EU are having to ensure their digital assets are accessible to all. Penalties for non-compliance will vary across the EU’s 27 Member States, but fines and potential market bans are on the table.

The likelihood is that a significant number of companies will not meet this deadline. According to our State of Digital Accessibility Report, more than a third (34.3%) of European respondents surveyed in Q3 2024 hadn’t yet taken action to comply with the EAA – despite 87.3% saying that the law applies to their organisation.

But, while the risks are real, avoiding penalties is hardly the only reason businesses should consider prioritising digital accessibility. Beyond helping organisations uphold a moral obligation not to discriminate, greater accessibility can fuel commercial success.

It’s important to remember that digital accessibility is an ongoing journey. While this journey often starts with meeting compliance requirements, organisations that invest in accessibility ultimately invest in innovation that benefits everyone

In the same report mentioned above, we found that more than nine out of ten professionals in Europe consider digital accessibility a competitive advantage – with a similar number (84%) saying it helps to improve revenue. So, although the EAA may provide an imperative to act, businesses should also treat compliance as an investment in business growth.

To help anyone in the early stages of their digital accessibility journey realise those benefits, I’ve outlined five steps that will help organisations start working toward EAA compliance.

1. Understand risks and requirements

With every Member State implementing the EAA through national legislation, obligations will differ depending on the country. In France, for example, certain organisations are required to publish annual and multi-year accessibility plans. Additionally, some countries have harsher penalties than others. In Ireland, for instance, non-compliance may in certain cases be punishable with jail time. It’s important to understand what your specific obligations are, based on the markets you operate in.

The EAA also has different requirements for different products and services. So, if you haven’t already, review your offerings and find out what falls within the scope of the EAA. You will want to consider what your company offers right now, and also what you’re planning to introduce in the future.

2. Audit and amend

Once you’ve identified your obligations, audit your digital assets to ensure they conform with the European standard for information and communications technology (ICT) accessibility: EN 301 549.

This audit should combine automated and manual testing. Specialised software tools are available that can quickly scan digital experiences to detect common accessibility issues. However, no tool on the market is currently capable of a comprehensive and wholly accurate assessment – so manual testing by accessibility professionals is also required. Preferably, these testers will include people with disabilities who are native users of assistive technologies, like screen readers.

Thorough testing will reveal any potential barriers for users, such as difficult-to-navigate interfaces and colour contrast problems. Then, remediation can quickly begin.

3. Ensure progress is sustainable

Digital accessibility is a continuous practice, not a one-time project. To remain compliant over the long term, companies will need tools and processes that support sustainable progress. These include:

- Monitoring: New issues can arise as digital product or service offerings are updated and upgraded. Regular scans and checks to catch these issues are necessary to stay on track.
- The gathering of feedback: Encouraging users to get in touch and share input can help you provide a better digital experience.
- Specialised accessibility tools: Tools like accessibility software development kits (SDKs) and design plugins allow your team to proactively resolve problems, and more efficiently scale digital accessibility across all your company’s products and services.

4. Educate your workforce

It’s also advisable to provide accessibility training to your teams. The EAA deadline is an opportunity for all organisations to draw attention to digital accessibility and empower staff with the skills to efficiently address and prevent issues.

By educating everyone from your product developers to the marketing team, you’ll be more likely to realise potential business gains. For example, research shows that professionals at organisations that offer “highly effective” training are 2.7 times more likely to say that digital accessibility contributes to improved customer retention.

5. Make a statement

The EAA demands transparency. To comply with the law, organisations must publish detailed information about their offerings’ accessibility, which we recommend providing in the form of an accessibility statement. This statement should include a description of your product or service, as well as its accessibility features. It should also explain how your offering conforms to standards like EN 301 549, and provide contact information for users to submit feedback.

Inclusive design benefits everyone

It’s important to remember that digital accessibility is an ongoing journey. While this journey often starts with meeting compliance requirements, organisations that invest in accessibility ultimately invest in innovation that benefits everyone.

Consider the “curb cut” effect: Curb cuts were originally invented by a group of students in the U.S., who reduced the height of a pavement curb to help classmates in wheelchairs. By cutting this curb, they also ended up providing greater access to people with buggies, bicycles, trollies and more.

Digital accessibility has a similar result. When companies remove barriers for users with disabilities on a website, mobile app, or digital product, they build more streamlined, intuitive, and enjoyable digital experiences for all users.

Rather than treating the EAA as an imposition, businesses should embrace it as a catalyst for positive change. The upcoming deadline is an opportunity to understand whether your digital experiences are open to everyone – and, if not, take the necessary steps to ensure the widest possible group of people can engage with your business.

Sil Segal

Sil Segal is Global CMO at Level Access, leading marketing strategy, brand, communications, and demand generation for the category leader in digital accessibility.

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Obstacles to net zero: The renewable challenges facing all UK businesses

Robert Groves — Thu, 29 May 2025 11:43:48 +0000

Every business in Britain is facing the same unavoidable challenge. The UK has committed to achieving net zero carbon emissions by 2050 – a target first set by Theresa May’s Conservative government in 2019 and then renewed under Labour.

Businesses make up roughly 19% of the nation’s carbon emissions. However, just 10% of companies worldwide have set targets to achieve 100% renewable energy usage, according to the CDP, a global non-profit that helps companies and public organisations disclose their environmental impact. Companies should be working towards the government-mandated goals now, but navigating a complex energy market is no mean feat, and the challenges just seem to be growing.

For example, AI provides the energy market with a double-edged sword as it will optimise operations and reduce costs, but the computational power required to train and run AI is significant. As its capabilities grow, so does its environmental footprint, and we need to see businesses focus on managing this as they continue to implement the technology across their products and services.

Outside of controlling emissions being the right thing to do for the planet, it also makes good business sense. Whether this is by avoiding fines from regulators for not meeting green energy goals or getting ahead of the competition by adopting a sustainability-first mindset now. It’s time for action – but what’s the first step?

Sourcing renewables

For many organisations, the energy that fuels their operations is something of a black box, with limited visibility of where the power comes from. Without the ability to see how much of their supply is sourced renewably, businesses face challenges in understanding and reporting their renewable usage.

This situation is changing rapidly. Organisations now have access to traceability technology that informs them exactly where their power originates, while also monitoring energy consumption and recording a Carbon Free Energy (CFE) score. The precision of traceability systems enables leaders to take the guesswork out of net zero initiatives. By understanding the granularity of their company’s energy consumption, businesses can reduce the uncertainty about their use of clean, renewable energy and report with confidence.

Sustainability pressures

There is no doubt that sustainability commitments can be a catalyst for change. However, while these measures are clearly well-intentioned, the growing weight of compliance, reporting obligations, and reputational risk has made the path to net zero more complex to navigate. Companies are under pressure to demonstrate measurable progress, yet often lack the resources, clarity, or support to deliver on these demands at pace.

In some sectors, sustainability reporting requirements can consume time and capital that might otherwise be allocated to on-the-ground decarbonisation initiatives. For others, uncertainty about which frameworks to prioritise – and how best to evidence their efforts – creates fear and hesitancy, slowing down action and creating friction.

To maximise the benefits of sustainability objectives, companies should prioritise practical, data-driven strategies that enhance visibility and understanding of their energy supply chains. Embedding this approach into core operations will also be a key enabler of success. Businesses committed to early progress are likely to view reducing their carbon footprint as more than a compliance burden, understanding that it can be a framework for making smarter, more resilient decisions in a changing energy landscape.

Evolving regulations

The shifting landscape of compliance and regulation can also create major hurdles on the road to net zero. As governments and international bodies refine their approaches to climate disclosure, organisations face uncertainty over how to future-proof their strategies. This lack of clarity can hinder investment and lead to decision paralysis.

To move forward, businesses should build agility into their sustainability plans, stay informed through credible sources, invest in robust data infrastructure, and focus on actions that drive genuine emissions reduction regardless of how regulation evolves. Although it is not possible to predict the future of compliance, we know that focusing on ensuring future flexibility will be key to meeting evolving requirements.

Transformation burdens

One of the most significant obstacles to achieving net zero is the sheer scale and speed of infrastructure transformation required. Businesses must shift away from decades-old energy systems while maintaining operational continuity and managing cost.

This often means unpicking entrenched consumption habits and committing budget during periods of economic pressure. To navigate this challenge, organisations should adopt a phased, data-informed approach – starting with energy audits to identify quick wins, then investing strategically in technologies and supply solutions that offer both carbon and cost savings over time.

The obstacles on the road to 2050 may look significant but the message to leaders is clear: overcoming the net zero challenge isn’t about overhauling everything at once, but about building flexibility, resilience and visibility into energy decision-making so that progress remains achievable, even in a volatile environment.

Right now, businesses have time on their side. However, 25 years can pass very quickly. The message to leaders is clear: don’t be nervous about the scale of the challenge. Get started today and you’ll be in a strong position to unlock the benefits of net zero and green energy well before the deadline.

Robert Groves

Robert Groves is CEO at SmartestEnergy. Robert joined SmartestEnergy in 2004 as Vice President of Commercial. He became Chief Operating Officer in 2006 and was ultimately appointed as Chief Executive in 2011. He is responsible for managing SmartestEnergy’s 3.3GW of generation capacity and supplying electricity to large business consumers, driving us forward on our net zero journey.

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The hidden carbon cost of business tech: Beyond AI hype, what else are we overlooking?

Al Kingsley MBE — Fri, 23 May 2025 11:20:30 +0000

As global headlines focus on the environmental cost of artificial intelligence – from the staggering energy required to train large language models to the carbon-intensive infrastructure powering real-time generative tools – another carbon culprit is quietly humming away in the background: our everyday business technology.

While the AI conversation is timely and necessary, it risks obscuring a broader, equally urgent issue. The reality is that long before AI began soaking up terawatts of electricity, organisations were already contributing to digital carbon emissions through the basic tech that underpins their daily operations. Idle laptops, always-on servers, neglected cloud storage and outdated IT procurement practices all play a significant, and frequently overlooked, role in shaping a company’s environmental impact.

In the race to decarbonise, businesses have become increasingly sophisticated in tackling emissions from travel, energy consumption in buildings and packaging waste. Yet the carbon cost of IT infrastructure remains under the radar for many, despite its growing contribution to global greenhouse gas emissions.

It’s time we changed that.

The silent emissions of everyday tech

The carbon footprint of IT operations is often invisible, largely because digital tools are so seamlessly embedded into our working lives. But just because something is virtual doesn’t mean it’s impact-free.

A typical laptop left in standby mode overnight might seem harmless, but across an office of hundreds or thousands, the cumulative energy waste is significant. Multiply that by 365 days, and you begin to grasp the scale. Similarly, servers that run continuously, whether or not they’re being actively used, draw power 24/7. Even cloud services, often perceived as more efficient, come with environmental costs: the data centres hosting them require enormous amounts of electricity and cooling, especially when services are left underused or unmanaged.

The hidden carbon cost of business tech isn’t in some distant server farm or futuristic algorithms – it’s on our desks, in our server rooms and in our inboxes.

Research by the International Energy Agency suggests that data centres already account for around 1–1.5% of global electricity demand, and this is expected to grow rapidly with the rise of AI and digital workloads. But for most businesses, it’s not the headline-grabbing compute clusters or hyperscale cloud infrastructures that are the issue. It’s the aggregation of smaller inefficiencies across countless devices, services and user behaviours.

Beyond awareness: changing digital habits

Addressing this challenge doesn’t necessarily require radical reinvention. In fact, the first and most effective step for many organisations is simply changing digital habits.

1. Audit before you optimise: Start by understanding what you have. Carry out a comprehensive audit of your IT estate, including hardware, software, subscriptions, server usage and cloud services. Identify devices that are left running when not in use, underutilised virtual machines, redundant software licences and duplicated storage. Without clear visibility, businesses can’t make informed, strategic decisions.
2. Switch off and sleep: Encourage staff to power down laptops, monitors and other devices at the end of the day. Activate automated sleep or hibernation settings across devices to ensure minimal energy draw during inactivity. Better still, use remote monitoring and management tools that provide visibility over device usage and enable intelligent automation, helping IT teams reduce unnecessary energy consumption without manual intervention.
3. Right-size cloud usage: Cloud services are often sold as scalable and efficient, but without active oversight, many businesses end up paying for unused capacity. Monitor your cloud workloads and decommission anything no longer needed. Consolidate storage, archive older data and delete duplicate files where possible. It’s not just good housekeeping; it’s good environmental stewardship.
4. Reduce travel through remote support: In many organisations, IT teams still rely on in-person interventions to diagnose and resolve device issues, particularly across multiple offices or remote sites. Where possible, enabling secure remote support can cut down on unnecessary travel, reduce delays, and enhance overall responsiveness. Tools that allow teams to remotely access, control and troubleshoot user devices can play a key role in lowering operational emissions.
5. Modernise infrastructure thoughtfully: Upgrading to newer, energy-efficient devices can reduce emissions, but only when done responsibly. Dispose of old tech through accredited e-waste recycling services and prioritise repair and reuse where appropriate. Shift from a culture of automatic replacement to one of longevity and circular economy principles.
6. Embed sustainability in IT procurement: When choosing new tech, assess the sustainability credentials of suppliers and manufacturers. Look for certifications around energy efficiency, recyclable materials and ethical sourcing. Opt for devices with lower power consumption and longer lifecycle support.
7. Measure and report: Sustainability isn’t just a matter of conscience – it’s increasingly a regulatory and reputational requirement. Track the energy consumption of your digital estate and factor IT-related emissions into your organisation’s broader ESG (Environmental, Social, Governance) reporting. Transparency drives accountability.

From digital transformation to sustainable digital practice

The drive for digital transformation has brought enormous gains in productivity, flexibility and innovation. But it has also introduced new layers of complexity, and with it, new sources of environmental impact.

As we embrace the benefits of automation, AI and hybrid working, we must also recalibrate what sustainability looks like in the digital age. That means recognising that IT is not just an enabler of greener working practices (through paperless offices, virtual meetings and smart systems) – it’s also an area in urgent need of carbon-conscious management in its own right.

The goal isn’t to reverse digital progress but to make it cleaner, smarter and more sustainable. Businesses that align their tech strategies with climate goals will not only reduce their environmental footprint but also lower costs, boost resilience and enhance their brand reputation with customers, partners and employees alike.

Small changes, big impact

It’s easy to point fingers at AI and the large-scale data infrastructure driving it, but we must also look closer to home. The hidden carbon cost of business tech isn’t in some distant server farm or futuristic algorithms – it’s on our desks, in our server rooms and in our inboxes.

Organisations of every size have an opportunity, and a responsibility, to take action. And the good news is that many of the solutions are within reach: simple, practical and cost-effective measures that collectively make a significant difference.

We don’t need to wait for breakthrough technologies or new regulations to get started. By taking stock of our digital habits today, we can make smarter choices tomorrow, and move one step closer to the sustainable future we all want to see.

Al Kingsley MBE

Al Kingsley MBE is CEO of NetSupport. With three decades of experience in EdTech and digital safeguarding, Al regularly speaks and podcasts on the use of technology in enhancing education, promoting online safety, and shaping effective digital strategies.

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Signs of progress: The power of collaboration and creativity in breaking barriers

Anders Jessen — Mon, 12 May 2025 16:02:00 +0000

In the last 20 years of digital design, I have seen the technological landscape change beyond recognition. The products that existed in the early 2000s, from the internet to the smartphone to streaming, have seen more iterations and phases than I could have imagined. But I am often interested in the decisions behind these iterations that drive this innovation.

Good user experience (UX) is the typical response. As tools and technologies mature, they seek to improve consumer experiences. But when more than two-thirds of UK adults with visible or invisible disabilities feel excluded from products or services due to accessibility issues, our approach to UX is still far too narrow.

Differently abled people have had to be incredibly resourceful, using screen readers, automatic speech recognition (ASR), chatbots, and countless other extensions to navigate a landscape that wasn’t built with them in mind. It’s impressive, but it shouldn’t be the case.

With AI disrupting so much of what we know to be true, we are at an inflection point. Nothing is set in stone, and we have thousands of new, AI-powered possibilities in front of us. Everything is ripe for innovation.

Rather than simply tinkering with our current tech, we have the power to build new, more inclusive products tailored directly to the users who have missed out on so many of our creations in the past.

Time to reinvent the wheel

I’d like to propose that the new urgent driving force for innovation be accessibility. By consciously catering to marginalised communities and reimagining how to make products for more people, we allow ourselves to break out of the mould to something new and exciting. It will enable us to reinvent experiences meaningfully.

Last year, Canon worked with the Royal National Institute of Blind People on an exhibition. Through sound, touch, and Braille, World Unseen showcased how those with sight loss experience art. In doing so, it created an immersive, unique, and educational experience designed for everyone to enjoy, including sighted attendees.

Technologies built to allow access can open doors for everyone. As subtitling software has grown and improved to deliver real-time, AI-powered access for deaf viewers, this has fed directly into better transcription technology in corporate meetings or allowed us to watch a funny reel even when we’ve left our earphones at home.

A growing opportunity

Technology’s rapid development allows us to do more than simply build an access ramp for disabled communities to join everyone in the joys of digital creativity. By working closely with people with disabilities, we can meaningfully improve their lived experience with solutions, and iterate to ensure they mature along with the underpinning technologies.

During the pandemic, my team and I started experimenting with hand-tracking technology, designing programs that would allow people to order from screens without the need to touch them. In this solution we found another entirely new solution that could help the deaf and and hard-of hearing community who are often underserved by society and technology.

People want to make the world better; they just need to be empowered to do it and to learn how.

Working with the American Society for Deaf Children, we built a platform that used playful animations and hand-tracking to teach the alphabet in American Sign Language (ASL). It was a fascinating technical challenge and won several awards, but mostly, it was a chance to contribute something genuinely useful. Crucially, the technology behind it kept maturing. With the help of NVIDIA, we applied our learnings to a much more complex challenge: Signs, a comprehensive ASL learning platform.

The challenge when building Signs was three-fold. We needed a front-end that could emulate signs to learners and give real-time feedback on their attempts. We would need a dataset, supplied by a community of deaf and hearing sign language users and interpreted through AI technology. Lastly, we needed a movement, inspiring signers to contribute continually so that our program was as dynamic and alive as the language itself.

User experience was critical to the first challenge. The cartoonish illustrations of Fingerspelling.xyz gave way to a sleek avatar that talks to the learner and employs colours and points to keep users engaged and motivated to learn more. Different aspects of Signs appeal to others; we all react to things that look visually pleasing and feel effortless, but the technology still wows me. I make a sign to my laptop screen, and the avatar sees, mimics, corrects, or praises me. It feels like magic.

For this to work, we needed to educate ASL users. People want to make the world better; they just need to be empowered to do it and to learn how. Through GiveaHand.ai, we told the public (signing and not signing) a story: that a few short images could feed into an expanding data set and bring a language to life for thousands of new learners. As AI improves and more signers contribute, so does the platform’s ability to recognise and teach increasingly complex signs.

The results have been incredible. The platform reached over 20 million people at launch, and in the first 14 days, Signs has taught an impressive 50,000 signs.

It takes a village - and a vision

The intersection of technology and accessibility is among the most exciting frontiers for innovation today. Whether through AI-powered tools, immersive sensory experiences, or entirely new ways of engaging with digital content, we can build a world where inclusion is not an afterthought but a driving force.

Samsung’s research estimates that brands ignoring accessibility lose access to a spending power of over £274 billion annually—but they risk losing much more than this. After all, when we rethink accessibility, we discover new and better ways of doing things. It’s good for society and business.

Anders Jessen

Anders Jessen is Founding Partner at Hello Monday/DEPT®

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Building an inclusive AI-driven future for education

Sascha Giese — Mon, 12 May 2025 16:01:00 +0000

Artificial intelligence (AI) is being hailed as the latest technology to put the UK’s public services on a new path to modernisation. While the jury is still out on how quickly this can happen, there is no denying that the public sector in the UK is in need of digital transformation.

Since the announcement of the UK’s AI Opportunities Action Plan at the beginning of this year, there have been a range of organisations and industries set to benefit from enhanced AI investment. It’s become clear that the education sector is no exception, from speeding up marking to an AI assistant to support teachers with workloads. For teachers struggling with workload, AI could improve their working life and change the way children are taught for the better.

However, as the role of AI in classrooms expands, ensuring inclusive, ethical, and safe AI adoption is essential to help prevent any bias, protect student privacy, and therefore create equal learning opportunities for all.

The promise of AI for teachers and students

With educators under more stress than ever, AI has the potential to completely revolutionise the education sector by automating certain tasks such as creating educational resources, lesson and curriculum planning or even completing strenuous admin tasks to help save teachers’ time. For students with disabilities, AI-powered tools can also provide speech-to-text features, real-time translation, and other accessibility features to ensure a more inclusive learning environment.

But some teachers are still dubious.

In a recent article, the National Education Union’s (NEU) general secretary, Daniel Kebede, told Sky News that while there were positive aspects to the rollout of AI, he felt there had “not been any meaningful discussion with the sector yet” and the Department for Education (DfE) was “running away with itself.” Many educators feel in the dark when it comes to how AI will be implemented in their daily work, adding to their concerns.

Addressing concerns and ensuring inclusivity

One of the biggest concerns currently surrounding the implementation of AI in schools is the risk of bias. If AI models are trained on data that reflects any form of societal inequalities, the AI systems could reinforce them.

For example, if a teacher is creating a lesson plan for their class using an AI education tool that generates lesson content, this content could unintentionally favour certain dialects, cultural references, or even historical perspectives, leading to an unequal learning experience for students. It’s important

As AI technology continues to advance, it’s crucial that inclusive AI goes hand in hand with these advancements to ensure it remains a top priority

To support with this, at the beginning of this year, the UK Government introduced its Generative AI: Product Safety Expectations, a framework intended to outline the capabilities and features that generative artificial intelligence (AI) products and systems should meet to be considered safe for users in educational settings.

These guidelines cover everything from exposure to harmful content to issues such as privacy and governance. They will be instrumental in supporting teachers, students and parents in helping ensure AI safety across the board.

Keeping inclusive AI at the forefront

I feel strongly about this approach to AI development, especially because it aligns with many of the themes outlined in AI by Design, which reflects four key principles for developing AI.

AI by Design focuses on building trust through transparency, helping ensure fairness, and embedding security from the ground up. It’s not just about creating powerful AI tools; it’s about creating tools that are built to last, with the safety and privacy of users always in mind.

Both AI by Design and the Generative AI: Product Safety Expectations framework highlight the need to avoid biased AI-generated content, helping ensure students are treated equally regardless of their background. They also emphasise the importance of strong data protection measures in educational AI tools to help ensure privacy and security are also prioritised.

The future of inclusive AI in education

It’s clear that we’ll see the role of AI in education continue to develop, especially as we are now seeing emerging technologies like AI tutors, intelligent assessments and virtual learning assistants that will further personalise education, making learning more accessible and inclusive to more students.

However, as AI technology continues to advance, it’s crucial that inclusive AI goes hand in hand with these advancements to ensure it remains a top priority. Ethical AI practices must be embedded throughout every stage of development, ensuring that future AI tools prioritise diversity, fairness, and accessibility. Schools should also receive the appropriate guidance on implementing AI responsibly, ensuring a balance is struck between innovation and ethical considerations.

The UK’s proactive approach to AI in education has definitely set the precedent globally and I’m sure we will see more countries follow suit.

Sascha Giese

Sasha Giese is Global Tech Evangelist, Observability, at SolarWinds, a leading provider of simple, powerful and secure solutions designed to help organisations accelerate business transformation in today’s hybrid IT world.

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Improving water efficiency for farmers with IoT and generative AI

Dom Couldwell — Mon, 12 May 2025 16:00:00 +0000

According to research, the impact of climate change on drought is undeniable. In the United States, the University of California and the National Oceanic and Atmospheric Administration found that rises in temperature linked to human activity are now more responsible for drought in the country than lack of rainfall. S&P Global has projected that countries like Saudi Arabia, Spain and Turkey would spend more than 45 percent of the 2050s in drought conditions, while Egypt would be in severe or extreme drought conditions for 80 percent of the decade. Drought in countries like Australia, Mexico and Italy would double in that decade as well. In almost 750 extreme weather events and trends analysed by CarbonBrief, 74 percent were made more likely or severe because of climate change.

For farmers, growing crops and meeting food production targets relies heavily on water. Without the right level of water, crops grow more slowly, or can die. Equally, over-watering can affect growing patterns or kill crops too. Getting the right balance in place around how water is used during the life of plants is essential to achieve optimum results. Using precision irrigation and deploying water exactly where it is needed will make a huge difference.

However, these irrigation systems can be costly to install in terms of capital and maintenance. Water itself is a valuable resource that has to be managed effectively as well, and can cost farmers when they have to import water for their systems to use. Over-watering can also waste fertiliser, adding run-off to the local water table and leading to increased chemical costs. These implementations also need power to pump water to where it is needed, adding more overheads for farmers to worry about.

So how else can technology make farming more efficient? And what innovations are possible around Artificial Intelligence, and Generative AI in particular?

Understanding your needs

To start with, we have to understand what is taking place in the physical world. Getting accurate data on precipitation and water levels is essential as a starting point for how hydrated crops are, or where additional water resources are needed. Using data from hydrometers and Internet-connected sensors that can detect water levels over time, it is possible to create a map of a farm and the crops that exist on it.

Alongside this base data on water levels, farmers will also need information on future rainfall and how this might affect crops over time. Getting accurate weather forecasts that can show how much rain may fall in a given period can influence decisions on how to use water over time. After all, it is a waste to use water now when rain may come in the next few days. Equally, when water is needed during specific parts of a crop’s growth season, it may be better to provide more to support growth.

Making decisions about water provision should be based on the combination of real-world water data and predictions on future weather. However, each crop is different – different plants will respond differently to different water availability, so being more precise on how much water to give will depend on recommendations for that specific crop and even for a specific variety of crop. Some plants are hardier than others and can survive longer without water, so this can influence the AI models for when and how to provide water.

Making the right recommendations

Alongside the data from sensors, weather and AI models, there is the real world side to consider too. Providing statistical models and recommendations is one element, but is that what the farmer in the field needs to get?

Using this information alongside Generative AI, it is possible to personalise the recommendations further for specific individuals to use. Generative AI creates responses to user prompts that deliver information to users in new contexts. Getting a recommendation in natural language around what to do and when is better for farmers to take action, rather than relying on sets of data that are more abstract. Responses could also include images or video resources, where this can help deliver information to the farmer in a smarter or more useful way. Generative AI can pull together all this data, personalise it, and then deliver recommendations about how they can make the best use of water in their specific environment at that time.

As an example of data and AI recommendation in action, Israeli company SupPlant built a real-time service for farmers that synthesises crop and weather data to create a proactive management perspective every 30 minutes. The service provides critical early warning to farmers, individualised to their farm, fields, and plants. Based on their combination of AI and data, SupPlant helps farmers save an average of 30 percent on their water usage. This efficiency around water doesn’t have to lead to smaller crop yields either – in fact, personalising data on water usage has helped farmers improve their crop quantity by between five and ten percent year on year.

Water will continue to be an extremely precious resource in farming, and one that grows in importance every year as drought conditions increase. Using AI, weather data and Internet of Things sensor information together for precision irrigation in agriculture can make a huge difference to how farmers make the most of their crops, while also reducing costs. Taking this one step further with Generative AI to personalise the experience to each farmer improves the chances for success even more. With more people on the planet and more pressure on water resources in future, this innovative approach to water management will be essential for future farmers.

Dom Couldwell

With over two decades of experience across a variety of verticals including Financial Services, Healthcare and Retail, Dom Couldwell is currently the Head of Field Engineering, EMEA for DataStax.

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Is AI the key to reliable emissions tracking in a net-zero world?

Jim Hietala — Thu, 17 Apr 2025 11:01:12 +0000

The road to net-zero emissions is paved with data. As governments tighten regulations and businesses face increasing pressure to curb their carbon footprints, the need for precise, reliable emissions tracking has never been greater. But tracking emissions is far from simple—without accurate measurement, progress is impossible. Artificial intelligence (AI) is now stepping in to revolutionize emissions reporting and management.

Why measurement is the first step to management

You can’t manage what you can’t measure. Before organizations can make meaningful emissions reductions, they need to understand the scale of their impact. Without tracking changes in emissions levels, it’s impossible to determine if interventions are effective. Additionally, without accurately measuring impact, any improvement efforts are merely speculative.

This makes emissions tracking one of the defining challenges of the coming decades. The world’s climate response is set to reshape economies and industries—from how we travel and manufacture goods to the way we consume energy. For these changes to be effective, they need to be based on reliable data that reflects the reality of emissions trends.

The challenge of inconsistent emissions data

For businesses, emissions reporting presents two key hurdles: compliance and opportunity. On the compliance side, organizations must adhere to increasingly stringent regulations, proving not only where their emissions stand today but also how they plan to reduce them over time. This requires a deep, data-driven understanding of where emissions originate—whether from direct operations, energy consumption, or supply chains (known as scope 1, 2, and 3 emissions).

Beyond regulation, emissions data also holds financial potential. Initiatives like the U.S. Inflation Reduction Actand others around the globe offer tax incentives for companies that can effectively track and manage carbon sequestration and sustainability projects. The ability to analyze carbon data doesn’t just ensure compliance—it unlocks opportunities for businesses to gain financial benefits from their sustainability efforts.

The problem? Carbon data is often inconsistent, incomplete, or fragmented. Different units of measurement, varying calculation methods, and a lack of standardized frameworks make it difficult for organizations to generate meaningful insights. When carbon data is unreliable, decision-making suffers.

How AI is changing the game

AI is uniquely suited to tackling these challenges. By analyzing vast volumes of data, AI can uncover trends, identify inefficiencies, and highlight areas for emissions reduction.

For example, AI can pinpoint anomalies in emissions reporting, helping companies detect discrepancies or fraudulent claims. It can automate complex carbon accounting processes, ensuring accurate, real-time emissions tracking. And it can integrate disparate data sources—energy usage, transportation emissions and supply chain footprints—into a cohesive picture of an organization’s environmental impact.

AI also plays a crucial role in forecasting, enabling businesses to model different sustainability strategies and predict their impact. By running simulations, organizations can evaluate the most effective paths to reducing emissions while balancing financial and operational considerations.

Is standardization the missing piece?

However, AI is only as effective as the data it works with. Without standardized emissions data, AI models risk generating unreliable results. The issue is particularly pronounced in scope 3 emissions tracking, where companies struggle to align supplier data due to a lack of uniform measurement and reporting methods.

One solution is the adoption of standardized data models, such as the Open Footprint Data Model. These frameworks establish consistency in key data elements—naming conventions, units of measurement, and calculation methods—making emissions data more usable and AI-driven insights more accurate. Standardization reduces the need for manual data conversion, allowing companies to deploy AI solutions faster and with greater confidence.

Real-world impact: AI-powered emissions insights

Consider a multinational corporation aiming to track emissions across its supply chain. Without standardized reporting from suppliers, the company would need to manually align and convert disparate data formats before AI could analyze them. This process is time-consuming and prone to errors.

With a standardized emissions data model in place, AI can instantly process and compare supplier emissions, identifying which suppliers are making real progress in sustainability and which need to improve. This enables businesses to make data-driven decisions about procurement and partnerships, reinforcing accountability across the supply chain.

In another case, a company looking to assess its scope 1 and 2 emissions across multiple business units would face similar challenges. AI can streamline this process—provided the data is structured consistently. Standardization ensures that AI can compare emissions across different segments accurately, identifying high-emission areas and prioritizing interventions where they will have the greatest impact.

The future: AI-driven sustainability

For businesses serious about sustainability, leveraging AI for emissions tracking is no longer optional—it’s a necessity. AI’s ability to process complex data at scale enables organizations to move beyond basic compliance and towards proactive emissions reduction strategies.

But AI alone isn’t enough. Without standardized data models, even the most advanced AI solutions will struggle to provide meaningful insights. That’s why businesses must invest in both AI technology and data standardization initiatives to unlock the full potential of emissions tracking.

The climate crisis demands action, and action starts with understanding. By combining AI’s analytical power with a standardized approach to emissions data, businesses can navigate the complexities of decarbonization with clarity and confidence. The path to net zero may be challenging, but with AI as a guide, organizations can track, manage, and ultimately reduce their emissions more effectively than ever before.

Jim Hietala

Jim Hietala is VP Sustainability and Market Development at The Open Group.

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