Much of this transformation is data-driven – the range and volume of data that exist about users, vehicles, transport options and services offer huge potential to improve mobility, create new business models, and improve the user experience. Given this, it’s no surprise that the sector sees the potential presented by advances in Data Science, AI and machine-learning, and continues to invest heavily in data-driven innovation.

Earlier this year, we attended the techUK Transport AI conference. That event reinforced how bold the sectors vision for AI is, the extent of investment in it, and the potential value that it can bring. Understandably, much of the focus was on exploring the use cases for AI, highlighting the benefits and risks, and on running pilot projects to prove safety and viability. 

A common theme that stood out is that, whilst there are lots of promising pilot projects, there isn’t currently a clear and consistent path for taking grassroots innovation in AI and scaling it up to the regional and national levels necessary to realise its full potential. 

The fact that there isn’t a defined path for scaling AI innovation in the sector isn’t surprising. Regulatory and governance frameworks for AI are still being shaped and, until there is more certainty about what these will mean in practice, there will be caution about scaling beyond a certain level. Public attitudes towards AI are also mixed and can change quickly and unpredictably in response to developments in other industries. Understanding attitudes to AI will be key to judging the best way to scale innovation in a way that creates trust and acceptance.

Working through uncertainties like this isn’t easy, but there are examples of delivering data-driven digital transformation at national scale that can provide a blueprint. One great example of this is our work with the DfT/Defra Joint Air Quality Unit, who we collaborated with to deliver the award-winning Drive in a Clean Air Zone service. There were three key ingredients that made this work a success: (1) we took a user-centred approach to delivery. This involved carrying out face-to-face research with users from all impacted sectors to understand their needs and attitudes and co-design the service, building trust, confidence and buy-in in the process; (2) we established a leadership and governance partnership with representation from central government, local government and the private sector. This helped to drive collaboration, as well as bringing together the diverse experience and perspectives necessary to make the transformation work for everyone; and, (3) we mapped out a ‘scale-up pathway’ that incrementally took the service from research and development prototype, to working proof-of-concept, to controlled pilot, to live national service. Safety, security and readiness criteria were defined and assessed at every stage to control progression through the pathway.

These same principles and frameworks can be applied to scaling AI-based innovation and prove that scaling can be done at pace, but in a safe and controlled way.

In summary, we think that the transport and mobility sector should start work now to develop a blueprint and pathway for scaling grassroots innovation in AI to regional and national level. The blueprint should be based on exemplars and lessons learned from delivering other digital transformation at scale in the sector and be developed in partnership between the public and private sector. By doing this, the future of transport stands the best chance of realising AI’s full potential at pace, whilst ensuring that it is safe, secure, and viable at the same time.

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As the UK strives to meet its ambitious decarbonisation targets, the collective efforts of dedicated leaders from diverse fields are crucial. This year’s Net Zero 50 List highlights innovators from SMEs, the public sector, and other key industries who are driving transformative change towards a sustainable future.

Among the List members are pioneers from SMEs developing AI-driven waste recognition software, regenerative farming practices, advisory and certification services for decarbonisation, and digital platforms for carbon footprint management. From the public sector, notable figures included sustainability leads from the Environment Agency and the Crown Commercial Service, alongside other professionals championing sustainable IT, local economic growth, and energy-efficient initiatives in the British Army. You can view the 50 List members here.

The event featured a series of inspiring speeches from prominent figures such as Dr. Mattie Yeta, Chair of the Net Zero 50 List Advisory Panel and Chief Sustainability Officer at CGI, and Sarah Kemmitt, Secretariat Lead at the Net-Zero Banking Alliance, UN. emphasised the critical role of innovative solutions and cross-sector collaboration in achieving the net zero goals.

For me, Lord Deben our host’s address was particularly impactful, highlighting three key takeaways: Firstly, he emphasised the importance of explaining why net zero matters, urging us to communicate that it’s about reducing our planetary impact to nil and transcending party politics for the common good. Secondly, he encouraged celebrating the benefits of a net zero world, such as a healthier, cleaner, and more equitable future with greater opportunities for prosperity. Lastly, he called for discussions on net zero to be in clear, accessible language that resonates with everyone, making the pathway into these discussions barrier-free.

As we reflect on the individual successes celebrated at the House of Lords, it’s clear that reaching net zero is an enormous challenge requiring collective action and a shift in both individual and organisational behaviours. The foundations for a sustainable future are being laid through ambitious actions, robust policies, and coordinated efforts at national, regional, and local levels. The Digital Leaders community, known for its commitment to tech for good and sustainability, continues to lead the charge by fostering conversations and networks around innovative solutions.

The Net Zero 50 List serves not only as a recognition of exemplary work but also as a catalyst for ongoing efforts. By showcasing the UK’s leading initiatives and individuals, we aim to advance the conversation across sectors and build a network dedicated to achieving net zero. The Net Zero 50 List is a testament to the inspiring work of those dedicated to a sustainable future.

To all those who contributed to this remarkable event and to the ongoing efforts towards net zero, thank you. Your work not only inspires but also paves the way for a greener, more sustainable world.

More information is available on the Net Zero 50 List 2024 website. 

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In short, “Surviving and Thriving in the Age of AI: A Handbook for Digital Leaders” is specifically designed for busy professionals, Digital Leaders and decision makers wanting to know more about AI’s issues and impact. It is organized to allow the reader to gain valuable insights quickly and on demand. Above all, the content is grounded in real-world experience and practical applications within the context of digital transformation, ensuring its relevance to everyone who is embarking on a digital journey. Most importantly, each topic concludes with thought-provoking questions and actionable steps to guide your personal exploration of AI and its implications.

So, what’s it all about? Here’s a summary of the main elements of the book. I’m looking forward to hearing your feedback. Hope you enjoy it!

Surviving and thriving in the age of AI: A handbook for Digital Leaders 

Opinions on AI are everywhere these days. From news headlines warning us how robots will soon be taking our jobs to academic papers describing the latest image analysis advances, the promise and perils of AI are being described and debated. Yet, with the breadth and depth of AI disruption it’s all too easy to feel lost in the technical details, overwhelmed by the barrage of announcements, and confused by the implications for business and society. How can we make sense of it all? What are the key aspects of AI that will determine its impact on our lives? What does it mean for leaders and decision-makers looking to accelerate digital transformation strategies and redefine their organisations to be ready for the age of AI?

I have written this book to address these questions. “Surviving and Thriving in the Age of AI” cuts through the hype and offers a practical roadmap for navigating the exciting (and sometimes confusing) world of AI. It takes the reader on a journey through the principles and practice of AI to provide a handbook for becoming a digital leader in the age of AI.

While it covers many elements, here I’ll unpack the three key themes that form the core of the book: Separating the AI reality from the hype; Assessing AI’s impact on digital transformation; and Delivering AI-at-Scale.

Separating AI reality from hype: How to stay head with AI

Let’s be honest, much of what we see today makes AI sound like a cross between magic and science fiction. The book encourages us to step back from the sensational headlines and presents the core elements you need to develop a critical eye.

• Critical Thinking is Key: Don’t be fooled by marketing speak. Leaders must learn to evaluate AI technologies for themselves to ask better questions and place AI developments into context. A framework for critical thinking is developed throughout the book. Stay informed about advancements, but be wary of overly optimistic predictions.
• Education is Power: All of us must invest in understanding the basics of AI. Digital leaders have a particular responsibility to educate themselves and their teams. This empowers them to see through the hype and identify how AI can specifically benefit their industry. The book highlights where to concentrate your educational efforts.
• Data is King: Don’t base decisions on empty promises. The book emphasizes the importance of data-driven decision making based on a resilient data engineering infrastructure. Leaders gain deeper insights from a mature approach to data and look for real-world case studies to test, evolve, and scale their AI solutions.

Assessing AI’s impact: A key weapon in your digital transformation arsenal

The evolution of AI brings new capabilities and opportunities to digital strategies already underway. The book positions AI as a powerful tool to extend your digital transformation journey. But only if AI is considered in the context of well-governed change management practices that focus on helping people to be more efficient and effective.

• Integration is Key: AI isn’t a standalone solution. It should seamlessly integrate with your existing digital infrastructure to maximize its impact. The book encourages you to think of it as an upgrade, not a complete overhaul.
• Boost Your Capabilities: AI can supercharge your data analysis, customer service, and operational efficiency. The book provides principles and real-world examples to show how companies are leveraging AI to achieve impressive results.
• Embrace the Experiment: Change can be difficult for organisation with established products, systems, and structures. Fostering a culture of innovation is crucial for success with AI. A key emphasis of the book is to encourage experimentation, learn from failures, and use those lessons to inform future efforts.

Delivering AI-at-Scale: Expanding AI across your organisation

Making the decision to bring in new AI tools and systems is just the start. Taking an AI project from pilot program to company-wide success story requires careful planning and substantial investment. The book provides the basis for you to create a roadmap to make this leap:

• Start Small, Win Big: Adopting AI can have major implications across the organization. Don’t try to boil the ocean. Begin with focused pilot programs in controlled environments. This allows you to test the waters, iron out kinks, and understand the potential impact before full-scale implementation. But the book helps you to recognize this is only a first step.
• Build a Strong Foundation: AI will place pressure on existing systems and skills. Ensure you have the technological and organizational infrastructure to support AI at scale. This includes robust data management systems, powerful computing resources, and a skilled workforce.
• Change Management Matters: People are key to successful AI adoption. The book uses case studies to show how leaders can communicate the vision clearly, address any concerns, and provide proper training to ensure everyone is on board.
• Continuous Improvement is the Focus: AI isn’t a “set it and forget it” solution. The book emphasises the importance of continuous monitoring and improvement. Establish feedback mechanisms to track AI performance, learn from outcomes, and refine your AI applications for maximum effectiveness.

Beyond surviving to thriving with AI

All organisations are now looking to AI to reinvigorate their digital strategy. It is by focusing on these three themes that “Surviving and Thriving in the Age of AI” equips leaders and decision-makers with the knowledge and tools they need to navigate the world of AI with confidence. By understanding the true potential of AI, leveraging it to extend your digital transformation efforts, and scaling AI solutions effectively, you can move from surviving to thriving in the age of AI. This is the handbook that Digital Leaders need to help you to make that happen.

If you would like to learn more about the book, get a signed copy and see me in conversation with Macmillan Cancer Support CIO, Roxanne Heaton you can join us on 23rd July at DLHQ 

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We are calling for… [a] Local Government Centre for Digital Technology: using technological innovation to deliver reform and promote inclusive economic growth across councils.

There’s no more detail in the document, and little in the news article about it in UKAuthority either.

Now, I’ve been chatting with Owen Pritchard, who I would guess is the person behind this line, for a few years now, and I don’t doubt that he has his own, very long list of things he thinks the centre should do – so I’m pretty excited to find out in due course what that looks like.

In the meantime though, let me toss around a few ideas… what could this centre do?

1) Coordinate procurement

The first thing for me would be to start investigating how the buying power of the sector could be consolidated to produce economies of scale, and better contracts.

I’d start in the bottom right of a Wardley map, the commoditised digital gubbins that keeps councils running, and where it makes no sense to have that stuff duplicated 300 odd times across the country.

Laptops, phones, broadband connections, hosting services… all these things have councils up and down the country running procurement exercises, negotiating contracts, managing those contracts… all this could be done once nationally, or a few times regionally, with no negative impact on local service delivery.

Once that’s done, how about we move on to using that collective buying power to:

• demand better products from suppliers, particularly in the line of business system market
• consolidate social value across contracts, to create meaningful, large scale opportunities for suppliers to support local public services
• Invest in the development of new products and services, either through existing or new companies, or even local authority trading organisations

This stuff is pretty boring in many ways, but I’m putting it first in this list because I think the actual opportunities are vast.

2) Fill the security gaps

With the cyber assessment framework and the LGA’s own cyber 360 reviews, there is plenty of advice out there in terms of best practice on security. However, many councils are lacking the capacity and the capability to implement this guidance. It’s nobody’s fault, just the result of many small organisations, who through years of austerity have been unable to invest in their technical infrastructure.

This could be sorted by having support available to councils to put in place the measures needed to ensure that data and information is kept as safe as possible from a technical standpoint. Flying squads of security experts who understand the local government environment, are knowledgeable about the frameworks and guidance, and can put in place the necessary steps to make all councils as secure as possible.

3) Education, education, education

One of the most important things that a centre like this could do would be to put a lot of effort into increasing the knowledge of digital across local government. Despite various efforts in recent years, the level of digital confidence within the general workforce in the sector is remarkably low.

We need people in leadership positions to understand what is possible and what they can do to unlock this potential. We need politicians who understand the strategic levers they need to pull to ensure the right long term decisions are being made in councils around technology strategy. We need the specialist teams within local government to be up to speed with the latest developments in cloud, development, security, and data, depending on their role. We need council teams to be way more confident in utilising user-centric service design approaches.

All of this could be advanced really quickly through a properly funded and planned out strategic learning programme across the sector.

The second strand is less about formal training and more about curating existing good practice case studies and examples, and creating ones that don’t exist but really should. Part of the problem of a fragmented sector of over 300 organisations is that it is really hard for anyone to know what is going on everywhere else.

The standard of documenting the good stuff is really poor. Case studies are dominated by vendors, announcing deals and anticipated outcomes, but with no follow up. We have councils going through the process of turning into unitaries, for example, but no documented playbook on how to successfully aggregate the IT in these situations. Why the hell not!? A local government dedicate centre could have a team of researchers and content designers producing useful, findable, actionable content that would help spread the word on how to get things done.

4) Data and standards

Everyone knows about the untapped potential of data within local government, but nobody so far has had the right mix of time, money, and intestinal fortitude to get it done properly.

It means taking on the line of business system providers to open up access to the data; to help navigate the arcane table structures of these creaking software behemoths; to have in place the data platforms to aggregate, transform, and usefully visualise data; to have data engineers and scientists able to formulate the right questions and figure out how to get the answers; and to have service managers who are open and willing to become data informed, and to change a lifetime’s habit of going by hunches and guesswork.

It’s a big ask, and it’s no wonder that progress has been slow. But so many of these problems are shared by every single council in the country. A centre such as the one being propose could come up with a whole host of replicable and scalable answers to these problems.

Alongside this kind of support, there’s also a need for standards around data and a centre could help coordinate and manage standards where they exist, and support the development of them where they are needed. Just as importantly, though, the centre could provide some teeth, ensuring that councils and their suppliers are meeting these standards to enable the safe use of data to improve outcomes for local areas. The use of coordinated, collective buying power would definitely help with this!

Another area of standards where a centre could help would be to produce re-usable data sharing agreements and policy documents, to help councils collaborate with other parts of the local public service system, without the need to reinvent the wheel at significant cost, over and over again.

5) Innovate at scale

Finally, I’d want to see some collective effort at innovating in a coordinated, replicable and scalable way. Pooled resources that can reduce the risk exposure for individual councils, bringing together the best brains and ideas with the people best at delivering results, to experiment, test, iterate and improve on radical ideas for local public services.

It’s far too big a risk for individual councils to take on, and no surprise that transformational change in local government is often so incremental. The exploration of new operating models in the internet era – companies like Uber, AirBnB, Netflix and so on – have been funded by billions in venture capital. But somehow we expect councils do be able to do it, alongside running the existing services, on a shoestring?

Imagine a centre, with enough resources to be able to pull together the best service design folk, the best data people, the best technologists, the subject matter experts for specific services, all able to identify the biggest challenges facing the sector and to innovate their way through to workable solutions that can be adopted across the sector. With this kind of scale and authority, such a centre could have the clout to agitate for legislative reform where it is needed, to call for the establishment of new institutions to deliver specific outcomes, or to work alongside existing council teams to help them adopt the new models.

Read More GovTech

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These days, we seem to mean a lot more than Ward Cunningham did when he first coined the term in 1992. As Cunningham has helpfully clarified, he was specifically referring to coding choices made in the absence of full information – information that could only be gained by releasing a version of the code and learning how it was used. The technical debt incurred in this way could be paid down by refactoring the code as its full feature set became apparent – or could be ignored, at the risk that, just as with financial debt, servicing the debt would become all-consuming: developers would spend all their time navigating an ever more complex code base, full of incrementally added and contradictory concepts.

The idea of technical debt is powerful, and intuitively appealing to anyone who has developed software, or managed or sponsored a software development project. It is so appealing that we have used it in many more adjacent contexts. I have seen the term ‘technical debt’ applied to:

• Poor coding choices made with the intention of tidying up later (the practice which prompted Cunningham’s helpful clarification).
• Systems where hardware and software upgrades have routinely been ignored.
• Systems which have become regarded as ‘legacy’ (whatever that means), and which everybody would like to get rid of.
• Choices made which contravene architectural standards (whether those standards are useful or not).
• ‘Shadow IT’ systems which have been developed in the absence of professional software practices, and brought into the scope of the technology team.

I don’t believe that this widespread use of the term ‘technical debt’ is bad in itself. The core concept – that we sometimes have to make compromises that we must pay for later – is sound, broadly applicable and has explanatory power. However, I also believe that casual misuse of the term is dangerous, and leads us to excuse technical debt or accept its persistence.

Credit, the financial concept on which technical debt is based, is a useful tool which makes our economy possible. It enables us to move value across time and space. It enables me to buy something today which I will not be able to afford until tomorrow.

But it is easy to let the habit of spending on credit get out of control, especially when you can’t see the balance and you don’t know the interest rate. Once the concept of technical debt as a conscious choice exists, it is easy to incur debt for bad reasons: the project manager only cares about this release, so we’ll test minimally and manually; the sponsor really likes this particular technology product and, well, they’re the boss; the release schedule is full this year, so we’ll push the upgrades to next year. Putting the consequences on the technical credit card becomes a habit.

Furthermore, just as most companies usually require some form of borrowing, to invest, or grow, or acquire, we can easily fall into the trap of thinking that technical debt will always be with us. Our systems will always be a bit out of date; our architecture will always be a bit of a mess; our code base will always be a bit difficult to understand. Paying the interest on this persistent debt becomes part of what we do, until it consumes or overwhelms us.

The form of technical debt that Cunningham originally wrote about was not an excuse and was not intended to be persistent: you cannot know everything about the desired behaviour of your system until you make a release, and the experience of that release will teach you that you would have done things differently if you’d had all the information. Small amounts of technical debt, paid down quickly, are an inevitable part of software development. But this is not how most organisations think about or manage their technical debt.

Perhaps one way to help us stay in control of technical debt is to ask ourselves: who are we borrowing from? In one sense, just as with all credit, we are borrowing from our future selves. But this doesn’t fit the metaphor: with true debt, there is always a lender, and it is that lender who makes us pay. I think that when we incur technical debt, we are borrowing from the systems we are building and the architectures we create. And, if we are not careful, they certainly make us pay.

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As user researchers, we support stakeholders by delving into complexity and distilling it into concise, actionable meaning. It does not matter how thorough or extensive the research is – if stakeholders do not walk away with clear takeouts in their mind, then the work will never have a significant impact.

The key to effectively communicating research is through providing insight. In this article we will look at what is meant by “insight” and the steps we can take to deliver it.

Information versus insight

“Information” includes data, user feedback, facts, quotes, and observations. It is the foundational starting point for having the evidence to make informed decisions.

However, where information falls short is that the implications are not always apparent. Stakeholders can receive a very informative report and yet still be left with the question, “so what does this all mean?”.

To answer the “so what?” question, we need to go a step further than providing just information – we need to provide the insight.

Insights are the “aha!” moments. They are statements which can inspire and change the way we think about something, delivering clear and impactful “nuggets” of understanding. They are also routed in a deep knowledge of the business problem and context.

Insight is about taking information, interpreting the implication for the project/organisation, and providing a direction for what should be done off the back of what we now know.

Information

To illustrate, here are some examples of information:

• 70% of users were satisfied with the website.
• Healthcare professionals were least satisfied with the website.
• The majority of users are healthcare professionals.
• There are several pain points with the guidance on the website.

Insight

If we were to draw out the insight from this information, we might say that:

If our goal is to improve overall satisfaction with the website, we should prioritise addressing the needs of healthcare professionals, as they are both the largest user group and the least satisfied. The starting point could be to address their pain points with using guidance.

Six ways of transforming information into insight

1. Understand The Stakeholders: To provide insights that resonate with stakeholders, you need a solid understanding of their perspective and the context within which they operate. This means being aware of the challenges they face, their priorities, their constraints, and the decisions they need to make. Doing this ensures that research findings are relevant and tailored to support them in their role.
2. Understand The Objectives: Research recommendations should be aligned with workstreams across the business. Otherwise, your insights could cause confusion by recommending steps, which conflict with business goals. It is important researchers consider the desired policy outcomes, the delivery programme, and the strategic objectives. Taking this holistic approach means that your research findings can be easily joined up and applied across the business.
3. Develop Hypotheses: A good way of making research findings insightful is through attaching it to a hypothesis. It means that when you complete your research, you end up with clear answers to your questions and can tangibly see what has been learned. For example, instead of a detailed description of the extent to which users find a system easy to use, consider framing a response to the hypothesis: “Users find the system easy to use”. Creating hypotheses in collaboration with stakeholders also helps everyone feel invested in the outcomes of the research.
4. Know the Users in Their Context: Users are not static data points – they are real people! You should always think about how you can apply the information you hold to what this means in relation to the wider environment. You can do this by supplementing your research with contextual evidence, such as literature on cultural trends, socio-economic changes, emerging technologies, and behavioural psychology. You can also understand context through benchmarking your service against relevant comparators.
5. Make Connections: Knowledge management is a difficult task for all large organisations but is very beneficial when done well. An insightful piece of research will look broadly at other relevant evidence, make the connections, and then develop a narrative for how the understanding of the user has developed. For example, “we already knew X, then our research uncovered Y, and now there is an opportunity to explore Z, as this has yet to be investigated and would deliver a lot of value.”
6. Finally, Be Curious! The primary role of a user researcher is to bring the users’ voice into conversations. Sometimes this means having the courage to challenge the status quo, pose questions to stakeholders, and suggest different ways of doing things. It is not about always coming up with great new ideas. Rather, it is about using research to be thought-provoking, introduce a fresh perspective and stimulate conversation.

In a world where we have more data than ever before, it’s not just about what we know, but how we use it. By considering and applying the above points, we can ensure that user research delivers valuable insight and leads to the delivery of relevant and viable user-centred services.

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Technology and sustainability were among the top 10 priorities of CEOs in 2023, according to Gartner. Business leaders say they view both as important drivers of growth and innovation, and increasingly they’re using technology to achieve their sustainability goals.

Cloud technology has an important role to play here, and has the potential to address, mitigate, and  help solve some of the biggest sustainability challenges. The scope of the cloud’s role in sustainability is highlighted in a recent series of AWS Institute masterclasses.

If companies start out on the right trajectory with their cloud deployment plan, they can accelerate their sustainability improvements and progressively achieve four gains:

1. More sustainable IT delivery
2. More sustainable business operations
3. Creation and delivery of more sustainable business models, services, and products
4. Thorough organizational transformation, with sustainability at the core of their operation

More energy-efficient IT delivery

AWS data centers are 3.6 times more energy efficient than a typical US enterprise data center and up to five times more energy efficient than the average in Europe.

So, when customers deploy technology using AWS Cloud infrastructure, they not only harness benefits but also pass them on to their customers in the form of measurable supply-chain sustainability improvements.

Amazon overall has committed to reaching net-zero carbon by 2040, 10 years ahead of the Paris climate agreement. Net-zero carbon is when you take the same amount of CO₂ from the atmosphere as you put in.

We lead this drive with the following aims:

• Match all of our electricity used in  our operations with 100 percent renewable energy  by 2025, five years ahead of our original 2030 target.
• Enable more than 400 renewable energy projects to generate enough renewable electricity to power the equivalent of 20.8 million European homes.
• AWS will become water positive by 2030, returning more water to communities than we use in our direct operations.

And Amazon has been able to decouple business growth from CO₂ emissions, as I explain in IT operations and efficiencies, the second part of the AWS Institute Sustainability Masterclass series. In 2022, for the first time, Amazon reduced CO2 emissions by 0.4 percent even though growth was 9 percent year-over-year.

Digital asset monitoring helps cut carbon footprint

Most companies’ carbon footprints are not generated predominantly by IT operations but through their wider use of resources. The cloud can help target improvements here, too—for example, by creating efficiencies through digital asset monitoring.

When a Coca-Cola bottling plant in Turkey created a digital twin (virtual representation) of the facility, it could model its entire bottle-washing process and then simulate and compare different settings. Once it implemented optimal settings at the physical plant, the company saw a 20 percent annual energy savings and a 9 percent reduction in water consumption. It’s also saved an estimated 34 days in processing time annually.

The cloud can help companies pinpoint and drive all kinds of new sustainability-related efficiencies through the capture and analysis of data from digital sensors, the use of machine learning (ML), and the implementation of efficient building management, to name a few.

The AWS Sustainability Insights Framework further allows companies to analyze data from across their various resource-management systems, utility data, and more, so they can devise new targets and include findings in corporate sustainability reports.

Innovation

Achieving sustainability offers an economic opportunity worth $12 trillion by 2030, according to McKinsey. The potential starts with imagining the future—because much of the innovations that the world needs to transition to a net-zero economy from 2030 to 2050 do not yet exist. We must then work to overcome the constraints that businesses face today.

These constraints are a powerful focus for innovation. There are significant opportunities to drive innovation through the reuse of existing resources in a circular economy.

Several companies are working on recycling programs for batteries. Other innovative projects use geospatial data from satellites to optimize vegetation, water flow, biodiversity, and soil health across regions.

Transformation

Beyond targeted innovation, modern cloud technology presents opportunities for organizations to reinvent themselves with sustainability at the core of their operation. Arup, an engineering group, promotes environmental regeneration, biodiversity, and conservation of resources in its projects to achieve “more sustainable development in the built environment.”

Research suggests that $44 trillion of annual economic output depends on the natural environment, such as clean air and water, pollination, and forest cover. And while companies typically do not directly pay for them, they cannot be taken for granted, so organizations need  to consider the materiality of biodiversity in their operations.

Islands make good laboratories for innovation, and the Naxos Smart Island project in Greece targets not just a more sustainable  environment but also smart  solutions linked to mobility, primary healthcare, and the transport of goods. The project—backed by the Greek government, local authorities, and the U.S. Embassy—will upgrade existing infrastructure, such as the local marina, energy grid, and water management systems to support smart infrastructure management.

Progress toward greater sustainability, aided by the cloud, begins with understanding an organization’s starting point and its scope for improvement. The potential for transformation is extensive once companies have set their course and are able to measure and interpret existing sustainability data, identify ways to optimize everyday operations, and introduce new innovation, business models, and mission statements.

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Today, UK Biobank has about 10,000 variables per volunteer, from simple lifestyle information to physical measures, electronic health records (EHRs), genetic sequencing, biomarker data, and full body scan images. But to reach its goal of a safe and accessible database, UK Biobank had to overcome the challenge of how best to accommodate all of the data in a way that met researchers’ needs. With the inclusion of whole-genome sequencing data for all 500,000 participants, the sheer size of the data (currently around 30 petabytes) meant they had to find a way for researchers to analyze the data where they were situated, instead of downloading huge amounts.

UK Biobank needed a purpose-built data platform with compute and data-storage capabilities that provided analysis tools in a centralized environment and the flexibility to manage increasing quantities of data, allowing researchers to work on the dataset with ease. This led to the establishment and launch in 2021 of the secure, cloud-based UK Biobank Research Analysis Platform (RAP), which is hosted on Amazon Web Services (AWS) in the Europe (London) Region and enabled by DNAnexus. This post highlights UK Biobank’s journey to becoming a globally accessible dataset for health researchers.

Health data for the public interest

The altruism of research participants is at the heart of UK Biobank’s existence. The dataset’s founders and core funders champion contributors’ generosity by making the data available to researchers worldwide, thereby maximizing its benefits as an enabler for new drug discovery, diagnostics, and treatments worldwide.

All the data is de-identified and available to approved researchers for health-related research that is in the public interest. Since the database opened in 2012, more than 30,000 researchers from 90 countries have registered to use UK Biobank. So far, there have been more than 10,000 scientific publications based on researchers’ discoveries using UK Biobank data.

These include discoveries about conditions including cancers, heart disease, chronic kidney disease, stroke, type 2 diabetes, and Alzheimer’s disease. For example, a PhD student in Boston, Massachusetts, took UK Biobank’s genotyping data (around 800,000 markers across the genome) to establish the value of polygenic risk scores (a measure of a person’s disease risk due to their genes). This kind of analysis could support earlier and more targeted interventions for heart disease or aggressive forms of cancer, for instance.

New findings continue to come thick and fast—there were more than 3,000 published reports in 2023. Each enhancement to the data adds to its potential for other scientists.

Continuous, collaborative innovation

UK Biobank is exploring the possibility of adopting new technologies, such as generative artificial intelligence (AI), to make its database even more accessible and digestible to researchers. Initially, generative AI algorithms may simplify and accelerate interrogating the database, for instance, through direct questions such as “How many people in UK Biobank have had a heart attack under the age of 65?” This may progress to predictive analysis, for example, “Given the cholesterol level of men over the age of 65 with obesity, what will their projected cholesterol level be in five years?”

UK Biobank hopes to see the development of complementary biobanks in more countries, as these are essential for capturing detail about disease progression in diverse demographics and environments. The dataset’s leadership team continue to provide advice to scientists on how to set up similar studies and look forward to seeing the continuing, transformative results the UK Biobank RAP has for diagnoses, treatments, and cures around the world.

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However, I think that some of the more interesting stories are those which use the concept of relativity and the weird effects it produces. By allowing their fictional vessels to accelerate to near, but not beyond, the speed of light, they create stories in which time moves differently for different characters. In such stories, a character who travels a lot may live a normal human lifetime while millennia pass on the places they visit, and events ripple across the galaxy in lightspeed jumps.

(I realise that I’ve assumed that everyone reading this is familiar with the concept of relativity. Just in case you’re not, the TL;DR version is that, as object A approaches the speed of light relative to object B, then the rate at which time passes for A slows down from B’s perspective but not from A’s perspective, meaning that A could undertake a journey which takes weeks for them, but centuries for B. Many, many people have explained this much, much better than me: a quick Internet search will tell you more. And if you’re genuinely encountering these concepts for the first time, I envy you; you’re in for a fun ride!)

Grasping relativity takes an act of imagination, and crafting stories which follow the rules of relativity takes further acts of imagination. I believe that we need to exercise similar imagination when putting technology to work in our enterprises – and that we can experience our own time-dilation effects.

A science fiction story which ignores relativity often draws from a familiar mode of travel: spaceships are like ocean-going ships. They may take a while to reach their destination, but their journeys take place within human scale spans of time. Such spaceships have bridges, and captains, and other roles which resemble those of a normal ship (you may be thinking of a familiar television series).

Similarly, when we put technology to work in our enterprises, it can be tempting to treat computers as machines which behave like humans, and to organise our systems as if they are subject to the same constraints as humans. Some attempts at business process redesign fall prey to this temptation: I know that my process is inefficient; I am going to invest a large amount of money digitising this process; therefore, my money will be better spent if I redesign the process and remove the inefficiencies.

This is an understandable chain of reasoning, and I have followed it myself, many times, in many projects. However, it is often a mistake and fails to recognise the effects of computing time compression. Why is my process inefficient? If it is because it allows errors, and handles those errors badly, then I probably need to redesign it. But if it is because it has five steps rather than two, why do I care? When I implement the process as code, I will compress the time taken for those five steps from one hour each to a few milliseconds each. Relative to the computer, time stands still: I can take as many steps as I want.

For people who build software, this time dilation effect is particularly apparent when we build automated tests. I remember being shocked when I read a programming guide which said that, when building tests, it was okay to ignore some (but not all!) of the good coding practices that applied to my main code. I could cut and paste and I could repeat myself. The goal was not elegance, but coverage. As a result, some of my test code is not organised in a way that would make any sense to a human manually executing a test script. But no human could ever execute that test script manually: even a simple test script, executed frequently, can represent a level of manual effort that would exceed a human lifetime. The effects of inefficiencies in the script are negated by extreme time dilation.

I am not arguing for bad processes and inefficient code. But I am proposing that, when designing computer systems, and particularly when explaining those systems to people who don’t have a technical background, we emulate those science fiction authors who embrace the constraints of relativity. We should remember that, relative to computers, our world is at a virtual standstill, and that work which saves hours in our world may only save fractions of a second in a computer world. We’ve built a lightspeed engine: let’s use it.

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