Since the origins of the quest for artificial intelligence (AI), there has been a debate about what is unique to human intelligence and behaviour and what can be meaningfully replicated by technology. In this article we discuss these arguments and the ramifications of 'ignorance' as it is expressed by current AI models. To what Extent can Artificial Intelligence Match or Surpass Human Intelligence? This article approaches the question of artificial intelligence by posing philosophical questions about the current limitations in AI capabilities and whether they could have significant consequences if we empower those agents with too much responsibility. Two recent podcast series provide useful and comparative insights into both the current progress towards Artificial General Intelligence (AGI) and the important role of ignorance in our own cognitive abilities. The first is Season 3 of 'Google DeepMind: The Podcast”, presented by Hannah Fry, which describes the current state of art in AI. The second is Season 2 of the BBC's 'The Long History of… Ignorance' presented by Rory Stewart, which explores our own philosophical relationship with ignorance. A Celebration of Ignorance Rory Stuart’s podcast is a fascinating exploration of the value that we gain from ignorance. It is based on the thesis that ignorance is not just the absence of intelligence. It feeds humility and is essential to the most creative endeavours that humans have achieved. To ignore ignorance, is to put complex human systems, such as government and society, into peril. The key question we pose is whether or not current AI appreciates its ignorance. That is, can it recognise that it doesn’t know everything. Can AI embrace, respect and correctly recognise its own ignorance: meaning it doesn’t just learn through hindsight but becomes wiser; and is fundamentally influenced, when it makes decisions and offers conclusions, that it is doing so from a position of ignorance. The Rumsfeldian Trinity of Knowns The late Donald Rumsfeld is most popularly remembered for his theory of knowns. He described that there are the things we know we know; things we known we don’t know; and things we don’t know we don’t know. Stewart makes multiple references to this in his podcast. At the time that Rumsfeld made the statement it was widely reported as a blunder—as a statement of the blindingly obvious. Since then, the trinity of knowns has entered the discourse of a variety of fields and is widely quoted and used in epistemological systems and enquiries. Let us take each in turn, and consider how AI treats or understands these statements. Understanding our 'known knowns' is relatively easy. We would suggest that current AI is better than any of us at knowing what it knows We also put forward that 'known unknowns' should be pretty straightforward for AI. If you ask a human a question, and they don't know the answer, it is easy to report this an an unknown. In fact, young children deal with this task without issue. AI should also be able to handle this concept. Both human and artificial intelligence will sometimes make things up when the facts to support an answer aren’t known, but that should not be an insurmountable problem to solve. As Rumsfeld was trying to convey, it is the final category of 'unknown unknowns' that tends to pose a threat. These are missing facts that you cannot easily deduce as missing. This includes situations where you have no reason to believe that 'something' (in Rumsfeld's case, a threat) might exist. It is an area of huge misunderstandings in human logic and reasoning; such as accepting that the world is flat because nobody has yet considered that it might be spherical. It is expecting Isaac Newton to understand the concept of particle physics and the existence of the Higgs boson when he theorises about gravity. Or following one course of action because there was no reason to believe that there might be another available: all evidence in my known universe points to Plan A, so Plan A must be the only viable option. In experiments with ChatGPT, there is good reason to believe that it can be humble; that it recognises it doesn’t know everything. But the models seem far more focused on coping with 'known unknowns' than recognising the existence of 'unknown unknowns'. When asked how it handles unknown unknowns, it explained that it would ask clarifying questions or acknowledge when something is beyond its knowledge. These appear to be techniques for dealing with known unknowns and not unknown unknowns. The More we Learn, the More we Understand How Much we Don’t Know Through early life, in our progression from childhood to adulthood, we are taught that the more you know and understand, the more successful you will be. Not knowing a fact or principle was not something to be proud of, and should be addressed by learning the missing knowledge and followed by learning even more to avoid failure in the future. In education we are encouraged to value knowledge more than anything else. But as we get older, we learn with hindsight from the mistakes we have made from ill-informed decisions. In the process, we become more conscious of how little we actually know. If AI in its current form does not appreciate or respect this fundamental concept of ignorance, then we should ask what flaws might exist in its decision-making and reasoning? The Peril of Hubris To feel that we can understand all aspects of a complex system is hubris. Rory Stewart touches on this from his experience in government. It is a fallacy to believe that we should be able to solve really difficult systemic problems just by understanding more detail and storing more facts about the characteristics of society. As Stewart notes, this leads to brittle, deterministic solutions based on the known facts with only a measure of tolerance for the 'known unknowns'. Their vulnerability to the 'law of unintended consequences' is proven repeatedly when the solution is found fundamentally flawed because of facts that were never, and probably could never be, anticipated. These unknown unknowns might be known elsewhere, but remain out of sight to the person making the decision. Some unknown unknowns might be revealed, by speaking to the right experts or with the right lines of enquiry. However, many things are universally unknown at any moment in time. There are laws of physics today that were unknown unknowns to scientists only few decades previously. The Basis of True Creativity Stewart dedicates an entire episode to ignorance’s contribution to creativity, bringing in the views and testaments of great artists of our time, like Antony Gormley. If creativity is more than the incremental improvement of what has existed before, how can it be possible without being mindful of the expanse of everything you don’t know? This is not a new theory. If you search for “the contribution that ignorance makes to human thinking and creativity” you will find numerous sources that discuss it, with references ranging from Buddhism to Charles Dickens. Stewart describes Gormley’s process of trying to empty his mind of everything in order to set the conditions for creativity. Creativity is vital to more than creating works of art. It is an essential part of complex decision-making. We use metaphors like 'brainstorming or blue sky thinking' to describe the state of opening your mind and not being constrained by bias, preconception or past experience. This is useful, not just to come up with new solutions, but also to 'war game' previously unforeseen scenarios that might present hazards to those solutions. What would you Entrust to a Super-Genius? So, if respecting and appreciating our undefined and unbounded ignorance is vital to making good and responsible decisions as humans, where does this leave AI? Is AI currently able to learn from hindsight – not just learn the corrected fact, but learn from the very act of being wrong? In turn, from this learning, can it be more conscious of its shortcomings when considering things with foresight? Or are we creating an arrogant super-genius unscarred by its mistakes of the past and unable to think outside the box? How will this hubris affect the advice it offers and the decisions it takes? What if we lived in a village where the candidates for leader were a wise, humble elder and a know-it-all? The wise elder had experienced many different situations, including war, famine, joy and happiness; they have improvised solutions to problems that they have faced in the past, and have learnt in the process that a closed mind stifles creativity; they knew the mistakes they had made, and therefore knew their eternal limitations. The village 'genius' was young and highly educated, having been to the finest university in the land. They knew everything ever written in a book, and they were not conscious of making a bad decision. Who would you vote for to be your leader? Conclusion The concepts described here are almost certainly being dealt with by teams at Google DeepMind and the other AI companies. They shouldn’t be insurmountable. The current models may have a degree of caution built into them to damp the more extreme enthusiasm. But I’d argue that caution when making decisions based on what you know is not the same as creatively exploring the 'what if' scenarios in the vast expanse of what you don’t know. We should be cautious of the advice we take from these models and what we empower them to do—until we are satisfied that they are wise and creative as well as intelligent. Some tasks don’t require wisdom or creativity, and we can and should exploit the benefits that these technologies bring in this context. But does it take both qualities to decide which ones do? We leave you with that little circular conundrum to ponder.

The Urgency for Efficiency in Local Government The financial challenges facing Local Governments in the UK over the past few years have been impossible to ignore. In 2023 alone, Birmingham City, Nottingham City, and Woking Borough councils were all reported ‘bankrupt’. Clearly, the realities of growing and aging populations, increasing poverty, and strained funding are putting greater pressures than previously realised. Specifically, this is challenging social care, and housing and accommodation, which are both suffering from an increased need in funding which is not available. At the recent ‘Future of Britain: Governing in the Age of AI’ conference (July 2024), organised by the Tony Blair Institute for Global Change, speakers suggested that the only opportunity presenting itself currently is the recent steps forward in Artificial Intelligence (AI), specifically Generative AI and Large Language Models. Needless to say, it will require more than poems on ChatGPT or images on Midjourney to drive improvements in local services provisions. However, in the last year we have seen an AI development that shows promise, albeit with translation into reliable operations with secure environments. This new development is being referred to as Agentic AI, or multi-AI agent teams. But what does this new technology offer for Local Governments? What is Agentic AI? Agentic AI represents a shift from traditional centralised AI models to a distributed system comprising multiple specialised Agents working collaboratively. This approach allows for the division and specialisation of tasks among trained AI agents, which can efficiently solve complex problems by leveraging the strengths of each individual Agent within their specialised domain. Agentic AI offers several distinct advantages over a traditional Large Language Models (LLMs), which are particularly relevant for environments where accuracy, transparency and security are paramount. Imagine you are a council leader, with the power to bring the best people, with the best knowledge and information at hand, into a room to solve every problem statement that you are currently facing. Now, imagine that you can quickly create AI Agents with that same knowledge and information at hand, and the ability to effectively collaborate to solve those problems. It probably sounds farfetched, and yet there are already examples of this technology working effectively in secure organisations within the UK. In this article, we explore the implications of Agentic AI for Local Government spending, procurement, delivery, and HR functions. Budgeting & Spend Management: Enhancing Precision & Reducing Costs What have you got planned over the next few years? What do you have to do vs what do you want to do? What variables play into those decisions? These questions may cover capital projects, provision of housing, technology products, or services reform—such as social care, operations, pensions, and more. Imagine this use case: you are able to do a budgetary cost estimate of everything in minutes, with multiple scenarios and risk analysis for each to a degree of confidence in the execution of the project or service within the price given, as well as proactive recommended interventions to de-risk. This can all be done with Agentic AI, which has already delivered time savings in central government by a factor in excess of 100x, with massive cost decreases too. This technology can provide completely calculated cost estimated and full referenceability in less than half an hour. This doesn’t work entirely by magic. It can be preconfigured to apply your estimate methodologies and local policies and understand what has been done before, but it learns over time, and will continue to verify from other sources, including talking to your employees. However, you would be amazed at the results observed in only weeks. Also, ask yourself this question: How do you find the most accurate budget estimate? Is it better to have a team follow a process to get one answer over time, or to apply a distribution curve to 100-1000 automatically generated estimates for multiple scenarios to determine what is statistically most likely? Agentic AI will give you a customisable set of accurate estimates, with as many parameters as you require, in a fraction of the time and cost. We help you build an Agentic AI team configured to support your project managers, service managers, and operational leaders in everything that they do. This can include accelerating onboarding, gaining excess to deep expertise, making informed recommendations, and working in conjunction with your teams. People have long worried about AI replacing humans, but what if it could be harnessed effectively to help superpower your teams? Agentic AI is a paradigm shift in budget planning and prioritisation, as well as reducing the risks of delay and cost slippage through provision of reliable budgetary estimates for everything Local Governments want to execute. Procurement: Accelerating Processes and Reducing Acquisition Costs Agentic AI can also be harnessed to improve the entire set of processes in the procurement cycle, with a focus on reducing risk and reducing elapsed time to next-step outcomes. There are already established Generative AI solutions that write bid responses, and soon they are likely to generate requirements documents such as ITTs, RFPs, and even contracts. There are AI solutions that enable global search for any widget in any geography, producing Gartner-style sophisticated reports, in hours, on recommended options—enabling procurement teams to source suppliers far more quickly. In addition, Agentic AI will provide effective decision-making solutions that assist with the review of responses to determine risks, costs, and gaps. There are now two approaches to accelerating the procurement process. The first is traditional, mapping out the end-to-end process, determining the areas of delay or pain, and focussing on improving or automating those elements. The second is more novel, and perhaps completely new with Agentic AI: if we can identify the capabilities, tools, and knowledge that are needed in that end-to-end process, then your team of AI Agents can be trained to determine approaches to accelerate these outcomes in your organisation. In truth, there is a strong argument to try both where possible. Delivery: Streamlining PMO Functions & Managing Risks Estimating costs faster is one essential function, but the challenge is also to ensure that these services, projects, or operational needs, are still being delivered for the cost envisaged. Agentic AI can also be applied to act as an enhanced Project Management Office (PMO) function by taking progress input from a variety of sources, interpreting against all that is known, and making proactive intervention recommendations to help keep the team on track. Imagine this use case: an Agent Team that has specific agents focused on aggregating data, perhaps supplied from existing Excel reports or through interfaces to the financial systems; some agents are specialised at determining and evaluating risks, while others are trained to have a deep understanding of the contract terms, operating model, resourcing, or anything that can be provided as a set of data or interface. There are, of course, numerous regulations (GDPR as a minimum), policies, and ethical AI frameworks that must be adhered to, but we have already seen robust solutions designed for highly secure environments. That being said, do not compromise here: it is critical that organisational data is protected from a security perspective, requiring a full transparent, auditable solution. Agentic AI in HR & Finance: Driving Productivity Improvement In a wider context, Agentic AI can impact the entire Operating Model of a local authority or council, improving productivity and enabling existing teams to achieve more, and faster, through the assistance of AI Team Members. There are numerous use cases for these applications across HR, campaign recruitment, performance appraisals, apprenticeships, and more. This technology is also beginning to ask questions of regulations; for example, for many years we have pushed job descriptions through tools that ensure gender neutrality, yet if we can easily create and promote a multiplicity of job descriptions and adverts that are targeted on broadly diverse groups, then there may be a more effective engagement across these demographics. We are also seeing Agentic AI applied to finance functions, bringing a meld of machine learning tools with Generative AI to help automate process flows such as invoice processing, forecasting, accounting, financial reporting, and auditing. Summary: Harnessing Agentic AI for Local Government Transformation If your perspective on Generative AI is driven by playing with ChatGPT or Dall-E, and you have dismissed it as being irrelevant to your work in Local Government, then my plea is to look further. If you have worried about hallucination, or the security/privacy issues of applying it to the public sector, or the impact it may have on jobs, then look at the emergence of Agentic AI as helping to resolve some of these genuine concerns. Regarding the impact on jobs, though it is undoubtedly true that the employment landscape is constantly evolving, there are some wider, incontrovertible megatrends that are making it increasingly difficult to recruit the necessary people to deliver the required services—for example, aging populations, or shrinking populations (in some geographies). As a strong voice in the world’s CDR (Digital Responsibility) movement, I have been talking about the necessity to think of these consequential impacts for nearly a decade. Yet, I have seen the reaction to public sector employees finding themselves better able to perform the actions required for their departments or citizens without the reliance on consultants in the supply chain. Think of Agentic AI as enabling you to do far more with your existing teams; to onboard new employees faster; and to condense elapsed times to respond to requests or deliver services. Think of it as a way of making your employees’ lives easier, by providing them with the information to help make their decisions, or complete activities faster. It is true that there are risks and dangers regarding AI, but these can be understood and mitigated in the context of specific use cases. Let its innovative potential drive your engagement with it, over fear of the unknown. In an environment in which taxation is unlikely to significantly increase to provide greater funding and the costs of delivering public services continues to increase, we must find some transformative ways to keep going. Agentic AI presents this opportunity, we just need to understand how to harness it most effectively in harmony with human teams who need that help. In short, Agentic AI can be instrumental in future-proofing your operations and delivering better public services for less cost. Agentic AI from Futuria Combined with Cambridge MC’s Public Sector Expertise Cambridge Management Consulting and Futuria have formed a strategic partnership to offer Agentic AI solutions tailored to the needs of UK local authorities. This collaboration brings together Cambridge MC’s extensive expertise in public sector transformation and Futuria’s cutting-edge AI technology, creating a powerful proposition for councils facing budgetary constraints and operational challenges. Craig Cheney, Managing Partner for the Public Sector at Cambridge Management Consulting, highlights the potential impact of this collaboration: "Our partnership with Futuria presents a transformative opportunity for local authorities across the UK. By combining our deep expertise in public sector transformation with Futuria's advanced Agentic AI technology, we are empowering councils to navigate their financial challenges while improving service delivery. This is not just about cost-cutting; it's about enabling local governments to do more with less—delivering better outcomes for their communities in a sustainable way." Cambridge MC has a long-standing commitment to supporting the public sector through economic challenges. With decades of experience working with councils and educational institutions, Cambridge MC has helped organisations save over £2 billion through cost reduction initiatives and business transformation. This expertise is now amplified by the integration of Futuria’s Agentic AI solutions, offering local governments a powerful toolset to future-proof their operations and superpower their leadership and teams. About Rob Price Rob is a co-founder of Futuria, an Agentic AI company enhancing organisational productivity with multi-agent teams. He hosts the Futurise podcast, interviewing CEOs and AI business founders about the start-up and scale-up world of AI and Generative AI in the UK, Europe and US. Rob has held various senior leadership roles, from Sales Director to CDO, COO, and Deputy CEO at Worldline UK&CEE, demonstrating strategic thinking, problem-solving, and effective execution. Link to Podcast on Spotify Rob co-founded the Corporate Digital Responsibility movement and helped launch the International CDR Manifesto in October 2021. He manages corporatedigitalresponsibility.net and hosts the 'A New Digital Responsibility' podcast, now in its fifth season. A frequent speaker at European events, he is also a trustee of Inspire+, a charity promoting healthy lives for primary school children. About Futuria At Futuria, we’re passionate about reshaping the future of enterprise operations with our advanced AI Agent Teams and pioneering Agentic AI solutions. Our mission is to empower businesses by integrating modular, explainable, and responsible AI that fits seamlessly into complex environments. By enhancing human expertise, we help organisations gain full control, transparency, and scalability—delivering impactful solutions that drive efficiency, reduce costs, improve decision-making, foster innovation, and empower users. Fine out more at: www.futuria.ai

Press Release: 13/11/2024, London – Cambridge Management Consulting (Cambridge MC), a global management consulting firm known for its expertise in digital transformation, and Futuria, a leader in Agentic AI solutions, today announced a strategic partnership. This collaboration will empower businesses with innovative artificial intelligence (AI) solutions that drive efficiency, growth, and competitive advantage. Futuria is transforming enterprise operations with its advanced AI Agent Teams and pioneering AgenticAI platforms. Its AI solutions are modular, explainable, and responsible, ensuring seamless integration into complex business environments while enhancing human expertise. Cambridge MC is an international consulting firm with a proven track record of helping organisations navigate complex challenges and seize emerging opportunities. Led by a team of senior executives, Cambridge MC provides strategic guidance and expert support to clients across sectors such as telecommunications, public sector, and back-office operations. Cambridge MC is committed to embracing technological advancements and maximising the benefits of AI for its clients. By combining Futuria's multi-agent AI teams with its own expanded market presence, Cambridge MC continues to enhance its AI-enabled service offerings to improve the speed and quality of client delivery. This strategic partnership brings together Futuria's AgenticAI solutions and Cambridge MC's deep industry expertise and global reach, enabling enhanced decision-making and project delivery. "We're excited to partner with Cambridge Management Consulting," said Rob Price, Co-Founder of Futuria. "Their extensive industry knowledge and global reach will accelerate the adoption of our AgenticAI multi-agent teams, empowering organisations to achieve new levels of efficiency and innovation." Tim Passingham, Chairman of Cambridge Management Consulting, added, "Futuria's innovative AgenticAI platform aligns perfectly with our commitment to providing clients with cutting-edge digital solutions and helping clients navigate the brave new world of Artificial Intelligence. We are confident that this go-to-market partnership will enable us to help our clients harness the tremendous opportunities presented by AI and avoid some of the potential risks of the new technology." About Cambridge Management Consulting Cambridge Management Consulting (Cambridge MC) is an international consulting firm that helps companies of all sizes make a positive impact on the world. Founded in Cambridge, UK, the firm has grown to over 200 consultants working on projects in 22 countries. Cambridge MC focuses on supporting private and public sectors with challenges related to people, processes, and digital technology. Cambridge MC is unique in employing only senior executives with real industry or government experience, ensuring clients receive advice from a place of true credibility. For more information, visit www.cambridgemc.com . About Futuria Futuria is dedicated to reshaping the future of enterprise operations with advanced AI Agent Teams and pioneering AgenticAI solutions. The company's mission is to empower businesses by integrating modular, explainable, and responsible AI that fits seamlessly into complex environments. By enhancing human expertise, Futuria helps organisations achieve control, transparency, and scalability, delivering solutions that drive efficiency, reduce costs, improve decision-making, foster innovation, and empower users. For more information, visit www.futuria.ai .

In a previous article, Building AI-ready Infrastructure, we looked at the challenges that face the builders of digital infrastructure to create the massive engines that will power the ‘AI Revolution’ – in particular, the mega-data centres that will host the training systems used in Generative AI platforms like ChatGPT.  Most of the attention in the data centre industry is on these monsters, but there is more to it that we need to consider. This article looks at the other uses, applications, and implications of AI, and the infrastructure required to maintain them. The Growth of Industrial AI There are many flavours of AI, and although much of the current focus is on Generative AI, commercial applications use all sorts of other techniques to get the benefits that AI can offer. Indeed, there are some AI experts who think that too much emphasis is being given to the prominent large language models, and that the market will require a more diverse model for deploying infrastructure that will support real-world applications. There are many examples of industrial and manufacturing applications using AI already to optimise, for example, production-line efficiency in factories. These systems take data from sensors and devices (e.g. cameras), and then control the manufacturing processes in real time to improve efficiency, or to reduce the use of raw ingredients – a great example being the use of specialist glues in the automobile industry for sticking windscreens to car bodies – an AI platform has been in use to reduce the amount of glue used without compromising the efficacy of the bond. This may sound, trivial but the quantities used globally mean that even small proportional savings can amount to huge monetary savings. This type of application, used across multiple industries, has enormous potential for saving precious resources (or money), and many industries have been using these techniques for years. However, it is mostly the large manufacturers and processing companies that have been able to exploit this. Deploying this type of system can be expensive and usually entails situating a lot of processing power close to the production line. This excludes smaller enterprises from being able to take advantage as the barrier to entry is too high and involves maintaining IT kit that is expensive and difficult to look after.

Artificial Intelligence (AI) is the hottest topic in technology for many reasons, good and bad, but it’s happening and it’s here to stay, so how do we build the infrastructure necessary to support it? To start with, we should recognise that there are many forms of AI. The one that has created the most buzz is generative AI, as seen in ChatGPT, Meta's LLaMA, Claude, Google’s Gemini, and others. Generative AI relies on LLMs (Large Language Models) which have to be trained using vast amounts of data. These LLMs sit in data centres around the world, interconnected by vast fibre networks. The data centre industry has not stopped talking about AI for at least 18 months, as it gears up for an ‘explosion’ in demand for new capacity. Some of the most respected voices in technology have predicted immense amounts of growth in data centre requirements, with predictions of triple the current capacity within 10 years being at the conservative end. That’s three times the current global data centre market, which has taken 30 years or more to get to where it is today. And, when we say growth, we’re talking about power. AI systems will require three times more electricity than data centres currently consume. Depending on who you ask, that’s about 2-4% of today’s global electricity production. And we’re talking about tripling that, or more. Data Centres So, what is ‘AI-ready infrastructure’ and how are we going to build it? The two key elements are data centres (to house the AI systems) and networks (to connect them with the rest of the world). LLM training typically uses servers with GPUs (the chip of choice for AI) and, for various technical reasons, these work best when in close physical proximity to each other – in other words, GPUs work best in large numbers in large data centres. Not just that, but the new generations of GPUs work best in dense data centres, meaning that each rack or cabinet of AI kit needs a lot of power. Most data centres are designed to accommodate older kit that is not so power hungry. The average consumption globally is about 8kW per rack, although many still operate at about 2kW per rack. The latest nVidia (the leading GPU manufacturer) array needs a colossal 120kW per rack. The infrastructure inside a data centre designed for these beasts is complex: the cooling systems (GPUs run very hot) and electrical distribution systems are much harder to design and set up, and are also expensive. So, data centres for AI training systems are mostly going to be new, as adapting older facilities is a non-starter. So, where do you put them? Finding land next to the vast amounts of electricity required is increasingly difficult in many European countries, especially in the UK. Most of the utility grids in Europe are severely lacking in spare capacity, and building new grid connections and electricity generation is a slow and expensive process. The answer might be to locate these new AI data centres near new renewable energy generation sites, but those are few and far between, so land with access to power now carries a hefty premium. Small nuclear reactors could also be an answer but might take a few years to materialise – we know how to build them (witness the nuclear submarine industry) but getting planning permission to put them on land is another matter. All in all, the data centre industry seems to be at least a few years away from being able to provide the massive upgrade in capacity that is expected. Even solving the land/power problem leaves the issue of actually building a new scale of data centre, 10 or 20 times bigger than what most would consider to be a gigantic site today. It can be done, we can solve the engineering challenges, but these are huge construction projects. Networks What about the networks? Actually, although very little real research has been done on the impact of large-scale AI rollouts on existing networks, we might be in a better position. The fibre networks in the UK and many European countries have benefited from significant investment over the last few years, so coverage is a lot better than it used to be. That does not mean that fast and large fibre routes, which will be a necessity for most AI systems, are all there, but it will be easier to build out new capacity than it will be to find power. Still, what we really need is some serious research into the amount of data that will need to be moved about and how that maps with existing network infrastructure. All in all, we have more questions than answers. Some people in the infrastructure industry are sceptical that things will ever get to the scale that some are predicting, but most of us do expect it to happen – it’s just a matter of time, and the race has already begun. Cambridge Management Consulting Duncan Clubb is a Senior Partner at Cambridge Management Consulting, specialising in data centre and edge compute strategy. Duncan has extensive experience as an IT consultant and practitioner and has worked with many leading organisations in the financial, oil and gas, retail, and healthcare sectors. He is widely regarded as a leading expert and is a regular speaker at industry events. If you or your organisation require support preparing your Digital Infrastructure for the emerging AI-industry, you can read about our array of Data Centre services, and get in touch with Duncan Clubb, through our designated Telecoms, Media, and Technology service page.

This month we put the spotlight on Zoë Webster, Associate Consultant for AI, Digital & Innovation With over two decades in the Artificial Intelligence (AI) sphere, Zoë Webster is renowned as a practitioner and leader, recently recognised as one of AI Magazine’s Top 10 Women in AI in the UK and Europe (2024). At Cambridge Management Consulting, Zoë takes on the pivotal role of leading our AI initiatives and driving digital innovation. Leveraging her extensive experience in developing and applying novel AI techniques across diverse sectors such as retail, cyber security, defence, and health, Zoë is instrumental in shaping our AI strategy and implementation. Her unique ability to bridge the gap between the public and private sectors, coupled with her insights on the opportunities and risks of emerging technologies like Large Language Models, positions her perfectly to guide our clients through the complexities of digital transformation. Zoë’s expertise ensures that we remain at the forefront of AI advancements, delivering cutting-edge solutions that drive sustainable growth and innovation for our clients. An Introduction to Zoë's work Having been in the AI space for over 20 years, the past couple of years, since the launch of ChatGPT and the catapulting of AI into the public consciousness, have been in part eye-opening and in part déjà vu for me. The scale and reach are different to anything we have seen to date – I realised this when friends and family of all ages and backgrounds are talking about AI – but it is part of the well-cited technology hype pattern we have seen before in AI as specific techniques show promise (expert systems and neural networks, for example) and organisations see them as a way to solve current problems/challenges. I am fortunate in that I got into AI early. I describe myself as classically trained in that I learnt and experimented with the broad range of AI algorithms on different applications in my early career, so I understand that AI has much more to offer than whatever technique is currently in vogue. After developing and demonstrating novel AI techniques in a range of applications, I got the opportunity to learn more about the role of innovation to the wider economy and society through my time at Innovate UK, now part of UK Research and Innovation. From that, I understand the impact of technology and how business innovation can be accelerated given the right conditions and collaborations. My COVID-19 story includes the juggle of leading Innovate UK’s first COVID-19 innovation competition, to get critical grant funding out to businesses to ensure innovation could continue during this time, while attempting to home-school two children. During lockdown I joined BT, where I built and led their AI Centre of Enablement to scale up AI development and deployment across the company. Developing a machine learning model as a proof-of-concept is one thing, but it takes a whole other set of skills and approaches to successfully and safely deploy that model at scale and with real users, and then to repeat that for other models for different applications. Luckily, my breadth of experience as well as my deep AI expertise enabled me to set up and lead the team to specify and address dozens of AI opportunities. Even as the current developments in AI fail to quite live up to all the hype for everyone, organisations have an opportunity to apply the best and most relevant advancements to generate value, whether that is through customer acquisition, better customer service, better colleague experience, greater productivity or improved sustainability. This goes beyond the technology but to AI governance too, which means thinking carefully about how to practice AI responsibly. Working with Cambridge Management Consulting, I am excited to use my breadth and depth to help more organisations make the most of AI to create value in meaningful ways. To find out more about our AI, digital and innovation services, go to our Innovation service page or contact Zoë using the form below.

In this article, Tom Burton, a cyber security expert and technology thought leader, addresses the historical roots of our implicit trust in computers. As AI models increasingly begin to mimic human traits such as memory and learning, he asks how we can better manage risk and evaluate trust in an era of AI technology.

Authors