Procurement, like many other sectors, is currently being transformed by AI technologies. Organisations are rapidly adopting AI solutions to enhance efficiency, reduce costs, and gain a competitive advantage in their procurement processes. According to recent research by Economist Impact, AI tools are already helping procurement professionals at 64% of firms, with larger organisations leading this digital transformation [7]. However, given the challenges of effectively implementing AI tools and their tendency to produce inaccurate or misleading outputs, it is essential for organisations to critically assess the immediate value of this technology, the costs involved in its deployment, and the potential impact on procurement teams. This article explores the user cases of AI in procurement, the emergence of Agentic AI, implementation challenges and strategies, and how Cambridge Management Consulting can guide you through this complex process and over the hurdles. We also stress that AI in procurement is not a panacea — it can be leveraged successfully for certain user cases when it is integrated with the support of well-trained teams who can spot errors and who understand the limits of these tools. Let's Start with the Limits AI, despite the marketing hype in the media, is not yet a ‘silver bullet’ or an all-encompassing fix when it comes to procurement. It will not solve everything on day one, but it will change how a procurement function operates and will no doubt drive efficiency alongside data accuracy and linkage. Now, more than ever, having a skilled Procurement team alongside cutting-edge technologies like AI is essential for unlocking new efficiencies and elevating procurement to the next level. AI will make a procurement team even more data driven in their analysis and decision making. AI tools will allow procurement teams to sift through vast amounts of data quickly and will draw conclusions for review and assessment. The power of being data driven should not be underestimated and as the American composer and economist W. Edwards Deming once said, “Without data, you’re just another person with an opinion, […] in God we trust; all others bring data” [22]. Each and every organisation must carefully consider how to leverage AI-generated data effectively. While AI can enhance procurement processes, an experienced procurement team remains essential for defining and prioritising key challenges, navigating contract negotiations, and implementing structured cost-reduction strategies. The human touch — particularly in managing and driving commercial supplier relationships — will continue to be a vital component of procurement. While relationship management may not be the single most important aspect of supplier management, it is undeniably critical. It encompasses relationship-building, communication, collaboration, and trust: elements that are fundamental to maximising supplier value and mitigating risks. Supplier management is more than just overseeing transactions; it demands a proactive approach that fosters strong partnerships. AI can revolutionise data management, but it must be complemented by the human expertise that ensures strategic decision-making, relationship stewardship, and long-term supplier success. One could argue that it is easy to get lost in an AI discussion or defining a procurement strategy, but bottom-line supplier relationship management is critical and integral for any procurement department to be successful. If you cannot build, leverage and maintain relationships, you shouldn’t be at the table. The Current State of AI in Procurement Generative AI (GenAI) is having the same disruptive effect in procurement that it is in many other business areas, initially by completing quite simple tasks with incredible speed, accuracy and efficiency. This includes automating routine tasks, providing actionable insights from data sets, and freeing up time for your teams to focus on higher-level tasks such as managing processes and vendor relationships. Below we highlight which tasks can be successfully enhanced or supported by AI. AI-Powered Procurement Automation For business leaders, AI is the tireless digital assistant that procurement teams have long needed. By automating tedious tasks like purchase order processing, linking third-party costs back to revenue services to strive for gross margin clarity, invoice management, and contract administration, AI frees professionals to focus on strategic initiatives. The impact is substantial: according to recent data, 45% of AI investments in procurement are focused on contract automation, highlighting organisational priorities for efficiency improvement and error reduction [1]. Real-world implementation has shown significant results. For example, a global manufacturing company deployed AI to automate invoice processing, reducing errors by 80% and cutting processing time by half [1]. Data-Driven Decision-Making AI spares procurement from wading through hours of paperwork, a process that is time-consuming and prone to cascades of errors. Rather than being overwhelmed by huge data sets and unsure on which useful information to extract, AI does this with much more precision and many orders of speed. With AI-driven analytics, procurement teams can manage and link multiple data sets, identify trends, and make more informed purchasing decisions in real time. McKinsey reports that procurement leaders implementing AI-driven analytics have accelerated supplier selection by 30%, demonstrating the significant impact on workflow efficiency [1]. The Rise of Agentic AI in Procurement While traditional AI has already made significant inroads in procurement, a more advanced form — Agentic AI — is now emerging as a step-change for the profession. What is Agentic AI? Agentic AI represents the next phase in artificial intelligence models. Unlike previous automation tools that require human oversight for key decisions, AI agents can operate independently, leveraging machine learning, predictive analytics, and natural language processing to interact with suppliers, assess risks, and optimise sourcing strategies with minimal supervision[4]. According to The Hackett Group's 2025 Procurement Agenda and Key Issues Study, Agentic AI is the top trend impacting procurement this year, alongside digital procurement and automation[4]. The technology is expected to disrupt nearly 50% of procurement activities over the next five to seven years, creating entirely new opportunities for strategy[4]. The outlook for procurement teams might be more climatic, depending on the consistency and accuracy of Agentic AI. These models will be capable of independent reasoning and it currently unclear how close this will bring us to Artificial General Intelligence (AGI). Adoption Trends and Strategic Focus The shift in Agentic AI from concept to a reality might be surprisingly rapid. A recent survey by ProcureCon found that 90% of procurement leaders are considering AI agents for optimising their procurement functions[4]. This technology is becoming central to orchestrating complex procurement activities with unprecedented efficiency — from sourcing and contract negotiations to spend classification, supplier onboarding, compliance, and risk assessment. There is relatively little data or evidence at this point to suggest the likely error-rate among these agents and to what degree all results and actions will need to be checked and validated by human teams. It is also underappreciated that in order to successfully implement AI, businesses must have set up basic data structures, metadata, and processes. A significant number of companies are not yet ready to adopt these technologies and must get their house in order first. Implementation is a potentially complex and expensive task, requiring long phases of design and testing to fine-tune the outputs. Benefits of AI Procurement The adoption of AI in procurement delivers multiple advantages that will enhance organisational performance across various metrics. We look at the key advantages below: Cost Reduction & Efficiency Gains AI implementation in procurement delivers measurable financial benefits. McKinsey highlights a 10% reduction in procurement costs through AI adoption[1]. By automating routine tasks, businesses reduce labour costs while simultaneously increasing throughput and accuracy. Enhanced Supplier Management AI transforms supplier relationships by providing deeper insights into supplier performance, risk profiles, and market dynamics. This enables procurement teams to make more informed decisions about supplier selection, negotiation strategies, and relationship management. Agentic AI will bring predictive analytics that will be able to flag and correct issues in your supply chain before they occur. Improved Risk Management Leading AI platforms apply advanced machine learning techniques to uncover signals in supplier data that indicate potential disruptions, from financial issues and bankruptcy risks to geopolitical challenges, climate events, and cyber threats. This allows procurement teams to mitigate risks proactively rather than reactively, creating a significantly lower threat to spend, compliance and reputational damage[6]. Contract Intelligence Natural language processing tools extract insights from legacy contracts and external databases to benchmark terms. AI can negotiate agreements with suppliers in real-time chat sessions, optimise renewals, and highlight risks — significantly reducing the manual burden on procurement teams. Smart contracts can then self-execute when conditions are met and provide comprehensive audit trails[6]. See our separate article on AI in Contract Management for more details: https://www.cambridgemc.com/how-to-successfully-integrate-ai-into-your-contract-lifecycle-management Challenges in Implementing AI in Procurement Despite the clear benefits, companies face several significant challenges when implementing AI in their procurement functions. Data Quality & Availability AI systems require vast amounts of accurate data to function effectively. Many supply chains struggle with data silos and inconsistent formats, making it difficult to create the comprehensive, high-quality datasets needed for AI[2]. Data fragmentation across different systems — legacy platforms, ERP systems, sensors, and IoT devices — creates integration challenges that can undermine the effectiveness of AI [8]. Integration with Existing Systems Many legacy procurement systems were not designed to integrate with modern AI technologies, leading to compatibility issues and potential disruptions in system functionality [2]. This technical challenge often requires significant IT resources to overcome. Implementation Costs Implementing AI involves substantial initial expenses for software, hardware, and skilled personnel. Additionally, there are ongoing costs to retrain AI models as business environments evolve [2]. These financial considerations can be barriers to adoption, particularly for smaller organisations. Internal Resistance Resistance to adopting new technologies often stems from a lack of understanding, fear of job displacement, or discomfort with changing established workflows[2]. This human factor can significantly slow or derail AI implementation efforts if not properly addressed with training, careful messaging and change management methodologies. Data Security Concerns As AI systems process sensitive procurement data, including confidential pricing information and intellectual property, security becomes a critical concern. Businesses must engage comprehensive data protection measures while still enabling AI systems to access the information they need. Responsible AI As well as data security concerns, there is also a strong need and argument for companies to strive for fitness and non-discrimination when it comes to AI. Companies should have an AI Risk and Assessment process in place to ensure that data bias is avoided and that ethical guidelines when it comes to data analysis and management are followed. The ‘AI Ethics Guidelines Global Inventory (AEGGI)’, created by Algorithm Watch, currently contains 167 sets of principles and guidelines, which it recommends should be followed, and there are also responsible AI training tools available, such as Google’s ‘People & AI Guidebook’ and Omidyar Networks ‘Ethical Explorer’, that can be used. Additionally, new legislation is also being introduced, for example, the ‘EU’s Artificial Intelligence Act’, to ensure that AI is used responsibly. It’s widely acknowledged that 8 core principles should be assessed and evaluated when developing AI accountability [20]: Privacy & Security Reliability & Safety Transparency & Explainability Fairness & Non-discrimination Professional Responsibility Human Control Promotion of Human Values Strategies for Successful AI Implementation To overcome implementation challenges and maximise the benefits of AI in procurement, you should consider the following strategies: Establish Strong Data Foundations Before diving into AI adoption, you must ensure that your business has the right data infrastructure in place. This includes: Improving data quality, governance, and standardisation Integrating disparate data sources Establishing real-time data capabilities, which are prerequisites for effective AI implementation[4] Implementing foundational tools like spend analysis and decision optimisation[1] Take a Targeted Approach Rather than attempting wholesale transformation, you should: Identify specific areas where AI can complement existing processes Focus initial implementation on high-value, low-complexity use cases Use AI where it adds the most value rather than applying it universally [1] Consider a phased implementation approach Address the Human Element Successful AI implementation requires careful attention to the people involved: Equip your workforce with the skills to leverage AI effectively Implement comprehensive change management strategies Educate employees about how AI will enhance their roles rather than replace them Rethink how procurement teams interact with AI-driven systems [4] Balance AI with Human Intelligence The most effective procurement functions will be those that: Combine the efficiency of AI with human judgment and expertise Preserve crucial human skills in negotiation, relationship management, and strategic decision-making Use AI to augment human capabilities rather than replace them entirely [1] Create collaborative human-AI workflows that maximise the strengths of both approaches Conclusion: Blending AI & Human Expertise AI is fundamentally reshaping procurement, transforming it from a primarily transactional function to a strategic and predictive driver of value. From automating routine tasks to enabling sophisticated predictive analytics and autonomous decision-making, AI technologies are creating unprecedented opportunities for efficiency, intelligence, and innovation. While implementation challenges exist, businesses that approach AI adoption strategically, with proper attention to data foundations, targeted use cases, and human factors, can realise significant benefits. As we look into the near future, the most successful procurement functions will be those that effectively blend AI capabilities with human expertise, creating a powerful synergy that drives an ongoing competitive advantage. Cambridge MC: Your Partner for AI-Powered Procurement Implementing AI in procurement requires specialised expertise and experience. Cambridge Management Consulting (Cambridge MC) offers you the guidance needed to navigate this complex transformation successfully. We have dedicated Data and AI teams as well as a deep background in procurement and contract management expertise. Comprehensive Implementation Support Cambridge MC offers: Strategic assessment of procurement AI opportunities Roadmap development for AI implementation Integration of AI solutions with existing procurement systems Change management support to ensure successful adoption Ongoing optimisation of AI-powered procurement processes Get in touch with Andy Everest or one of our procurement experts to discuss your current needs and any issues pertaining to AI and procurement. Use the form below or email: aeverest@cambridgemc.com . Visit our Commercial & Procurement page: https://www.cambridgemc.com/procurement-and-commercial Citations [1] https://consultingquest.com/insights/generative-ai-in-procurement/ [2] https://www.linkedin.com/pulse/6-key-challenges-ai-implementation-supply-chain-industry-chris-clowes-1r67c [3] https://www.oracle.com/scm/ai-in-procurement/ [4] https://www.gep.com/blog/technology/agentic-in-procurement-overview-benefits-implementation [5] https://futuria.ai/futuria-and-cambridge-management-consulting-announce-innovative-ai-driven-partnership/ [6] https://www.gep.com/blog/technology/how-ai-is-revolutionizing-the-procurement-cycle [7] https://impact.economist.com/perspectives/strategy-leadership/ai-demands-new-era-procurement-skills [8] https://www.qservicesit.com/9-common-challenges-in-supply-chain-management-with-ai [9] https://precoro.com/blog/ai-in-procurement/ [10] https://www.cio.com/article/3853910/how-agentic-ai-can-deliver-profound-transformation-in-procurement.html [11] https://www.cambridgemc.com/futuria-and-cambridge-management-consulting-announce-innovative-ai-driven-partnership [12] https://www.spendflo.com/blog/ai-in-procurement-orchestration [13] https://media-publications.bcg.com/BCG-Executive-Perspectives-Future-of-Procurement-with-AI-2025-27Feb2025.pdf [14] https://pmc.ncbi.nlm.nih.gov/articles/PMC11788849/ [15] https://www.cappo.org/news/660146/Pros-and-Cons-of-Using-Artificial-Intelligence-for-Procurement.htm [16] https://pactum.com/understanding-agentic-ai-in-procurement-how-autonomous-ai-has-been-transforming-supplier-deals/ [17] https://digitalisationworld.com/news/67692/qarbon-technologies-collaborates-with-cambridge-management-consulting [18] https://www.coupa.com/blog/ai-in-procurement/ [19] https://suplari.com/10-procurement-job-roles-most-impacted-by-ai/ [20] https://stockiqtech.com/blog/disadvantages-ai-supply-chain/ [21] ‘ Responsible AI: Principles and Practical Applications ’ – LinkedIn Course, By: Tsu-Jae Liu, Brandie Nonnecke , and Jill Finlayson ( https://www.linkedin.com/learning-login/share?forceAccount=false&redirect=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fai-accountability-build-responsible-and-transparent-systems%3Ftrk%3Dshare_ent_url%26shareId%3DhTdANzytTi28DI30mdTN%252BQ%253D%253D ) [22] Top 200 W. Edwards Deming Quotes (2025 Update). QuoteFancy . https://quotefancy.com/w-edwards-deming-quotes.

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 .

The Environmental Trade-off in Digital Infrastructure Development Digital development presents a double-edged sword. On the one hand, it boosts productivity through remote work, AI, and automation, with the potential to lift billions out of poverty. Yet, at the same time, the rapid growth of infrastructure required to support these developments will need a corresponding growth in decarbonisation to avoid a climate catastrophe. The German Advisory Council on Global Change highlights this contradiction: “uncontrolled digital change threatens to undermine the important foundations of our democracies” [1] . This article takes an in-depth look at how global institutions push the mantra of ‘digitisation’ as a developmental priority for nations while failing to adequately acknowledge the huge climate impact of this enterprise. This obscuring of consequences eases the way for a rapid extension of infrastructure that consumes billions of gallons of non-renewable resources annually. In this article, I suggest that detailed modelling and forecasting are one of the major pillars needed to address this dichotomy. I will set out an approach and resources for modelling the digital demand to design a more predictive approach to digital infrastructure builds. The Environmental Impact of a Data Explosion The amount of data flowing over global digital infrastructure has exploded 300-fold over the last 10 years [2] , with the next 20 years expected to see faster-paced growth on the back of the continued digitisation of life and entertainment, as well as from huge numbers of people in developing countries coming online for the first time. This explosion is a good thing—the UN’s Sustainable Development Goal (SDG) 9 aims to provide universal and affordable access to the internet by 2030 [3] . Access to the internet and digital services strongly correlates with improvements in education, healthcare and women’s empowerment. As increasing numbers of people come online, and the scale of their data use grows, a variety of digital infrastructure will need to be built or scaled up if the digital ambitions of countries and trading blocks are to be realised. Connectivity is one part of the solution—increased coverage of broadband, mobile and satellite will undoubtedly support these targets. But, ultimately, all that data traffic needs a destination point, in the form of data centres, which, unfortunately, require vast sums of power. In the USA, data centres are expected to consume 380TWh of electricity by 2027 [4] , almost 9% of the country’s total consumption. Ireland faces an even larger burden with digital infrastructure expected to consume 33% of the country’s total electricity by 2026 [5] , and potentially 70% of the country’s electricity by 2030 [6] . Ireland and the USA have reliable national power grids, but this is not necessarily the case in developing countries. In Nigeria, data centres and mobile towers rely heavily on diesel generators, burning nearly a billion litres of diesel annually. This is a country where the average annual mobile data traffic per subscription is only 6GB per year [7] , just over 0.1% of the average traffic from a UK subscriber. To achieve universal internet access for a population that is estimated to cross the 300 million threshold by 2036 will require an exponential growth in digital infrastructure. If Nigeria remained dependent on diesel generators, and data consumption on a per-person basis reaches the UK’s level of data traffic, then the country would consume 9 trillion litres of diesel a year—over 100 times the amount of diesel consumed by the entire world in 2022 [8] . This single event would create a climate catastrophe—even if the UK, France, Germany, Spain and the Nordics reduced their CO2 emissions to zero, this would offset less than half of this increase. This is of course the worst-case scenario. Grid infrastructure has developed across West Africa and there are a multitude of projects which are building green energy infrastructure. But there has yet to be a major MNO, TowerCo or data centre company which has shown significant year-on-year reductions in emissions. It is unjust to expect developing nations to slow down or halt their digitisation while developed countries reap the benefits of a digitised economy. Instead, alternative approaches to managing global emissions are needed. And this is where predictive analytics become a crucial tool for forecasting future demand. These tools and models will support the development of alternative strategies for power generation and implement methods to reduce emissions from digital infrastructure. A predictive tool that models national network traffic growth and compares it to projected digital infrastructure expansion will help identify underserved areas early, enabling better planning of digital and power infrastructure. Early planning allows for the integration of renewable energy, natural cooling solutions, and partnerships with sustainability experts to reduce emissions. Creating the Model: Traffic vs Digital Infrastructure To address these challenges, David Jones, an Associate of Cambridge Management Consulting, has developed a comprehensive model that examines global internet traffic on a country-by-country basis and compares it to existing and planned digital infrastructure within those countries. This model considers several factors: Population Growth: Increasing numbers of internet users Economic Growth: Rising wealth levels leading to more internet usage Internet Penetration: A growing proportion of each country’s population getting online Usage Patterns: Moving towards video transmission over the internet significantly increasing traffic B2B and M2M Traffic: Business-to-business and machine-to-machine Internet traffic growth This model projects internet traffic growth over the next 20 years, if data traffic growth follows a logarithmic curve, increasing at a decreasing rate. In Germany and other developed nations, the rate of traffic growth slows once it reaches a certain threshold, as there is a natural limit to how much HD video a person can consume. By comparing these projections with a database of over 10,000 data centres, including locations and power consumption, it is possible to identify regions with underdeveloped or overdeveloped digital infrastructure. Note: This model does not account for the growth in generative AI, which adds further demand on a strained digital infrastructure. For more information on this subject, see our recent article: Building an AI-ready infrastructure . Initial Results When we run this model and compare countries, what immediately becomes clear is the difference in scale between the growth of digital infrastructure and internet traffic. Ireland’s digital infrastructure is increasing at a rate faster than its internet traffic, while in countries like Bangladesh and Algeria internet usage is growing ten times faster than the digital infrastructure that supports it. David has modelled 76 countries and will be completing another 50 over the next few months. So far, the CAGR of internet traffic is around 30%, and the CAGR of data centres is around 12%. What’s clear from this graph is how the difference in growth rates compounds over time, and that as the years progress the gap between traffic and infrastructure widens. This shows that over time the availability of infrastructure will become a massive limiting factor to digital experience. Eventually, the lack of adequate infrastructure may even prevent citizens from accessing essential internet services.