This article explores the ‘Third Way’ to AI adoption – a balanced approach that enables innovation, defines success clearly, and scales AI responsibly for lasting impact | READ FULL ARTICLE

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.

What is the Contract Lifecycle Management and Why does it Matter? The future success of your business depends on realising the value that’s captured in its contracts. From vendor agreements to employee documents, everywhere you look are commitments that need to be met for your business to succeed. The type of contract and the nature of goods or services it covers will determine what sort of management activities might be needed at each stage. How your company is organised will also determine which departments or individuals are responsible for what activities at each stage. Contract Lifecycle Management, from a buyer's perspective, is the process of defining and designing the actual activities needed in each stage for any specific contract, allocating ownership of the activities to individuals or groups, and monitoring the performance of those activities as the contract progresses through its lifecycle. The ultimate aim is to minimise surprises, ensure the contracted goods or services are delivered by the vendor in accordance with the contract, and realise the expected business benefits and value for money. The Problem of Redundant Spend in Contracts Despite the built-in imbalance of information favoring suppliers, companies still choose to oversee these vendors internally. However, many adopt a reactive, unstructured approach to supplier management and struggle to bridge the gap between contractual expectations and actual performance. Currently, where governance exists, it is often understaffed, with weak, missing, or poorly enforced processes. The focus is primarily on manual data collection, validation, and basic retrospective reporting of supplier performance, rather than on proactively managing risk, relationships, and overall performance. The amount of redundant spend in contracts can vary widely depending on the industry, the complexity of the contracts, and how rigorously they are managed. For further information on this, Cambridge MC’s case studies provide insights into typical ranges and common sources of redundant spend. As a general estimate, industry analysts often state that redundant spend can account for as much as 20% of total contract value. In some cases, especially in poorly managed contracts, this can be much higher. What is AI-driven Contract Management? Artificial Intelligence (AI) is redefining contract management, transforming a historically time-consuming and manual process into a streamlined, efficient, and intelligent operation. Traditionally, managing contracts required legal teams to navigate through extensive paperwork, drafting, reviewing, and monitoring agreements — a process prone to inefficiencies and human error. With the emergence of artificial intelligence, particularly generative AI and natural language processing (NLP), this area of operations is undergoing a paradigm shift. This step change is not without concerns however, as there are the inevitable risks of AI hallucinations, training data biases and the threat to jobs. AI-driven contract management solutions not only automate repetitive tasks but also uncover valuable insights locked up in contract data, improving compliance and reducing the risks that are often lost in reams paperwork and contract clauses. Put simply, AI can automate, analyse, and optimise every aspect of your contract lifecycle. From drafting and negotiation to approval, storage, and tracking, AI-powered platforms enhance precision and speed across these processes; in some cases reducing work that might take several days to minutes or hours. By discerning patterns and identifying key terms, conditions, and concepts within agreements, AI enables businesses to parse complex contracts with ease and efficiency. In theory, this empowers your legal and contract teams (rather than reducing them), allowing personnel to focus on high-level tasks such as strategy rather than minutiae. However, it is important to recognise that none of the solutions available in the marketplace today offer companies an integrated supplier management solution, combining a comprehensive software platform, capable of advanced analytics, with a managed service. Cambridge Management Consulting is one of only a few consultancies that offers fully integrated Contract Management as a Service (CMaaS). Benefits of Integrating AI into your Contract Lifecycle Management Cambridge MC’s Contract Management as a Service (CMaaS) 360-degree Visibility: Enable your business to gain 360-degree visibility into contracts and streamline the change management process. Real-time Data: Gain real-time performance data and granularly compare it against contractually obligated outcomes. More Control: Take control of your contracts and associated relationships with an integrated, centralised platform. Advanced meta data searches provide specific information on external risk elements, and qualitative and quantitative insights into performance. Reduces Costs: By automating manual processes, businesses can significantly reduce administrative costs associated with contract management. AI-based solutions eliminate inefficiencies in the contract lifecycle while minimising reliance on external legal counsel for routine tasks. Supplier Collaboration: Proactively drive supplier collaboration and take a data-driven approach towards managing relationships and governance process health. Enhanced Compliance: AI tools ensure that contracts adhere to internal policies and external regulations by flagging non-compliant clauses during the drafting or review stage. This proactive approach reduces the risk of costly disputes or penalties. Reduces Human Errors: In traditional contract management processes, human errors can lead to missed deadlines and hidden risks. AI-powered systems use natural language processing to identify inconsistencies or inaccuracies in contracts before they escalate into larger issues. Automates Repetitive Tasks: AI-powered tools automate time-consuming tasks such as drafting contracts, reviewing documents for errors, and extracting key terms. This frees up legal teams to focus on higher-value activities like strategic negotiations and risk assessment. We can accurately model and connect commercial information across end-to-end processes and execution systems. AI capabilities then derive and apply automated commercial intelligence (from thousands of commercial experts using those systems) to error-proof complex tasks such as searching for hidden contract risks, determining SLA calculations and performing invoice matching/approvals directly against best-in-class criteria. Contract management teams using AI tools reported an annual savings rate that is 37% higher than peers. Spending and tracking rebates, delivery terms and volume discounts can ensure that all of the savings negotiated in a sourcing cycle are based on our experience of managing complex contracts for a wide variety of customers. Our Contract Management as a Service, underpinned by AI software tooling, has already delivered tangible benefits and proven success. 8 Steps to Transition Your Organisation to AI Contract Management Implementing AI-driven contract management requires a thoughtful and structured approach to ensure seamless integration and long-term success. By following these key steps your organisation can avoid delays and costly setbacks. Step 1 Digitise Contracts and Centralise in the Cloud: Begin by converting all existing contracts into a digital format and storing them in a secure, centralised, cloud-based repository. This ensures contracts are accessible, organised, and easier to manage. A cloud-based system also facilitates real-time collaboration and allows AI to extract data from various file formats, such as PDFs and OCR-scanned images, with ease. Search for and retrieve contracts using a variety of advanced search features such as full text search, Boolean, regex, fuzzy, and more. Monitor upcoming renewal and expiration events with configurable alerts, notifications, and calendar entries. Streamline contract change management with robust version control and automatically refresh updated metadata and affected obligations. Step 2 Choose the Right AI-Powered Contract Management Software: Selecting the right software is a critical step in setting up your management system. Evaluate platforms based on their ability to meet your organisation’s unique contracting needs. Consider key factors such as data privacy and security, integration with existing systems, ease of implementation, and the accuracy of AI-generated outputs. A well-chosen platform will streamline workflows while ensuring compliance and scalability. Step 3 Understand How AI Analyses Contracts: To make the most of AI, it’s essential to understand how it processes contract data. AI systems use Natural Language Processing (NLP) to interpret and extract meaning from human-readable contract terms, while Machine Learning (ML) enables the system to continuously improve its accuracy through experience. These combined technologies allow AI to identify key clauses, conditions, and obligations, as well as extract critical data like dates, parties, and legal provisions. Training your team on these capabilities will help them to understand the system and diagnose inconsistencies. Step 4 Maintain Oversight and Validate AI Outputs: While AI can automate repetitive tasks and significantly reduce manual effort, human oversight is indispensable. Implement a thorough process for spot-checking AI-generated outputs to ensure accuracy, compliance, and alignment with organisational standards. Legal teams should review contracts processed by AI to verify the integrity of agreements and minimise risks. This collaborative approach between AI and human contract management expertise ensures confidence in the system. Step 5 Refine the Data Pool for Better Results: The quality of AI’s analysis depends heavily on the data it is trained on. Regularly refine and update your data pool by incorporating industry-relevant contract examples and removing errors or inconsistencies. A well-maintained data set enhances the precision of AI outputs, enabling the system to adapt to evolving business needs and legal standards. Step 6 Establish Frameworks for Ongoing AI Management: To ensure long-term success, set clear objectives and measurable goals for your AI contract management system. Define key performance indicators (KPIs) to track progress and prioritise features that align with your organisation’s specific requirements. Establish workflows and governance frameworks to guide the use of AI tools, ensuring consistency and accountability in contract management processes. Step 7 Train and Empower Your Teams: Equip your teams with the skills and knowledge they need to use AI tools effectively. Conduct hands-on training sessions to familiarise users with the platform’s features and functionalities. Create a feedback loop to gather insights from your team, allowing for continuous improvement of the system. Avoid change resistance by using change management methodologies, as this will foster trust in the technology and drive successful adoption. Step 8 Ensure Ethical and Secure Use of AI: Tools Promote transparency and integrity in the use of AI-driven contract management. Legal teams should have the ability to filter sensitive information, secure data within private cloud environments, and trace data back to its source when needed. By prioritising data security and ethical AI practices, organisations can build trust and mitigate potential risks. With the right tools, training, and oversight, AI can become a powerful ally in achieving operational excellence as well as reducing costs and risk. Overcoming the Technical & Human Challenges While the benefits are compelling, implementing AI in contract management comes with some unique challenges which need to be managed by your leadership and contract teams: Data Security Concerns: Uploading sensitive contracts to cloud-based platforms risks data breaches and phishing attacks. Integration Complexities: Incorporating AI tools into existing systems requires careful planning to avoid disruptions and downtime. Change Fatigue & Resistance: Training employees to use new technologies can be time-intensive and costly. There is a natural resistance to change, the dynamics of which are often overlooked and ignored, even though these risks are often a major cause of project failure. Reliance on Generic Models: Off-the-shelf AI models may not fully align with your needs without detailed customisation. To address these challenges, businesses should partner with experienced providers who specialise in delivering tailored AI-driven solutions for contract lifecycle management. Case Study 1: The CRM That Nobody Used A mid-sized company invests £50,000 in a cutting-edge Customer Relationship Management (CRM) system, hoping to streamline customer interactions, automate follow-ups, and boost sales performance. The leadership expects this software to increase efficiency and revenue. However, after six months: Sales teams continue using spreadsheets because they find the CRM complicated. Managers struggle to generate reports because the system wasn’t set up properly. Customer data is inconsistent, leading to missed opportunities. The Result: The software becomes an expensive shelf-ware — a wasted investment that adds no value because the employees never fully adopted it. Case Study 2: Using Contract Management Experts to Set Up, Customise and Provide Training If the previous company had invested in professional services alongside the software, the outcome would have been very different. A team of CMaaS experts would: Train employees to ensure adoption and confidence in using the system. Customise the software to fit business needs, eliminating frustrations. Provide ongoing support, so issues don’t lead to abandonment. Generate workflows and governance for upward communication and visibility of adherence. The Result: A fully customised CRM that significantly improves the Contract Management lifecycle, leading to: more efficient workflows, more time for the contract team to spend on higher value work, automated tasks and event notifications, and real-time analytics. With full utilisation and efficiency, the software delivers real ROI, making it a strategic investment instead of a sunk cost. Summary AI is reshaping the way organisations approach contract lifecycle management by automating processes, enhancing compliance, reducing risks, and improving visibility into contractual obligations. From data extraction to risk analysis, AI-powered tools are empowering legal teams with actionable insights while driving operational efficiency. However, successful implementation requires overcoming challenges such as data security concerns and integration complexities. By choosing the right solutions, tailored to their needs — and partnering with experts like Cambridge Management Consulting — businesses can overcome the challenges and unlock the full potential of AI-based contract management. A Summary of Key Benefits Manage the entire lifecycle of supplier management on a single integrated platform Stop value leakage: as much as 20% of Annual Contract Value (ACV) Reduce on-going governance and application support and maintenance expenses by up to 60% Deliver a higher level of service to your end-user community. Speed without compromise: accomplish more in less time with automation capabilities Smarter contracts allow you to leverage analytics while you negotiate Manage and reduce risk at every step of the contract lifecycle Up to 90% reduction in creating first drafts Reduction in CLM costs and extraction costs How we Can Help Cambridge Management Consulting stands at the forefront of delivering innovative AI-powered solutions for contract lifecycle management. With specialised teams in both AI and Contract Management, we are well-placed to design and manage your transition with minimal disruption to operations. We have already worked with many public and private organisations, during due diligence, deal negotiation, TSAs, and exit phases; rescuing millions in contract management issues. Use the contact form below to send your queries to Darren Sheppard , Senior Partner for Contract Management. Go to our Contract Management Service Page

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.