Innovation

Disruption now occurs with unprecedented regularity, as industries are upended not by traditional competitors but by unexpected entrants wielding innovative technologies and business models.  The difference between thriving and becoming obsolete increasingly hinges on your organisation's ability to anticipate and adapt to disruption before it's too late. The Ur-case of this was Blockbuster, who ignored the threat of streaming technologies, and specifically Netflix (which it could have bought), until it was far too late to pivot and catch up. Our article explores how businesses can develop strategies that offer predictions and agility, embedding creativity and insight into frameworks and actionable steps that plot a course through the disruptive landscapes of the next few years and beyond. Understanding the Nature of Disruption Disruption is no longer just a buzzword — or the philosophy of ‘break things and move fast’ that drove the early tech start-ups that now dominate our waking lives. The theory of disruptive innovation, popularised by Harvard Business School professor Clayton Christensen, explains how new technologies, products, or services can start small but eventually surpass established offerings in existing markets[1]. This process typically begins when smaller companies with fewer resources challenge established or traditional businesses by addressing underserved market needs[5] in new ways; usually with business models that bypass normal routes to market and allow these companies to scale at pace. Recent examples include: fintech banks that challenge the need for brick-and-mortar; online over-the-top media applications that replace the need for print media and traditional broadcast television; digital media and the success of subscription models, replacing physical media for music, films and other forms of entertainment; and platform apps like Uber, which connect us to a fleet of independent drivers who are paid per ‘gig’ and regulated by a ratings system. Today's notion of disruption is characterised by several key features: Accelerated Pace of Change The pace of disruption has accelerated beyond anything previously seen, with transformative technologies reaching mainstream adoption faster than ever[15]. While it took decades for technologies like electricity and telephones to achieve mass adoption, modern innovations like smartphones and AI have transformed entire industries in just a few years. Cross-Industry Disruption Disruptive threats increasingly come from outside traditional industry boundaries. Companies must now monitor not only direct competitors but also adjacent industries and completely unrelated sectors where transferable innovations might emerge[15]. For example, tech giants have disrupted financial services, retail, healthcare, and automotive industries without prior experience in these sectors. Technology-Enabled Business Models Today's most powerful disruptions combine technological innovation with business model innovation. Examples include: Platform models: Uber revolutionised transportation by connecting riders and drivers through a user-friendly mobile app, utilising independent drivers who pay for their own vehicles for rapid scalability[1]. Subscription services: Netflix and Spotify transformed entertainment consumption by shifting from physical media to on-demand streaming with personalised algorithmic content recommendations[1]. Direct-to-consumer approaches: Tesla's direct sales model bypassed traditional dealership networks while integrating advanced electric vehicle technology and autonomous capabilities[1]. From Traditional to Adaptive Strategy Traditional strategic planning approaches — characterised by multi-year roadmaps and rigid implementation plans — have become increasingly inadequate in today's fast-moving business environment. We look at some of the challenges businesses now face below. The Limitations of Traditional Strategy Conventional strategies often fail because they: Assume relative stability in market conditions Take too long to develop and implement Lack flexibility to respond to unexpected changes Rely heavily on historical data to predict future outcomes The Adaptive Strategy Advantage Adaptive strategy, often described as the "Be Fast" approach, emphasises agility, experimentation, and continuous evolution[3]. This approach thrives in fluid industries with high uncertainty and a fast pace of change, such as technology, fashion, entertainment, and start-ups[3]. Organisations that embrace adaptive strategies gain significant advantages: Higher profitability: Companies ranking high in adaptability enjoy up to 75% higher profitability than their less adaptive counterparts[10]. Faster market response: Adaptive firms achieve approximately 60% faster time-to-market compared to traditional competitors[10]. Innovation capacity: The ability to experiment boldly and rapidly iterate creates an environment where breakthrough innovations are more likely to emerge[10]. Real-World Adaptive Strategy Success Consider Netflix's journey from DVD rental service to streaming giant to content producer. Rather than creating a 10-year plan, Netflix constantly evolved based on emerging technologies, customer preferences, and market opportunities. This adaptive approach allowed them to pivot whenever necessary while maintaining their core value proposition of convenient entertainment access[1]. A New Framework for Ensuring Strategy Relevance To maintain strategic relevance amid disruptive trends, companies need a systematic framework that balances stability with flexibility. Anticipate Disruption Through Trend Analysis Successful businesses identify potential disruptions before they manifest fully by monitoring Hard Trends — future certainties based on measurable facts[15]. These include demographic shifts, technological advancements, and regulatory changes that provide predictable directional guidance. For example, financial services firms that recognised the Hard Trend of increasing digital connectivity were better positioned to respond to the rise of mobile banking and fintech disruption. Build your Agility Organisational structures and processes must be designed to support rapid adaptation: Decentralised decision-making: Empower teams closest to customers and market changes to make decisions without lengthy approval chains[3]. Cross-functional collaboration: Break down silos between departments to enable faster information sharing and coordinated responses to change[3]. Agile methodologies: Adapt software development approaches like sprints, continuous integration, and iterative testing to broader business strategy[3]. Foster a Culture of Innovation Innovation cannot be an isolated function — it must permeate your entire organisation: Encourage experimentation: Create safe spaces for testing new ideas with minimal bureaucracy and fear of failure[3]. Customer-centric innovation: Ground innovation efforts in a deep understanding of customer needs rather than internal assumptions[14]. Structured innovation processes: Establish clear pathways for moving ideas from conception to implementation while maintaining flexibility[14]. KPIs that support innovation: For example, looking at the value of a portfolio of innovations rather than a specific innovation project. Leverage Data & Technology Data-driven insights provide a vital competitive advantage in your disruption response: Real-time market intelligence: Deploy advanced analytics to detect weak signals of change before they emerge fully-formed[3]. Predictive modelling: Use Agentic AI to identify patterns and forecast potential disruptions[2]. Digital transformation lifecycle: Invest in the necessary expertise and infrastructure to undertake on-going programmes of transformation — a big step, and potentially expensive, but it can help immunise your business against disruptive technologies and new models. Practical Implementation Steps Translating disruption awareness into effective action requires specific tactical approaches.

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.

Pioneering Technologies for the Future of Urban Transformation Smart cities might sound like a utopian vision from the 1950s; something that sounds already out-of-date and perhaps even naive in our current geopolitical climate. But as urban spaces gradually implement a a series of technological leaps, the smart city emerges as a potential reality, offering a new way to unite communications with infrastructure via real-time feedback. Smart cities could dramatically enhance our quality of life, efficiency, and environmental stewardship. Given that cities are significant contributors to global emissions — responsible for approximately 70% of greenhouse gases — they will play a critical role in reaching net zero. Reflecting insights from the last Smart City Expo in Barcelona (November 2024) and a range of ambitious projects across the UK, this article delves into the strategic alignment of technology, infrastructure, and sustainability shaping today's urban landscapes. What Defines a Smart City? A smart city is fundamentally ‘a municipality that uses information and communication technology to increase operational efficiency, share information with the public, and improve the quality of government services and citizen welfare.’ While definitions vary, the overarching mission is to optimise city functions, drive economic growth, and enhance the quality of life through technology and data analysis. Smart city initiatives typically require three critical components: Networks of sensors and citizen participation to collect data Connectivity linking these networks to government systems Open data sharing to make results, changes, and improvements accessible to the public Developing this underlying infrastructure is complex and expensive. Crucially, it depends on strong relationships between government, the private sector, and citizens, as most of the work to create and maintain these data-driven environments happens through collaboration and public-private partnerships.