AI at the Edge: The Backbone of Digital Infrastructure

Duncan Clubb

SUBSCRIBE CONTACT US

Author


The symbiotic relationship between AI and edge computing has continued to evolve from emerging technology to essential infrastructure, fundamentally reshaping how industries can operate across the globe.


Back in 2024, we explored the challenges facing builders of digital infrastructure as they created the massive data engines powering the AI paradigm-shift – particularly the mega-data centres hosting training systems for generative AI platforms.


While attention remains fixed on these behemoths, the true revolution is happening at the edge, where AI meets real-world applications across every industrial and commercial sector.

Beyond Generative AI: The Diverse Ecosystem of Industrial Applications

The narrative around AI has matured significantly since 2024. While generative AI platforms continue to capture headlines, the practical implementation of AI technologies spans a far more diverse ecosystem.


As Duncan Clubb, Senior Partner at Cambridge Management Consulting, notes, "Too much emphasis has been given to prominent large language models, when in reality, the market requires a more diverse model for deploying infrastructure that supports real-world applications."


Today we're seeing this diversity play out across multiple sectors. Industrial and manufacturing applications leveraging AI for optimisation have moved beyond early adoption into mainstream implementation. Gartner now predicts that 75% of enterprise data will be processed at the edge by the end of this year, a dramatic increase from just 10% in 2018. The Financial Services industry is also beginning to use edge technology for real-time fraud detection. This shift reflects a fundamental transformation in how businesses approach data processing and analysis.


Consider the manufacturing sector, where AI systems connected to production line sensors now control processes in real-time, improving efficiency and reducing waste. One compelling example highlighted by Clubb involves the automobile industry's use of specialist adhesives: "An AI platform has been in use to reduce the amount of glue used without compromising the efficacy of the bond. This may sound trivial, but the quantities used globally mean that even small proportional savings can amount to huge monetary savings."


Similar applications exist across healthcare, transportation, retail, and agriculture, where edge-based AI is providing unprecedented operational insights and efficiencies. According to recent projections, spending on edge computing technologies will reach $378 billion by 2028, demonstrating the critical importance these technologies have assumed in our digital infrastructure.

The Evolution of Edge Solutions

While large enterprises have leveraged AI for process optimisation for years, many smaller businesses have been excluded due to high barriers to entry. The ideal solution would be cloud-like services for AI-driven applications, but traditional cloud offerings from providers like Amazon, Microsoft, or Google often prove unsuitable for industrial applications.


"For most applications, the reason is either the amount of data that needs to be processed – it costs a fortune to transport the humungous amounts of data generated in production lines to where it could be processed – or the network latency is too long," explains Clubb. "Real-time control of industrial processes requires extremely fast networks."


The solution lies in distributed cloud-like infrastructure, positioning processing power near the companies or users generating or consuming data. This defines the current direction and rationalisation for edge computing.


"For me, it's quite simple," Clubb states. "The edge is where data processing needs to happen. That need is either defined by the sheer quantity of data that needs to be processed or the latency requirements."

The Edge Landscape: Smaller, Distributed, and Powerful

The infrastructure requirements for edge AI differ significantly from those of large language model training platforms. Edge AI data centres can be smaller and easier to build than their hyperscale counterparts, and they typically don't require the extreme power densities that training systems demand.


However, the key difference is quantity – we need many more of them. As Clubb observes, "Having access to an edge data centre within 20-40 km will normally be sufficient for many applications, but this means that we will need to build possibly hundreds of new (small) data centres to cover a country the size of the UK."


This presents both challenges and opportunities. According to research from Vertiv, the densification of computing workloads is driving innovation in power and cooling solutions. "AI applications demand increasingly efficient systems to manage the power requirements and thermal output of high-performance computing. Inferencing at the edge provides critical benefits such as reduced latency and enhanced security, making it an essential strategy for managing AI workloads efficiently in edge deployments."


The integration of 5G networks has further accelerated this trend, with edge deployments now capable of reducing latency to under 2 milliseconds – crucial for applications like autonomous vehicles and smart healthcare systems.

Meeting the Power Challenge

The exponential growth in data centre deployment brings with it significant energy demands. In 2025, the data centre industry faces intensifying power transmission challenges that threaten to delay development timelines.


"Energy availability is becoming a major concern as the demand for compute capacity grows and power densities increase," notes Vertiv's assessment of 2025 trends. "At the edge, this challenge is particularly pronounced due to distributed locations with varying access to power. AI applications further complicate this issue, as they require consistent and scalable energy sources to maintain operations."


These challenges are driving innovation across the sector. Companies are increasingly adopting renewable energy integrations, high-efficiency power systems, and alternative energy sources. For edge deployments specifically, local energy solutions like microgrids and battery storage systems are gaining traction to ensure uninterrupted operations even in remote or underserved areas.


Notably, 2025 has seen an acceleration of small modular reactor (SMR) announcements, with the total gigawatt capacity likely to double by year-end. "Nuclear power is emerging as a preferred solution to meet growing energy demand. As traditional power grids struggle to keep pace, the sector is exploring both traditional large-scale nuclear power and small modular reactors (SMRs)."

Building for a Distributed Future

The construction sector has responded robustly to these evolving demands. What started as a relatively niche sector a few years ago has transformed into a powerhouse of construction activity. Data centres continue to drive significant growth in nonresidential construction planning, with these projects contributing to a 19% increase in planning activity since December 2023.



The growth shows no signs of slowing. J.P. Morgan estimates that spending on data centres could add between 10 to 20 basis points to U.S. economic growth in 2025 and 2026. This growth extends beyond the United States, with global players making substantial investments in core and edge infrastructure.


The major tech giants companies are likewise doubling down on their data centre investments and exploring new energy solutions. Amazon is planning to invest $150bn over the next 15 years on infrastructure to handle the expected demand for artificial intelligence and other cloud computing needs.


Recently, it was reported that Google has signed its first corporate geothermal energy agreement in Taiwan with Baseload Capital, adding 10 megawatts of continuous power to support its local data centres. 


Announcements such as these show market-wide confidence in the projected growth in demand, as well as growing interest in renewable energy sources.

Conclusion: The Dual Future of Data Infrastructure

As we prepare for Cambridge Tech Week 2025 (September 15-19), where ‘Seizing the AI Advantage’ will be a central theme, it's clear that the future of digital infrastructure depends on both centralised and distributed computing models working in harmony.


Duncan Clubb summarises this dual approach eloquently: "For the data centre industry, I think that is just as exciting as the need to build the behemoth data centres at the core – the truth is, we need both."


The AI industry is now a reality demanding immediate attention and action. As businesses navigate this rapidly evolving landscape, those who understand and leverage the symbiotic relationship between edge computing and artificial intelligence will be best positioned to thrive.


Cambridge's deep-tech ecosystem is at the forefront of this revolution, developing innovative solutions to address the challenges of power, cooling, and infrastructure that accompany the growth of AI and edge computing. The conversation at Cambridge Tech Week 2025 will undoubtedly shape how industries approach these opportunities in the years to come.


For organisations looking to harness the power of edge AI, the message is clear: the future is distributed, the future is intelligent, and the future is now.


If you would like to know more about our Data Centre, Edge and Cloud services, please get in touch with Duncan by email or use the Contact Form below.

Select References

Contact Form

Contact - Craig Devolution Blog

Subscribe to our Newsletter

Blog Subscribe

SHARE CONTENT

Neon letters 'Ai' made from stacks of blocks like a 3D bar graph
by Darren Sheppard 4 December 2025
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
Sun through the trees
by Scott Armstrong 26 November 2025
Nature means something different to everyone. For some, it is a dog-walk through the park; for others, it is hiking misty mountains in Scotland, swimming in turquoise waters, or exploring tropical forests in Costa Rica.
Aerial view of Westminster, London.
by Craig Cheney 25 November 2025
With the UK Budget being published tomorrow, councils are facing intense financial pressure. Rising demand for adult and children’s social care, homelessness services, and temporary accommodation has left little room for manoeuvre.
by Cambridge Management Consulting 20 November 2025
Press Release
Lightning strike in dark sky
by Scott Armstrong 17 November 2025
Non-commodity charges are driving UK energy costs higher. Discover what’s changing, why it matters, and the steps businesses should take to protect budgets | READ NOW
Futuristic building with greenery growing out of it.
by Cambridge Management Consulting 10 November 2025
Over the last few decades, carbon offsetting has become a go-to strategy for businesses looking to demonstrate sustainability commitments and enhance their external credibility. Offsetting takes many forms, from tree planting and forest conservation to providing communities with clean cookstoves and renewable energy.
Aerial view of solar panels in a green field.
by Drew Davy 7 November 2025
In today's rapidly evolving business landscape, Environmental, Social, and Governance (ESG) factors have moved from niche considerations to critical drivers of long-term value, investor confidence, and societal impact.
Two blocks of data with bottleneck inbetween
by Paul Brooker 29 October 2025
Read our article on hidden complexity and find out how shadow IT, duplicate tools and siloed buying bloat costs. See how CIOs gain a single view of IT spend to cut waste, boost compliance and unlock 5–7% annual savings | READ FULL ARTICLE
More posts