Where Should Leaders Really Start with AI in 2026?
Simon Crimp
KEY TAKEAWAYS
- AI adoption is now mainstream across Europe and the UK, but most large organisations remain stuck in pilots rather than delivering scaled, measurable impact on financial and risk metrics.
- The real starting point for AI in 2026 is explicit board-level clarity on risk appetite, governance, and accountability – not another isolated proof of concept.
- Sustainable AI value comes from treating it as a socio-technical system, with joined-up ownership across product, risk, operations, data, and HR rather than confining it to IT or innovation teams.
- Organisations that define business outcomes first, assess data fitness and exposure rigorously, and run tightly scoped, measurable pilots are far more likely to move from experimentation to a repeatable AI production model.
4 MIN READ
Introduction
Business and technology leaders in larger organisations are starting 2026 with a familiar tension: AI is everywhere – pilots, proofs of concept, demos – but scaled, measurable value is still the exception rather than the rule.
Across Europe, AI adoption is now firmly mainstream. By 2025, around one in five EU enterprises with 10+ staff were using some form of AI, up from just 8% in 2023, with Nordic markets already above a third of firms using AI. In the UK, Office for National Statistics data shows that roughly a quarter of all businesses – and over 40% of larger firms with 250+ employees – report using AI tools, with a further wave planning adoption in the next few months.
Yet despite this momentum, most larger organisations are still stuck in pilots and localised experiments, not in fully scaled, governed capabilities. The question for boards and executive teams in 2026 is no longer "Should we use AI?" but "Where should we really start if we want impact we can see on the P&L, the balance sheet, and risk metrics?
Start by Deciding Your Risk Appetite
In bigger businesses – especially regulated or multi-country organisations – the question of when to move from pilots to scale is fundamentally a question of risk appetite.
Moving early brings higher uncertainty but also the chance to define the edge in your market: better customer experiences, faster cycle times, or radically different cost and risk profiles. Waiting allows you to borrow proven patterns – but risks leaving the field open to nimbler competitors or new entrants.
For boards, this is not a purely technical decision. It is a governance decision that should sit alongside capital allocation and major transformation commitments. That means getting clear on:
- Which risk domains matter most (e.g. conduct, cyber, data protection, model risk, operational resilience)
- Where you are prepared to experiment, and where you need stronger controls from day one
- What "unacceptable" looks like – for customers, regulators, and your brand
Once this is explicit, your AI roadmap becomes a risk-shaped roadmap, not a list of disconnected experiments.
Treat AI as a Socio‑Technical System
In large, complex organisations, AI does not live in isolation. Models sit inside products, processes, supply chains, and front‑line workflows. Their behaviour is shaped as much by people, incentives and data governance as by algorithms.
That has two consequences for where you start:
- You cannot fully outsource the risk. Even if you use third‑party models or platforms, regulators and customers will still hold you responsible for outcomes.
- You need joined‑up ownership. AI initiatives that sit only in IT or innovation labs rarely get to scale. You need product, risk, operations, data and HR in the room from the outset.
Frameworks such as the NIST AI Risk Management Framework and emerging EU AI governance requirements are useful because they force you to think across the full lifecycle – from design and data sourcing through deployment, monitoring, and retirement – rather than treating AI as a one‑off technology decision.
Put Basic AI Governance in Place Before Chasing Use Cases
For larger organisations, the real starting line in 2026 is governance, not tools.
Before you launch another wave of pilots, ensure you have a robust and coherent governance framework in place. At minimum:
- AI policy – what is allowed, what is prohibited, escalation routes, and who can approve higher‑risk experiments.
- AI risk assessment template – ideally aligned to an established framework such as NIST AI RMF, so teams have a common language for impact, likelihood and controls.
- A model and data register – a live inventory of AI systems, data sources, critical dependencies, and accountable owners.
- Clear expectations for AI literacy and training – especially important for EU‑facing organisations, where regulatory expectations on competence and oversight are rising.
In practice, many larger organisations already have fragments of this in model risk, information security, and data protection. The opportunity in 2026 is to connect these into a visible, usable framework that lets business teams move faster because governance is clear, not in spite of it.
Define Value First – Then Choose the Technology
A recurring pattern in both UK and global surveys is that organisations struggle less with access to AI tools and more with identifying where those tools genuinely move the dial. In one recent UK study, difficulty finding meaningful use cases was cited more often than cost or skills as a barrier to adoption.
For larger organisations, the fastest way to dilute credibility is to start with a model or vendor and then go hunting for somewhere to point it. GenAI in particular can feel like a technology in search of a problem – part of the reason so many proofs of concept never cross into production.
Instead, reverse the logic:
- Start from outcomes. Decide which outcomes matter most in the next 12–24 months – e.g. unit cost, time‑to‑serve, risk‑weighted assets, customer retention, regulatory capital, or resilience metrics.
- Break outcomes into initiatives. Translate those outcomes into a shortlist of initiatives with clear measures of success.
- Pick the right approach per initiative. For each, decide whether GenAI, classic analytics, automation, or process redesign is the right choice – or a hybrid – and what "good" looks like in plain business language.
Only then should you ask which AI tooling, platforms, or partners you need.
Get Serious About Data Fitness and Data Exposure
For any sizeable organisation, the gating question for AI is not "Do we have data?" but "Is our data good and safe enough to use at scale?"
A simple way to operationalise this is to use a Data Readiness & Safety scorecard before any new pilot or scaled deployment. Keep it high level, score each dimension 1–5, and agree thresholds for go, go with mitigations, or no‑go.
Two dimensions are usually enough to start:
- Data fitness (is it good enough to deliver value?) – quality and accuracy, completeness, freshness, consistency, and whether you can trace and maintain the data over time.
- Data exposure (is it safe enough to use?) – sensitivity (e.g. personal or commercially confidential data), rights and permissions, access controls, auditability, and the likely harm if the model is wrong or information leaks.
Treat access control, permissions, and auditability as gating items: if they are weak, you redesign or stop the pilot. This aligns with the direction of travel in UK and EU regulation, where boards are expected to evidence control over high‑impact AI systems, not simply innovation.
Design Your First Enterprise AI Pilots to Be Boring and Measurable
Once you have governance, value hypotheses, and data clarity, you are ready to run the kind of pilots that actually scale.
In larger organisations, the first wave of serious AI pilots should feel almost boring in scope: obvious value, obvious metrics, and tightly controlled risk. For example:
- Reducing call‑handling or case‑handling time in a high‑volume service process
- Accelerating document review or drafting in legal, compliance, or finance teams
- Improving triage and routing for incidents, leads, or customer requests
For each pilot:
- Define success in plain language. Time saved, error rates reduced, fewer hand‑offs, higher conversion, or NPS – and capture a baseline before you start.
- Set up a small, accountable team. Blend product, operations, risk, and data/engineering; keep access and permissions tight.
- Build human guardrails in from day one. Approvals, review steps, or escalation paths where impact is high – not bolted on after launch.
- Make monitoring part of the design. Systems drift, user behaviour changes, and external data shifts.
- Monitoring for performance, bias, and security should be routine, not a special project.
At the end of each pilot, make a single decision quickly: scale, iterate, or stop – and document the learning so the next use case starts from a stronger base.
From Scattergun Experiments to a Repeatable Production Line
By 2026, the differentiator between organisations that 'dabble' in AI and those that build a durable advantage is rarely the underlying model. It is whether AI is treated as a managed change programme with:
- Disciplined delivery and portfolio management
- Clear governance and risk ownership
- Metrics that link directly to business outcomes, not just usage
For larger organisations, the most pragmatic path is to start small, but start deliberately:
- Put basic AI governance and a data readiness lens in place.
- Focus on a handful of use cases tied directly to strategic outcomes.
- Run them through a repeatable production line: assess, pilot, test properly, scale only what works, and measure ROI against a clear baseline.
Done this way, AI stops being a patchwork of disconnected initiatives and becomes a feedback loop that consistently improves your time‑to‑value at enterprise scale.
AI Strategy Stress Test for Larger Organisations
If you’d like an external view on where to start, Cambridge Management Consulting runs a short AI Strategy Stress‑Test for larger and mid‑market organisations.
In a series of focused sessions, we look at your current AI experiments, data foundations and governance, then highlight use cases where AI can create real value with manageable risk – using the teams and platforms you already have. We also help you frame an achievable 6–12 month roadmap that balances ambition with control.
If you’d like to benchmark where you are today – and what a sensible next stage of AI adoption could look like for your organisation – we’d be happy to talk.
About the Author
About Us
Cambridge Management Consulting (Cambridge MC) is an international consulting firm that helps companies of all sizes have a better impact on the world. Founded in Cambridge, UK, initially to help the start-up community, Cambridge MC has grown to over 200 consultants working on projects in 25 countries. Our capabilities focus on supporting the private and public sector with their people, process and digital technology challenges.
What makes Cambridge Management Consulting unique is that it doesn’t employ consultants – only senior executives with real industry or government experience and the skills to advise their clients from a place of true credibility. Our team strives to have a highly positive impact on all the organisations they serve. We are confident there is no business or enterprise that we cannot help transform for the better.
Cambridge Management Consulting has offices or legal entities in Cambridge, London, New York, Paris, Dubai, Singapore and Helsinki, with further expansion planned in future.
Contact Form
Subscribe to our Newsletter
Blog Subscribe
SHARE CONTENT
We were approached by one of the fastest growing data centre providers in Europe. With over 20 data centres throughout the continent, they are consistently meeting the need for scalable, high-performance infrastructure. Despite this, a key data centre in Scandinavia had become reliant on a single, non-redundant 1 Gbps internet service from a local provider, posing significant risks to operational continuity. To enhance the reliability of its network and resolve these risks, our client needed to establish additional connectivity paths to ensure the redundancy of its infrastructure. The Ask Cambridge Management Consulting was engaged to address these connectivity challenges by identifying and evaluating potential vendors and infrastructure options to create second and third connectivity paths. This involved exploring various types of connectivity, including internet access, point-to-point capacity, wavelengths, and dark fibre. Additionally, Cambridge MC was asked to provide recommendations for building a local fibre network around the data centre to control and maintain diverse paths. This would allow the data centre to connect directly to nearby points of presence (PoPs) and reduce dependency on external providers, thereby enhancing network resilience and operational control. The goal of this project was to ensure that the Nordic data centre could maintain continuous operations even in the event of a failure in the primary connection. Approach & Skills Cambridge MC approached the project with a focus on ensuring operational continuity and resilience for the data centre. By identifying multiple connectivity paths, we aimed to mitigate the risk of network failures and ensure that the data centre could maintain continuous operations even in the event of a failure in the primary connection. This approach allowed Cambridge MC to provide a comprehensive solution to address both immediate and long-term connectivity needs. We employed a combination of Agile and Waterfall methodologies to manage the project. The initial investigative phase allowed a Waterfall approach, in which our team conducted thorough research and analysis to identify potential vendors and connectivity options. This phase involved detailed interviews with various telecommunications providers and an assessment of publicly available information. Once the initial analysis was complete, the workflow transitioned to an Agile approach for the implementation phase. This allowed Cambridge MC to adapt to new information and feedback from stakeholders, ensuring that the final solution was both flexible and robust. Challenges Lack of information: One of the primary obstacles we faced was the lack of detailed network maps and information from some of the potential vendors. To overcome this, the team conducted extensive interviews with contacts at these companies and leveraged its existing network of industry contacts to gather as much information as possible. Remote location: Another challenge was the remote location of the data centre, which limited the availability of local infrastructure and required us to explore creative solutions for connectivity. Cambridge MC addressed this by proposing the construction of a local fibre network around the data centre, which would allow for greater control and flexibility in connecting to nearby PoPs. Fragmented factors: Additionally, coordinating with multiple vendors and ensuring that their services could be integrated seamlessly posed a logistical challenge. We mitigated this by recommending a phased approach to implementation, starting with the most critical connectivity paths and gradually expanding to include additional options. Outcomes & Results Increased Connectivity: Cambridge MC successfully identified and evaluated multiple connectivity paths for the data centre. By exploring various types of connectivity, including internet access, point-to-point capacity, wavelengths, and dark fibre, we provided a comprehensive solution that significantly enhanced network resilience and reliability. Greater Control & Flexibility: Our recommendations for building a local fibre network around the data centre allowed for greater control and flexibility in connecting to nearby points of presence, ensuring continuous operations even in the event of a failure in the primary connection. New Vendors: The team’s extensive network of industry contacts and deep understanding of the regional telecommunications landscape allowed for a thorough and nuanced evaluation of potential vendors and connectivity options. Scope for Future Work: Cambridge MC identified several future developments with the potential to further enhance international connectivity and provide additional redundancy for the data centre. We also proposed further assistance, including a site visit for a more in-depth analysis of options, issuing RFI/RFP to vendors for capacity and fibre, and conducting similar connectivity studies for other candidate sites in the region.
Thames Freeport this week revealed the eight companies selected to participate in the Freeport’s Connectivity Lab, an initiative focused on validating commercially proven technologies in live port and logistics environments.
Emerging as a hub for innovation, Thames Freeport is a unique initiative designed to stimulate trade and transform the lives of people in its region. Leveraging global connectivity and occupying a strategic position with intermodal capabilities across river, rail, and road, Thames Freeport has recognised its opportunity to drive economic regeneration for the local area. Thames Freeport engaged Cambridge Management Consulting to design a clear strategy for innovation over the next three to five years. Key considerations for this innovation strategy included objectives and KPIs, the future of the business ecosystem in the region, physical clusters and assets such as innovation hubs, and opportunities and challenges on the way. The Solution Working with our innovation partner, L Marks, Cambridge MC conducted an innovation strategy project which involved the following: Engaging with a range of stakeholders and partners from local authorities to corporate partners across the Thames Freeport area, leveraging interviews with key individuals to build a common picture of innovation aspirations, opportunities, and challenges. Conducting a series of workshops for the Thames Freeport team to consider visions and objectives, themes and focus areas, physical hubs and overall programme structure, and a three-year roadmap plan. Building a comprehensive innovation strategy which internalised all of the above questions. This was then presented to their board and formed the basis of the public tenders for innovation programmes that were then made public.
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
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.
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.
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
