How to achieve ultra-reliable networks in 2022

Charles Orsel Des Sagets


Subscribe Contact us

Authors


The challenges of delivering low latency and 5G


The pandemic has brought into sharp focus the need and importance of reliable and flexible networks at home. The switch to remote working and the rapid normalcy of meetings from our desks at home, brought with it surges in internet traffic and demand for reliable, stable connections.


Networks across Europe coped well, for the most part, but there are questions hanging over the near-horizon, about how carriers will adapt and scale for both a growing remote workforce and the predicted rise of new technologies.


5G is part of the answer (but only a part) and its development and rollout will coincide with and drive innovations in IoT, autonomous vehicles, and AI. These emerging technologies also exist within larger tectonic shifts in society and culture, including increasing digitalisation, virtualisation and autonomy in services; and the beginnings of the decentralised application of blockchain technology.


As our society embraces digitalisation, and the process is accelerated by Covid-19, we ask, what are the major challenges faced by carriers heading into 2022 to deliver on twin fronts of both infrastructure demand and customer expectation?


We will discuss the issue of low-latency, particularly in meeting customer expectations. These expectations anticipate the demands of video-centric content, remote working, IoT and gaming. We will also explore metrics for customer experience, and conclude with the impact of 5G technology.

Exposed end of a cluster of optical fibres with neon glow

Measuring Quality of Experience for internet connectivity 


With a surge in internet traffic during lockdowns potentially being the start of a sustained uptick in demand, including online games and growing markets for streaming games and VR, there is a spotlight on the issue of latency as a key indicator of customer expectation. 


Let us first, talk more broadly about indicators of network quality in 2021 and beyond.


Digital Equality - The widening speed gap in Europe


Europe’s internet speeds have increased by more than 50% in the last 18 months. However, this comes at the cost of widening gaps between urban and rural areas and also between Northern European countries and South-Eastern Europe.


The UK too lags behind much of its Western European neighbours when it comes to average internet speed. It was placed 47th on the list in a study conducted in 2020. In fact, the average broadband speed in the UK was less than half that of the Western European average.


The EU has a stated goal to be the most connected continent by 2030. It has already taken action to do this by ending roaming charges and introducing a price-cap on inter-EU communications. The key goal is for every European household to have access to high-speed internet coverage by 2025 and gigabit connectivity by 2030.


The elevation of internet access as a necessary human right is of course encouraging, and so are the targets set by the EU. However, for these targets to be truly meaningful, there needs to progress on a number of challenges to connectivity across Europe. Redefining the metrics we use to track this progress is also vital.

Neon outline of cloud with apps and light emanating

Measuring Quality of Experience (QoE)


There are a variety of problems with measuring internet speeds in a comparable way. Usually, ISPs present averages across a range of time in Mbps or sometimes the % of plan speed achieved across a range of time. 


As bandwidth in many countries in Europe moves towards, and over, 100Mbps, this proxy is becoming a weaker indicator of user experience. 


There are also a number of key reasons why figures published by an ISP might be misleading compared to actual user experience.


Some of these problems are as follows:


  • Lab-testing of internet speed does not replicate the real-world chain of devices/hosts involved in sending and receiving packets


  • Averages of Mbps ignores speeds at peak periods when the network is congested and networks throttle bandwidth


  • The ‘plan speed’ does not reflect actual speeds experienced in a household where packet queuing and WiFi congestion and network affects users differently on the customer LAN


  • This metric ignores latency, which is becoming a better signal of internet experience in an age of video streaming and online gaming (more on this below)


Many voices in the industry are pushing for more holistic Quality of Experience (QoE) metrics to enlarge the current set of Quality of Service (QoS) measurements.


The difference between QoE and QoS is that the latter method is comparable to measuring the success of a call centre by how many calls are concluded in a given day. This metric completely ignores whether a caller’s problem was sufficiently resolved or how satisfied the caller felt about the interaction, the ‘experience’.


Research shows that users are happy when a website loads in under two seconds (QoE). If network management is calibrated with this information, bandwidth saved can be allocated elsewhere if necessary (QoS).


Thus, one characteristic of QoE is the realisation that there are many examples where a better QoS (above a threshold) does not readily impact the user’s perception/experience of the service.


This has some important ramifications in terms of design. For example, services such as online gaming rely on low latency far more than video streaming, where buffering protocols absorb lag. QoE can be used to design SLAs and network management that are specific to the needs of an individual service.


If network providers can achieve methods of gathering QoE data, it can be used to build Autonomic Network Management (ANM) capabilities that use artificial intelligence to allow networks to achieve even more efficient network performance that reacts in real-time to user experience. 


Low latency: jitter and packet loss


Bandwidth has generally been king in the history of communication networks. Low latency has generally lagged behind (pun intended) as a priority in the upgrading of networks.


From a QoE perspective, latency can be roughly defined as ‘the delay between a user’s action and the response of a web application’ – in QoS terms, this is the time taken for a data packet to make a round trip to and from a server (round trip delay).


Latency is affected by many variables, but the main four are:


  • Transmission medium: The physical path between the start and end points i.e. a copper-based network is much slower than fibre-optic.


  • Network management: The efficiency of routers and other devices or software that manage incoming traffic


  • Propagation: The further apart two nodes are in the network will affect latency. For every 100 miles of fibre-optic cable it is estimated this adds 1ms of latency


  • Storage delays: Accessing stored data will generally increase latency


There are two types of latency issue.


One is the ‘lag’ (delay) we defined above, and the other is ‘jitter’, the variations in latency that can make connections unreliable. Jitter is usually caused by network traffic jams, bad packet queuing and setup errors.


Impacting the QoE ‘perception’ of latency is also packet loss. Packet loss occurs when packets of data do not reach their intended destination. It is commonly caused by congestion and hardware issues —the issue can be more frequent over WiFi where environmental factors and weak signal are factors. The effect of packet loss is worse for real-time services such as video, voice and gaming. Packet loss is also worse in networks where there are no TCP protocols to retrieve and re-send packets that have dropped.


Why is low latency so important now?


All areas of business and private life rely more heavily today than ever before on digital applications. The latency-sensitivity of these applications is not only a hallmark of quality and guarantee of commercial productivity, but also – in critical use cases, a lifeline.

Ivo Ivanov, CEO of DE-CIX International


Recent technological innovations all tend to require lower latency. Cloud applications, mobile gaming, virtual/augmented reality, and the smart home rely on real-time monitoring and fast signal to action responsiveness. The growth of IoT and a world of interconnected sensors dictate that networks have a consistently low latency that is less than human reaction speeds.


  • Human beings: 250 milliseconds responding to a visual stimulus 
  • 4G latency: 200 milliseconds
  • 5G latency: 1 millisecond


Consider the safety implications when your car can react 250 times faster than you. At 100km/h the reaction speed of a human creates a reaction distance of 30m. With a 1 millisecond (1ms) reaction time, your autonomous car can break with a reaction distance of 3cm.

chart graph on latency

Latency and geography: the effect on user experience (UX)


The maximum affordable latency for a decent end-user experience with today’s general-use applications is around 65 milliseconds. However, a latency of no more than 20 milliseconds is necessary to perform all these daily activities with the level of performance that everybody deserves. Translating this into distance, this means the content and the applications need to be as close to the users as possible. Geographically speaking, applications like interactive online gaming and live streaming in HD/4K need to be less than 1,200 km from the user. But the applications that our digital future will be based on will demand much lower latency – in the range of 1-3 milliseconds. Smart IoT applications, and critical applications requiring real-time responses, like autonomous driving, need to be performed within a range of 50-80 km from the user.


How networks can reduce latency


There are a variety of ways of lowering latency. Businesses can pay for dedicated private networks and links that deliver extremely reliable and stable connections. This is also one of the few solutions that tackles performance gaps in the ‘middle mile’ (the network infrastructure that connects last mile (local) networks to high speed network service providers) of the internet.


Any service which uses the backbone of the internet will run into problems of inefficient routing due to:


  • Border Gateway Protocol (BGP) for routing (because it has no congestion avoidance)
  • Least-cost routing policies
  • Transmission Control Protocol (TCP): It is a blunt-tool protocol that reacts strongly to congestion and throttles throughputHow do I choose the right SD-WAN overlay?



Efficient network management


One other solution is offered by the latest breed of SD-WAN software. SD-WAN operates as a virtual overlay of the internet, testing and identifying the best routes via a feedback loop of metrics. Potentially SD-WAN can limit packet loss and decrease latency by sending data through pre-approved optimal routes. MPLS does something similar, labelling traffic to ensure it is dealt with on a priority basis; but this service is more expensive than SD-WAN and its architecture is not suited to cloud connectivity.


SD-WAN is a hybrid solution, meaning that the software overlay can route traffic over a host of networks, including MPLS, a dedicated line and the internet. WAN management also includes a host of virtualised network tools that optimise network efficiency. This includes abbreviating redundant data (known as deduplication), compression, and also caching (where frequent data is stored closer to the end user).


To find out more about the range of network infrastructure and SD-WAN services offered by Cambridge Management Consulting visit our capability page.

Infographic - 5G vs 4G spectrum

5G promises ultra-low latency


5G promises to lead us into a world of ultra-low latency, paving the way for robotics, IoT, autonomous cars, VR and cloud gaming. For this to become a reality, new infrastructure must be installed; this requires significant investment from governments and telecoms companies. Most countries need to install much more fibre to deal with the backhaul of data.


During the transition, the current 4G network will need to support 5G and there will be a combination of new and old tech, patches and upgrades to masts. Edge computing will eventually move data-centres closer to users, also contributing to lower latency. It could be many years before we see the kinds of low-latency connections that have been promised. 


How 5G and network slicing will end high-latency


With the fifth generation of cellular data, gigabit bandwidth should become the norm, and the frame length (the time waiting to put bits into the channel) will be drastically reduced. 5G moves up the electromagnetic spectrum to make use of millimeter waves (mmWave), which have much greater capacity but poorer propagation characteristics. These millimeter waves can be easily blocked by a wall, or even a person or a tree. Therefore, operators will use a combination of low, mid, and high range spectrum to support different use cases. 


The mid- to long-term solution to propagation restrictions is that 5G will require a network of small cells as well as the cell towers to support them (NG-RAN architecture). Small cells can be located on lampposts, sides of buildings, and also within businesses and public buildings. They will enable the ‘densification’ of networks, broadcasting high capacity millimeter waves primarily in urban areas. Because optical fibre may not be available at all sites, wireless backhaul will be a common option for small cells.


Edge computing will further support this near-user vision. Using off-the-shelf servers, and smaller data centres closer to the cell towers, edge computing can ensure low latency and high bandwidth. 

Infographic - 5G network structure with towers and cells


As latency requirements get lower and lower, it becomes more and more important to bring interconnection services as close to people and businesses as possible, everywhere. Latency truly is the new currency for the exciting next generation of applications and services.

Ivo Ivanov, CEO of DE-CIX International


What is network slicing?


The key innovation enabling the full potential of 5G architecture to be realised is network slicing. This technology adds an extra dimension by allowing multiple logical networks to simultaneously overlay a shared physical network infrastructure. This creates end-to-end virtual networks that include both networking and storage functions.


Operators can effectively manage diverse 5G use protocols with differing throughput, network latency and availability demands by ‘slicing’ network resources and tailoring them to multiple users.


What is realistic progress for 5G in 2022?


According to the California-based company Grand View Research, the global 5G infrastructure market size —valued at $1.9bn in 2019— is projected to reach $496.6bn by 2027.


There are however significant costs associated with 5G roll-out, as well as complications arising from planning regulations (for small cells in the UK alone, separate planning applications have to be files for each cell) and the need to alleviate public health fears about the technology.


There is still also the issue of digital equality (conquering the digital divide). There is a risk the divide could widen further if 5G services are concentrated only in cities, as economics will almost certainly dictate.


The EU recently announced their Path to the Digital Decade, a concrete plan to achieve the digital transformation of society and the economy by 2030.


Read more about the Path to the Digital Decade.


The European vision for a digital future is one where technology empowers people. So today we propose a concrete plan to achieve the digital transformation. For a future where innovation works for businesses and for our societies. We aim to set up a governance framework based on an annual cooperation mechanism to reach targets in the areas of digital skills, digital infrastructures, digitalisation of businesses and public services.

Margrethe Vestager, Executive Vice President for ‘A Europe Fit for the Digital Age’


5G has been dubbed by some as the next industrial revolution. If all the technologies that it intends to drive are realised within the next decade, that could certainly be the case. What is achievable in the short-term, however, is less clear and progress could be slowed by infrastructural barriers and rising costs.


As we head into 2022 there needs to be significant work to upgrade legacy systems to integrate with the rollout of 5G and an acceleration laying fibre optic cables to deal with the backhaul of data from the proliferation of 5G cells.


While 5G leads the technological improvement of the network, lowering latency at the network edge also needs to be a primary goal and operators must focus on latency as one element (albeit it a key element) of a holistic strategy to improve the mobile internet experience (and measure this against a robust QoE framework). 


Contributors


Thanks to Ivo Ivanov, CEO of DE-CIX International; Charles Orsel des Sagets, Managing Partner, Cambridge MC; Eric Green, Senior Partner, Cambridge MC; and Tim Passingham, Chairman, Cambridge MC, who all made contributions to this article. Special thanks to Ivo Ivanov, for his quotes.


Thanks to Karl Salter, web designer and graphic designer, for infographics.


You can find out more about Ivo Ivanov on LinkedIn and DE-CIX via their website.


Read bios for Charles Orsel des Sagets, Tim Passingham, and Eric Green.


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 24 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. 


Find out more about our telecommunication services and full list of capabilities

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