The Role of KPIs in a Post-Pandemic Landscape

Jon Wilton

The difference between metrics and KPIs

A KPI is a form of metric. Metrics are any measurement that tracks a specific business activity or outcome. KPIs help and guide organisations toward their goals. To put it a slightly different way, KPIs are strategic and align with business goals, whereas metrics are tactical (they just tell you something useful).

To function effectively, KPIs must be linked to your company’s strategic goals. They should then be broken down into KPIs both vertically (at each business level) and horizontally (by department for example). Each department should only deal with a handful of carefully selected KPIs.

The first lesson of KPIs: the map is not the territory

No matter how good your data is, it is always a slice of reality. Sometimes managers get too fixed on data, on shortfalls, on targets. Why was productivity lower this month? Rather than looking for wider issues and trends, they might just wave a finger. In her Ted Talk on the downfall of Nokia (www.ted.com/talks/tricia_wang_the_human_insights_missing_from_big_data?language=en), Tricia Wang, an ethnographer, noted how big data failed the former mobile phone giant. Nokia relied heavily on quantifiable data about the here-and-now, and the company failed to see the sweeping changes on the horizon: the smartphone in your pocket. 


This article, among other things, sets the human factor alongside digital. One must not supplant the other.

Get the basics right: if you are a senior leader or manager setting KPIs, make sure your company adopts these rules

  • Make sure your KPIs are realistic. If your KPIs are unrealistic you risk putting huge pressure on teams who will potentially manipulate the metrics or take damaging shortcuts as the only means of success. 
  • Always attach context and human insight to your KPIs.
  • Choose a handful of focussed KPIs. Too many KPIs leads to confused messaging and an inability to separate wheat from chaff. It is overwhelming for both management and staff.
  • Make sure KPIs at every level feed into the overall strategic vision. 
  • Share information about high-level goals, what the current levels of performance look like, how should it be and why, and how the future looks. Make sure employees feel connected to your high-level KPIs.
  • Include employees in setting departmental KPIs. 
  • Ask employees to set their own KPIs.
  • Too often KPIs fail because they can’t be measured with a meaningful or unbiased method. Make sure your KPIs have a quantifiable outcome. The exceptions to this rule are Value-driven KPIs and CPIs (see below).
  • If you have the resources to do so, include Value-driven KPIs and CPIs (see below).
  • Don’t focus on the system over the people. This is a golden rule and something we talk about a lot at Cambridge Management Consulting.


Be careful: KPIs can make things worse

Unfortunately, it’s still commonplace to find KPIs that track tasks completed per X (where X is a period of time, usually a day, week or month), with little distinction made between complex and straightforward tasks. While averages taken over a longer period can be useful, this is a simplistic and demotivating way to compare employees and usually ends in corner-cutting or manipulation of stats. Senior management should be careful pressing for data on cost-to-serve (i.e. how much does it cost on average to serve a client) if, for example, the tasks being measured vary considerably in duration depending on complexity.


Another common fault in the design of KPIs is creating data just for the sake of it. There is occasionally an obsession with producing data sets without asking whether these metrics are actually useful and add value. As we noted earlier, what distinguishes KPIs from other metrics is their alignment with and contribution to the strategic goals of the company.


Setting individual KPIs at annual reviews—when this is done wrong—can be extremely demotivating. Some leaders think they have skirted this problem by asking employees to set their own targets, but this can be either a positive or negative experience depending on a variety of factors. There is a tendency to do these things off a checklist and fulfil the pantomime of ‘set and forget’. In times of Covid-19, with workplaces changing rapidly, it might be necessary to review KPIs more frequently. With remote-working now the norm, KPI appraisals over video-chat should be delayed if there are more pressing problems in your team.


To make KPIs successful, you need to build a positive culture within your team first and foremost. Make sure your team are engaged and motivated and have the digital tools necessary to perform well in a remote environment. Once you have this foundation, you can set team KPIs, making sure they are realistic and can be measured without bias. It is too easy for managers to hide behind a KPI dashboard, managing stats and not people. 


Include your team in setting these goals and demonstrate how they fit into the overall strategic vision; also agree how they should be measured. Then set KPIs per role, also with contributions from employees. Whether you should set KPIs per role or per individual depends on your business, how your team functions best and your leadership style. If setting KPIs per individual, be careful that you do not seem to be punishing someone unjustly or forcing individuals to compete against one another. 


At both team and role level, consider setting Value-driven KPIs (see below). This could also be summarised as ‘don’t lose sight of quality’ since quality is generally underrepresented in metrics. The immediate rebuff is to say quality is hard to measure, which it is (and this was one of our KPI rules: stick to what you can measure), but if you have the resources to do so, produce KPIs for quality and values using ethnographic methods and digital tools. 


KPIs and remote-working

We’ve recently seen businesses struggle with communicating a message of relaxing productivity—while their employees juggle work and home schooling—while also doubling down on departments that are underperforming. There are conflicting views about whether remote working in good or bad for productivity. JP Morgan Chase made the news when the CEO Jamie Dillion stated in late 2020 that no ‘creative combustion’ was occurring in video meetings. He voiced his intention to get employees back to offices as soon as possible. Yet, when surveyed, employees regularly report their productivity as equal or higher while they are remote working.


Despite the lack of good data on productivity levels, the function of KPIs doesn’t necessarily change in a remote environment. However, if your team is struggling, or if certain members of your team are underperforming—perhaps with other team members being forced to take up the slack—, it might be worth changing to a more value-based KPI system for the time being. Switching to broader targets across the team might allow you to concentrate efforts on morale or the introduction of digital tools without going into the ‘red’ on your KPIs. 


The benefits of including Value-driven KPIs in your metrics

To avoid some of the pitfalls of using performance indicators for your team, make your targets value-driven—connect them to value behaviours or make some of your higher-level goals value-based. This should connect up with company-wide Values and commitments to devote time/energy/money to social causes. In the third sector these kinds of Value-driven KPIs are being discussed in white papers and touted as the future of performance management. However, the idea is also making its way into the private sector and it can be recontextualised in a number of ways.


Leaving aside the high-level Value-driven KPIs for now, what does a Value-driven KPI look like at an individual or team level? The emphasis is on soft skills: creating positive behaviours such as teamwork, creativity, going the extra mile for customers/clients and so forth.   


One of the smaller transitions might be changing from a score-based appraisal system to a list of competencies—ironically, it is often the case that organisations switch from a list of competencies to a score-based system to gather better data across the business! 


The obvious advantage of using Value-driven KPIs is that they will more closely mirror the real-life values that employees are motivated by and which they hold about their jobs and lives. The downside is cost and time. Companies that wish to make these performance indicators measurable (and, of course, they must be measurable to function as metrics) must invest in ethnographic tools and methods, and train their management team accordingly. This can be an expensive and time-consuming.


It is interesting to note how this paradigm shift has already started. Automated surveys, using a Likert scale, are regularly sent out by companies and they are an attempt to obtain the sort of qualitative data that tells a business how it is performing against its own set of values (it should also be noted that sometimes this data has a different agenda). 


Value-driven KPIs are part of a much greater cultural shift occurring across the business landscape, one in which values are central to strategic vision. It also relates to the Jeff Bezos mantra of designing everything you do around customer expectations—which leads us to Customer Performance Indicators (CPIs), as discussed below.   


Coaching and KPIs

B&W image of coach and kids in basketball team joining hands

There is huge value in coaching for leaders when it comes to the introduction and management of KPIs, and targets in general. As we have already shown, the way in which targets are discussed and the whole culture around them is key to their success. The human factor weighs in heavily. Some managers have the communication skills necessary and can build motivated teams around a healthy adoption of metrics. For others, they may feel weighed down by data and how to implement it without creating the familiar disconnect between management and team (the cold, calculating spreadsheet vs. the emotional human). When you think of what you value about your job, how many of those things are represented in your metrics? 


For leaders struggling with the complex soft skills to navigate human and digital, coaching can revitalise the relationship dynamic between you and your team. Take a look at Cambridge Management Consulting’s Leadership services to find out how we can help with this central tenet of performance management.   

A new metric: Customer Performance Indicators (CPIs)

As a disclaimer, we should first mention that businesses should think extremely carefully before introducing more KPIs. You may already have Critical Success Factors (CSFs) and Key Risk Indicators among many other metrics. However, our article ends with a necessary and timely discussion of Customer Performance Indicators (CPIs). These relatively new performance metrics place the customer experience at the heart of your success as a business: your client or customer is the measure of your successful outcomes. 


Think about it this way. High-level KPIs such as tracking revenue measure your customers in terms of what they produce for your company. CPIs turn this around and measure the business in terms of what it produces for customers/clients. 


The first stage is to find out what your customers/clients value most when they buy your product or use your service. This requires ethnographic tools combined with surveys and questionnaires or focus groups to gather and analyse qualitative data. The most effective method is contextual inquiry, an ethnographic research method where specially trained researchers speak with and observe customers as they think about or try to achieve specific outcomes.


Once complete, these ethnographic methods reveal a mixture of Value-driven and quantifiable metrics (which can be captured by your KPIs). A Value-driven CPI might relate to how a customer views reputation or environmental impact before making a purchase, whereas a more straight-forward, quantifiable CPI might simply be the time it takes for you to reply to an enquiry or how long it takes to deliver a product. Once again, we can see how Amazon has totally dominated the retail market by concentrating on CPIs and ensuring all of their KPIs deliver on those targets.


Once you have identified your CPIs, you can link them up with your strategic goals, and then to your internal KPIs and Value-driven KPIs. CPIs are sometimes recommended as the best measure of growth, because your customers/clients are the biggest levers when it comes to generating revenue. 


If you are thinking about implementing CPIs, it is a complex undertaking which requires significant planning and resources and external expertise. But it almost certainly is worth the effort for medium to large businesses who want to scale while shifting their focus to core values and a customer-centric outlook.


“We’ve had three big ideas at Amazon that we’ve stuck with for 18 years, and they’re the reason we’re successful: Put the customer first. Invent. And be patient.”



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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
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