How do we identify the best AI use cases in our company?
Find valuable AI use cases with a practical discovery process, evidence-based scorecard, risk checks and clear pilot decisions for your business.
What is the quick answer?
Find the best AI use cases by mapping recurring work, measuring the current problem and checking information readiness, consequences and ownership. Compare AI with simpler changes and conventional automation. Prioritise a narrow task with meaningful value and a checkable output. Test the candidate with representative examples before committing to wider implementation.
Where should an AI use-case discovery process start?
Start with an operational objective that the business already cares about. It might be reducing the time required to prepare quotations, helping employees find approved procedures or shortening a queue of routine requests. Identify the current constraint and the person accountable for improving it. “Use AI across the company” is an aspiration, but it does not tell a team which activity to investigate first or what evidence would demonstrate progress.
Ask departments for work that is repetitive, variable and difficult to manage. Invite evidence rather than a list of products they have seen advertised. AI consultancy can help structure these conversations and separate a useful requirement from an appealing demonstration. Keep the question open: discovery may identify a conventional workflow, better information management or process redesign as the most effective answer. The aim is a defensible improvement, with AI considered where it has a clear role. Agree this objective before collecting a long list of possible tools.
↑ Back to contentsWho should help identify potential use cases?
Include people who perform the work, the process owner and colleagues responsible for the systems and information involved. Where a proposal affects customers or employees, involve those who understand these relationships and obligations. A management-only discussion may miss informal checks that keep a process working. A technology-only discussion may focus on what can be built without establishing why the business should build it.
Use short interviews or a practical workshop to map actual tasks. Ask what happens on an ordinary day, what creates a difficult case and how mistakes are discovered. Give employees a way to challenge assumptions afterwards. A quieter colleague may know that an apparently simple request depends on an unpublished exception. Record differing accounts instead of forcing immediate consensus. These differences provide evidence about process maturity. Resolving them can benefit the business whether or not an AI project proceeds, and may reveal preparation the original opportunity estimate did not include.
↑ Back to contentsWhich questions uncover useful opportunities?
Ask where people repeatedly read material to find a small amount of information, reformat similar documents or interpret differently worded requests. Explore delays caused by missing details, duplicated work and uncertainty about the next step. Then establish how often the task occurs, how long it takes and what a satisfactory result looks like. An opportunity becomes more concrete when its volume and accepted output can be described consistently.
Follow a promising answer with an example. If reporting is slow, find out whether the delay comes from extracting figures, reconciling them or writing commentary. Those are different problems. Digital transformation services are relevant where the discussion spans process and technology. Do not assume that the most visible frustration offers the largest benefit. Compare it with less conspicuous work that happens more often and has a clearer route to improvement. Ask how colleagues currently cope, because their workaround may reveal both hidden effort and an important control.
↑ Back to contentsHow do we map a process without overcomplicating discovery?
Draw a simple sequence from the arrival of an input to an accepted result. Name the person or application responsible for each step. Note the information used, the decisions made and common exceptions. Mark where the result is checked and who can authorise the next action. Begin with a shared document if that is sufficient; a specialist modelling tool is not a prerequisite for understanding the work.
Examine handovers. A task may be slow because information waits in an inbox, rather than because an employee needs help interpreting it. Routing, clearer ownership or a required field may solve the immediate problem. Power Platform consulting can support assessment of conventional workflow options alongside AI. Keep the current process separate from the proposed version so the change is visible. A useful map explains where AI would read, suggest or act, and where established rules and human decisions remain. Update the map when testing reveals a step previously taken for granted.
↑ Back to contentsHow can we decide whether AI adds anything useful?
Test the requirement against simpler alternatives. Could a form collect missing details? Could a template improve consistency? Could a fixed rule route the request? AI is more plausible when inputs vary in ways that require interpretation, such as free-text enquiries or differently structured documents. It is less compelling for exact calculations or actions already defined by a stable business rule. Explain the reason for choosing it in the use-case record.
Describe the smallest AI contribution that could help. Instead of an agent managing all purchasing, an initial use case might propose a document category while established controls validate and approve the transaction. This distinction focuses testing and exposes dependencies. Ordinary automation can handle much of the eventual workflow. Choosing a simpler method for part of the process may improve predictability and make it easier to explain. If no one can identify the particular step that needs AI, gather more evidence before spending time selecting a platform.
↑ Back to contentsWhat should a use-case record contain?
Create a concise record with a title, problem statement, process owner, users, inputs, proposed outputs and intended benefit. List systems, information sources, known risks and unresolved questions. Include what the system must not do. Finish with the next evidence-gathering action rather than jumping straight to a purchase. Keep the format short enough that departments will maintain it, and give every record a date and named owner.
For illustration, “assist service enquiry triage” could specify a proposed category and internal summary, with an employee confirming the route. It would exclude automatic refunds and customer commitments. A manager would own quality, while historical messages would support testing. This is a format example, not a reported client result. Consistency makes ideas comparable and prevents a broad aspiration becoming several uncosted projects. If a candidate has several different outputs or affected groups, consider splitting it into smaller records so that value, risk and ownership can be assessed separately.
↑ Back to contentsHow do we establish a reliable baseline?
Measure the current process before introducing the change. Record a representative sample of workload, handling time, corrections and delays. Define completion so one department does not measure a draft while another measures an approved result. Separate active work from time waiting for a decision or missing information. That distinction matters because drafting assistance may do little to shorten a delay caused by an approval queue.
Use existing records and supplement them with structured observation if necessary. Power BI services may help where measures are spread across applications. A carefully managed spreadsheet can support early discovery. Avoid treating an optimistic recollection as a verified baseline. Document the method and its limitations. If volume or complexity changes seasonally, make that visible. A credible baseline supports a fair pilot comparison and helps the organisation distinguish an actual improvement from the novelty of using a new tool. Keep source measurements available so the result can be reviewed later.
↑ Back to contentsHow do we check information readiness?
Find the material an experienced employee would trust for the task. Check who owns it, how current it is and which users may access it. Look for contradictory versions, missing fields and inconsistent terminology. If the source cannot answer the intended question, the project needs to address that gap. AI cannot be relied on to invent missing organisational knowledge, even when its output sounds convincing and uses familiar business language.
SharePoint consultancy is relevant where document governance and access need attention. Begin with a limited approved collection. Record the preparation required and the effort needed to keep it current. A candidate with substantial information dependencies may still be valuable, but score it with those dependencies included. Test whether users can retrieve the source themselves. Difficulties here suggest more preparation than a clean demonstration might imply. Assign owners for unresolved information questions rather than leaving them as a general task for the implementation team to discover after work begins.
↑ Back to contentsHow should we assess risk without losing useful ideas?
Consider the consequence of an incorrect output, inappropriate disclosure or unintended action. Ask who could be affected, how the mistake would be detected and how difficult it would be to reverse. Separate assistance for an experienced employee from a decision materially affecting a customer or staff member. The same software can carry different responsibilities in these settings, so evaluate the actual activity and permissions rather than the product name.
The ICO's AI resources provide a starting point for personal-information considerations. Some guidance is under review following legislative changes; check the current position for the proposed use. Obtain specialist advice for consequential applications. Assessment may lead to a narrower assistance task rather than abandoning the whole opportunity. Record the revised boundary. Exclusions and human review need to be reflected in permissions, instructions and an effective intervention route. A policy statement alone does not prove that an automated action can be stopped or corrected.
↑ Back to contentsWhat makes a use case technically feasible?
Check access to the required applications, permitted interfaces, authentication and available environments. Establish whether the tool can receive the real input and return the output in the form the process needs. For integrations, determine which records may be read or changed and how values will be validated. A supplier demonstration does not establish that the company's own configuration can support the same workflow or scale.
System integration services can help investigate these dependencies. Ask about missing records, duplicate requests, retries and application outages. Give each unproven assumption a test action and owner. A short feasibility exercise can prevent a large commitment based on a false premise. Include the operating environment: employees need suitable access and support. A solution working only for its developer on an isolated example has not demonstrated that colleagues can use it reliably. Record any required changes to other systems, because their cost and timetable belong in the overall assessment.
↑ Back to contentsHow should we score and compare the shortlist?
Choose a small set of criteria: value, frequency, information readiness, ease of verification, delivery effort, ownership and consequences of failure. Use a simple scale with a written explanation for every rating. Agree definitions before scoring so departments interpret them consistently. Keep the evidence linked to the record. The discussion about a rating is often more informative than the number itself, particularly where departments disagree about current performance.
Do not let one total conceal a serious unknown. A promising proposal with no approved information route cannot become ready by gaining points elsewhere. Use decision gates for conditions that must be satisfied, and scores to compare otherwise viable options. The government's AI Management Essentials guidance concerns organisational management practices and can prompt governance questions. It is not product certification or a ranking of your company's opportunities. Treat the scorecard as a transparent aid to judgement, with a named decision maker accepting the reasons for the chosen next step.
↑ Back to contentsHow do we estimate benefits without overstating them?
Estimate improvement after review, exceptions and support are included. Faster drafting creates little net benefit if checking becomes slower. State expected adoption and the proportion of work within scope. Use cautious assumptions until a pilot provides evidence. Keep assumptions visible so they can be updated. If only clean requests are suitable, do not apply the estimated saving to the entire workload without explaining that limitation.
Distinguish capacity, cash savings, service improvement and reduced delays. These may all matter, but should not be counted twice. Released time becomes a cash saving only if the organisation can identify the relevant expenditure change. Microsoft 365 support may help establish whether existing products can support the candidate before additional licences are considered. Include configuration, training and continuing ownership as well as subscriptions. A measured opportunity with moderate benefits is easier to defend than an ambitious forecast relying on immediate universal adoption and perfect outputs. Update the case using observed results at each decision point.
↑ Back to contentsWhich use case should become the first pilot?
Choose meaningful value combined with narrow scope and a clear assessment method. The first pilot should teach the organisation how to operate and evaluate the change. Prefer a task with available inputs, knowledgeable reviewers and consequences that can be contained. The largest theoretical benefit does not automatically make the best starting point. Readiness matters because the business needs evidence it can collect and understand within the pilot's agreed scope.
Write a brief covering users, approved information, exclusions, measures, costs and a decision date. Name who can pause it. Cyber security audit services may help if permissions or controls require investigation first. Keep early versions from changing important records or contacting customers without required approval. Include awkward examples rather than only clean ones. The objective is to learn whether the process improves under realistic conditions and what further work would be needed. Make stopping or narrowing the project an acceptable result when the evidence does not support expansion.
↑ Back to contentsHow do we stop the list becoming an endless backlog?
Give every idea a status and a next decision. Useful states include investigate, ready for pilot, blocked, deferred and closed. Record why something is blocked and what would change its position. Review the shortlist with owners on a scheduled basis. Remove duplicates and retire ideas whose problem has disappeared or whose assumptions no longer hold. Keep findings so a closed proposal is not rediscovered without its earlier evidence.
Limit active investigations to the team's capacity. A long list can look productive while nobody has time to test anything. Managed IT support is relevant where several pilots could create unmanaged access and support requests. Reserve time for existing operations as well as new experiments. A stopped pilot provides a useful decision if its findings are documented. Share those findings across departments. This helps prevent repeated spending on the same unworkable idea and makes the shortlist an operational decision tool, rather than a presentation assembled once and forgotten.
↑ Back to contentsHow can NetMonkeys turn a shortlist into an actionable plan?
Bring a few well-described candidates, anonymised example inputs, baseline measures and the names of the process owners. Explain the applications involved and which decisions must remain with employees. Identify limitations in information, access and staff availability. This creates a practical starting point without requiring a complete technical specification. Be clear about which assumptions have been measured and which remain estimates that need investigation.
Ask NetMonkeys to help assess feasibility, compare options and define the smallest useful next test. Request clear deliverables and responsibilities in any proposal. The result might be a pilot brief, information-preparation plan or conventional automation recommendation. It should explain what the company needs to learn and how the evidence will influence spending. Keep business ownership as the plan develops. The best use case is one the company can justify, operate and improve, with a result colleagues and customers can recognise. Review that result after implementation so the original objective remains part of the service's ongoing management.
↑ Back to contentsWhat else do businesses ask?
Do we need an AI use case in every department?
Collect ideas broadly, but do not force every team to deploy AI. Compare genuine problems and prioritise evidence, readiness and value. Some departments may benefit more from a process change or conventional workflow.
Can a demonstration count as our pilot?
A demonstration can show capability. Your pilot should test the intended process with representative inputs, appropriate controls and agreed measures. Confirm the gap between the demonstration and the actual operating environment.
What if our preferred idea is not ready?
Record the missing evidence or dependency and choose a proportionate next action. This could mean improving information ownership, testing an integration or deferring the idea while another candidate proceeds.
Who wrote this guide, and how was it checked?
Written by Caleb Adoh, Growth Marketing Manager at NetMonkeys. Primary ICO and GOV.UK resources checked 27 October 2026. Scoring and discovery methods are original practical recommendations, not a certified assessment or reported client outcomes. Internal links opened successfully.
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