AI use cases for business

See where AI could create value across your business.

Explore practical situations across strategy, sales, marketing, customer experience, operations, finance and people—with leadership, value, readiness and risk kept in view.

Explore the use-case library

These examples are starting points for better questions. They are not recommendations to adopt a particular technology or pursue every possible opportunity.

Business need before technology

Value, evidence, readiness and risk—considered before deciding what deserves attention.

Examples, not prescriptions

A use case is a starting point—not an investment case.

The same idea can be valuable in one organisation and a distraction in another. Context, evidence, readiness, risk and leadership ownership determine whether an opportunity deserves to progress.

01Start with the business problem.

02Test the value and evidence.

03Decide whether to pursue, defer or reject.

Use Case Library

Recognise a situation, then examine the leadership questions.

Use the filters to find relevant examples. The library helps you recognise where AI might create value; our Find AI Opportunities service examines your business context, evidence, readiness and risk to determine which opportunities are credible and deserve further consideration.

Strategy & Leadership · Decision Quality

Strategic Use Case Prioritisation

AI ideas are accumulating, but leadership has no consistent basis for deciding what deserves attention or investment.

Complexity: Medium
Indicative time: 6–12 weeks
Risk: Medium

Potential measures

Decision speed, portfolio focus, proportion of initiatives with defined owners and outcomes.

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What AI might support

Create an evidence-led view of the opportunity portfolio so ideas can be compared consistently before resources are committed.

How The AI Centre would approach it

Align the portfolio to business priorities, diagnose the evidence available and support leadership to pursue, defer or reject each opportunity explicitly.

What success could look like

Leaders can explain why each initiative is progressing, what must be true for it to succeed and what should not receive further investment.

Data likely neededBusiness priorities, use-case proposals, current initiatives, cost and readiness evidence.

Key leadership concernWeak evidence can create false confidence in an apparently attractive opportunity.

Strategy & Leadership · Decision Quality

Sales Performance Leadership Assistant

Sales leaders spend significant time compiling performance information but still struggle to turn strategy and KPIs into timely action.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Low

Potential measures

Reporting time, action completion, forecast confidence and speed of issue resolution.

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What AI might support

Use an AI-assisted management view to compare performance with the sales strategy, identify exceptions and prepare sharper leadership questions.

How The AI Centre would approach it

Define the decisions the assistant should support, establish trusted data sources and keep management judgement accountable for every action.

What success could look like

Leadership conversations spend less time assembling reports and more time addressing exceptions, choices and agreed actions.

Data likely neededSales strategy, targets, CRM data, pipeline measures and agreed performance indicators.

Key leadership concernCommercially sensitive information and over-reliance on incomplete CRM data.

Sales · Revenue Growth

Predictive Lead Scoring

Sales time is spread across enquiries of uneven quality, contributing to inconsistent conversion and slow follow-up on stronger opportunities.

Complexity: Medium
Indicative time: 6–12 weeks
Risk: Medium

Potential measures

Lead-to-opportunity conversion, response time, sales capacity and scoring accuracy.

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What AI might support

Use patterns in historical lead and customer data to help sales teams identify which opportunities may warrant earlier attention.

How The AI Centre would approach it

Test the commercial case and data quality, define how scores inform rather than replace judgement, and establish monitoring before wider adoption.

What success could look like

Sales attention is focused more consistently and any improvement in conversion can be demonstrated against a credible baseline.

Data likely neededCRM records, lead sources, engagement history, outcomes and relevant customer attributes.

Key leadership concernPoor historical data or hidden bias may systematically misdirect sales attention.

Sales · Revenue Growth

Proposal Development Assistant

Creating tailored proposals takes too long, while quality and consistency vary depending on who prepares them.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Medium

Potential measures

Time to first draft, proposal turnaround, revision effort, consistency and conversion.

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What AI might support

Support teams to produce a first draft using approved propositions, evidence, customer context and brand language.

How The AI Centre would approach it

Set clear source boundaries, approval responsibilities and human review, then test whether faster drafting produces better commercial outcomes.

What success could look like

Teams respond faster without weakening accuracy, relevance, brand consistency or accountability for the final proposal.

Data likely neededApproved propositions, previous proposals, customer briefs, case evidence and brand guidelines.

Key leadership concernInvented claims, incorrect customer details and confidential information appearing in the wrong proposal.

Marketing · Productivity

Automated Marketing Reporting

Marketing teams spend too much time collecting figures and formatting reports, leaving less time to interpret performance and act.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Low

Potential measures

Reporting time, data accuracy, decision turnaround and actions completed from each review.

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What AI might support

Bring agreed measures together and use AI to prepare a structured performance narrative, exceptions and questions for review.

How The AI Centre would approach it

Agree which decisions the report should support, reconcile the underlying measures and retain named ownership for interpretation and action.

What success could look like

Reporting becomes faster and more consistent while management attention shifts from producing numbers to improving performance.

Data likely neededAnalytics, CRM, campaign platforms, targets and agreed reporting definitions.

Key leadership concernInconsistent definitions or missing data can create a persuasive but misleading performance story.

Marketing · Decision Quality

Competitor Research Monitoring

Competitor information becomes outdated quickly, leaving sales and marketing teams reliant on sporadic research and assumptions.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Low

Potential measures

Research time, coverage of priority competitors, useful alerts and decisions informed by new evidence.

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What AI might support

Monitor approved public sources and summarise material changes in propositions, content, recruitment, reviews and market activity.

How The AI Centre would approach it

Define what matters commercially, restrict monitoring to appropriate sources and require evidence links before insights inform a decision.

What success could look like

Teams receive timely, attributable intelligence and can distinguish meaningful market movement from background noise.

Data likely neededCompetitor URLs, public content, review platforms, news sources and an agreed monitoring brief.

Key leadership concernCoverage gaps, misinterpretation and confidently reported claims that are not supported by the source.

Customer Experience · Retention

Customer Churn Prediction

Warning signs of customer dissatisfaction are identified too late, after the opportunity to intervene effectively has narrowed.

Complexity: Medium
Indicative time: 6–12 weeks
Risk: Medium

Potential measures

Retention, preventable churn, intervention rate, prediction accuracy and customer value protected.

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What AI might support

Combine behavioural, service and commercial signals to highlight customers that may require earlier human attention.

How The AI Centre would approach it

Establish a measurable retention case, test which signals are reliable and define how account teams review and act on recommendations.

What success could look like

Customer teams intervene earlier and can demonstrate whether targeted action improves retention without damaging trust.

Data likely neededCRM history, service interactions, usage, complaints, renewal dates and commercial records.

Key leadership concernInaccurate predictions may lead to inappropriate treatment or distract teams from customers who genuinely need attention.

Customer Experience · Cost Efficiency

AI-Assisted Customer Support

Support teams are handling large volumes of repetitive enquiries while customers still expect fast, accurate and appropriate responses.

Complexity: Medium
Indicative time: 6–12 weeks
Risk: High

Potential measures

Resolution time, first-contact resolution, escalation rate, customer satisfaction and cost per case.

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What AI might support

Assist agents or automate tightly bounded enquiries using approved knowledge, escalation rules and clear human hand-offs.

How The AI Centre would approach it

Start with a narrow service boundary, establish ownership and testing, and expand only when accuracy, escalation and customer outcomes justify it.

What success could look like

Routine demand is handled more efficiently without reducing service quality, transparency or access to accountable human support.

Data likely neededSupport history, approved knowledge, product information, policies and escalation rules.

Key leadership concernIncorrect customer-facing answers, weak escalation and the inappropriate use of sensitive information.

Operations · Productivity

Internal Knowledge Assistant

Employees lose time searching across documents and systems, and may rely on outdated or inconsistent information.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Medium

Potential measures

Search time, successful resolution, repeated questions, source coverage and answer accuracy.

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What AI might support

Provide a controlled way to find and summarise approved internal knowledge with references back to the source.

How The AI Centre would approach it

Define trusted sources and permissions, assign content ownership and test retrieval quality with the people who will use it.

What success could look like

Employees find reliable information faster and can see where answers came from, while sensitive content remains appropriately controlled.

Data likely neededPolicies, procedures, guidance, product information, permissions and document ownership.

Key leadership concernAccess-control failures, outdated sources and answers presented without sufficient evidence.

Operations · Cost Efficiency

Document Processing Automation

Teams spend substantial time extracting, checking and re-entering information from routine documents.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Low

Potential measures

Processing time, cost per document, error rate, exception rate and backlog.

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What AI might support

Extract defined fields, classify documents and route exceptions for human review within an existing business process.

How The AI Centre would approach it

Choose a bounded process, establish baseline cost and error rates, then design human review around the material exceptions.

What success could look like

Routine documents move through the process faster while accuracy and exception ownership remain visible and controlled.

Data likely neededRepresentative documents, validation rules, process exceptions and destination-system requirements.

Key leadership concernExtraction errors can move quickly into downstream systems if validation and exception handling are weak.

Finance · Financial Control

AI-Assisted Invoice Processing

Finance teams spend substantial time extracting, checking and re-entering supplier invoice data, while exceptions can be difficult to identify and resolve consistently.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Medium

Potential measures

Processing time, cost per invoice, approval cycle time, exception backlog and duplicate or data-entry errors.

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What AI might support

Extract agreed invoice fields, check them against purchase orders and supplier records, and route exceptions for human review before approval.

How The AI Centre would approach it

Define a bounded invoice process and matching rules, test representative documents, and retain human approval with a clear audit trail for every exception.

What success could look like

Finance teams spend less time re-entering routine information, approvals move faster and material exceptions remain visible, controlled and accountable.

Data likely neededSupplier invoices, purchase orders, supplier records, approval rules and finance-system field requirements.

Key leadership concernIncorrect extraction or matching may create accounting errors, duplicate payments or inappropriate handling of financial and supplier data.

People & HR · Productivity

AI-Assisted Candidate Shortlisting

High application volumes make initial screening slow and inconsistent, particularly when criteria are interpreted differently by reviewers.

Complexity: Low
Indicative time: 2–6 weeks
Risk: High

Potential measures

Screening time, consistency, qualified-candidate progression and adverse-impact indicators.

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What AI might support

Organise applications against an agreed rubric to support a consistent first review while keeping employment decisions with people.

How The AI Centre would approach it

Confirm whether AI is appropriate, complete the necessary privacy and impact assessment, test for bias and retain meaningful human review.

What success could look like

Recruitment teams save time without weakening fairness, transparency, data protection or accountability for shortlisting decisions.

Data likely neededJob specifications, scoring criteria and appropriately handled candidate information.

Key leadership concernDiscrimination, privacy breaches, inaccessible reasoning and automation influencing consequential employment decisions.

People & HR · Productivity

Job-to-Task Analysis

Teams feel overloaded, but leaders lack a reliable view of which tasks consume capacity and which could be redesigned or supported.

Complexity: Low
Indicative time: 2–6 weeks
Risk: Medium

Potential measures

Time released, process delay, workload balance, adoption and employee experience.

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What AI might support

Break roles into tasks and identify where AI, process change or clearer prioritisation may release useful capacity.

How The AI Centre would approach it

Involve employees, distinguish tasks from roles and test whether proposed changes improve work rather than simply reducing visible activity.

What success could look like

Leaders gain a clearer capacity picture and teams spend more time on work requiring judgement, relationships and specialist expertise.

Data likely neededJob descriptions, process maps, work diaries, interviews and representative task information.

Key leadership concernPoor communication can create anxiety about job loss and produce an unrealistic view of how work is actually performed.

From possibility to decision

Technical possibility is not enough.

An opportunity earns its place by surviving commercial, operational and governance scrutiny.

01
Does it address a material business problem?
02
Is the potential value clear enough to measure?
03
Are the data, capability and operational foundations credible?
04
Can the risks be controlled proportionately?
05
Is ownership clear from decision through adoption?

A practical first conversation

Which opportunities genuinely deserve attention?

Discuss the situations relevant to your business and the evidence leadership needs before deciding what should happen next.

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Start with business priorities

Consider value and risk together

No technology pitch