5 AI Use Cases You Can Stand Up in 90 Days

• Most organisations do not need a large-scale AI overhaul to get started – they are more likely to see value by piloting a small number of tightly scoped operational use cases first.

• The best 90-day AI pilots sit close to existing workflows, address a clear operational pain point, and keep human oversight firmly in place.

• Early AI use cases such as triage, forecasting, knowledge search, summarisation and anomaly detection can improve speed, consistency and decision-making without requiring major systems redesign.



• The real challenge is no longer AI awareness but turning experimentation into measurable operational impact through clear ownership, governance and success metrics.
  

Most organisations do not need a grand AI transformation programme before they see value. They need a few well-chosen operational use cases, clear ownership, sound governance and a fast path to proof.

This article sets out five AI uses that operations leaders can often pilot in around 90 days, together with the questions to ask before you start.

Why AI Still Struggles to Reach Your Operations

Most leadership teams are now discussing AI on a regular basis. Boards might be receiving updates, pilots are announced, and strategy decks are full of references to generative AI. Yet the impact in day-to-day workflows often remains limited and incomplete.

Recent McKinsey research found that most organisations now use AI in at least one function, but only a minority have scaled it across the enterprise or achieved material contribution to EBIT. A BCG study reported that around three-quarters of companies have yet to show tangible value from their AI investments. So, the problem is no longer awareness, it is converting experimentation into operational outcomes.

For COOs and heads of shared services, the immediate question is therefore not, “What is our AI vision?” but, “Where can we safely prove value in the next quarter using the people, platforms and data we already have?”

What Makes a Good 90-day AI Use Case?

The five use cases below share a common pattern.

They sit close to an existing workflow rather than requiring a new operating model. They address a visible operational pain point. They rely on data or knowledge that is already available, even if it needs tidying. Integration can be kept modest. Success measures are straightforward to define. Human oversight remains central, in line with guidance such as NIST’s AI Risk Management Framework for generative AI, which stresses trustworthiness, evaluation and clear human responsibility.

Against that backdrop, here are our five candidates that often work well as early AI pilots:

1. Intelligent Triage and Routing

In many organisations, cases, tickets or requests still bounce between queues. Work is assigned on a 'who is free' basis, and priorities are therefore applied inconsistently. The result is slower response times, staff frustration and avoidable SLA breaches.

An AI-supported routing layer can classify incoming work, apply business rules and route items by priority, skill or queue. Modern platforms, such as Microsoft’s unified routing capabilities in Dynamics 365 Customer Service, combine machine learning with configurable rules so that, for example, complaints about a high-risk product are fast-tracked to a specialist team while routine requests follow a standard pathway.

A 90-day pilot would typically narrow the scope to one function – for example a service desk, complaints team, or HR service centre. The aim is to improve throughput and consistency rather than redesign the entire operation.

How to Track

Measure success in response times, reassignment rates, backlog age, SLA performance and first-time resolution. Governance should focus on the quality and representativeness of training data, potential bias in categorisation, how exceptions are handled, and how frontline teams remain able to override or challenge the routing when needed.

2. Demand and Capacity Forecasting

Many operations leaders will recognise a persistent workload pattern: teams spend their time firefighting, rosters need to be adjusted at short notice, SLAs slip on busy days, and then overtime costs rise. And yet, historic workload data often exists in ticketing tools, telephony systems or workflow platforms.

AI-driven forecasting can use historic volumes, seasonality and selected external signals to predict short-term demand for a given team. The goal is not perfect prediction but a more informed view of likely workload over the next few weeks.

In a 90-day window, a pilot might focus on a single operational area, one forecast and one planning cycle – for example, forecasting weekly inbound volumes for a contact centre and using that forecast to shape rotas.

How to Track

Key measures include forecast accuracy at the level that matters for planning, schedule adherence, overtime and temporary staffing usage, and SLA performance. Risks to manage include patchy or inconsistent historic data, overconfidence in early model outputs, and the temptation to treat the forecast as a substitute for operational judgement rather than an input to it.

3. AI Assistant for Knowledge Bases and Company Intranet

Frontline workers often spend significant time searching for answers. Policies, procedures and operational knowledge are scattered across SharePoint sites, intranets, shared folders and email archives. Even when the information exists, it may be hard to find quickly while serving a customer or handling a case.

An internal AI assistant can provide natural-language questions and answers over a curated body of internal content. Using a retrieval‑augmented generation approach, as outlined in Google’s reference architectures, the assistant retrieves relevant documents from authorised sources and then generates a grounded answer with clear references back to those sources.

To keep the risk profile manageable in 90 days, the pilot should be scoped tightly: a well-defined knowledge domain, a named group of users, and clear escalation paths when the assistant is unsure. Approved content is ingested and regularly refreshed; anything outside scope remains out of reach.

How to Track

Impact can be tracked through time saved finding answers, user satisfaction, the rate of repeat questions and escalation patterns. Governance should address hallucinations, the quality and ownership of source material, permission controls, and how users are trained to verify answers rather than treating them as authoritative in isolation.

4. Document and Case Summarisation

In document-heavy workflows, staff routinely read long records, handover notes, complaint histories, case files, meeting transcripts or policy documents. The work is necessary but time‑consuming, and important details can still be missed by human error.

Summarisation models can help by producing concise overviews, extracting key actions and highlighting material changes or risks from existing content. The underlying systems do not need to be rebuilt; the summarisation layer can often sit alongside current tools.

A 90-day pilot might focus on a single workflow such as complaints handling, procurement, case management or service transition. The priority is reducing handling time for defined activities while maintaining or improving quality.

How to Track

Metrics include time spent on review, handover quality, speed of decision-making and the consistency of notes across different teams. From a risk perspective, leaders should pay attention to missed nuance, over-reliance on summaries for high-stakes decisions, and the discipline with which staff review both the source material and the AI output. NIST’s guidance on human oversight is particularly relevant here: the summary should support, not replace, professional judgement.

5. Exception Detection and Anomalies

Finally, many teams spend a large share of their time spotting unusual cases, broken process paths or recurring failure patterns by manual labour. Billing anomalies, repeat incidents, orders that repeatedly fail in the same step and service exceptions are all examples where patterns often hide in plain sight.

Exception‑detection models can use historic operational data to flag unusual behaviour, predict likely issues or suggest the next operational action. The aim is to focus human attention where it can have the greatest impact: earlier intervention, lower rework and fewer missed problems.

As with the other examples, a 90-day pilot works best when scope is narrow. One repeatable process with enough history – for example a specific billing flow or a defined incident type – is usually sufficient.

How to Track

Measures might include the number of issues detected earlier, changes in rework or repeat incidents, and any movement in cost‑to‑serve. Controls should cover data quality, false positive rates, the clarity of ownership for alerts, and how recommended actions are embedded into existing workflows rather than treated as a separate activity.

Moving from Intent to Impact

The strongest signal from recent AI surveys is not that organisations lack ambition. It is that many are still working out how to convert that ambition into measurable value in their core operations. This applies to Fortune 500 companies in much of the same way it does to SMEs and startups.

Starting with one tightly framed, governable use case – and proving it can deliver value in 90 days – is often the most practical response and a way to present an efficient test case to the board or leadership team.

An AI test case involves choosing the right workflow, setting clear measures, applying recognised governance frameworks and ensuring that AI augments rather than displaces human expertise. For organisations operating in complex or regulated environments, that balance matters as much as the underlying technology.

Cambridge Management Consulting has an in-house AI team that works with leaders to identify the right AI opportunities, design and govern pilots, and integrate them into real-world operations across sectors. If you would like to explore what a 90‑day AI pilot could look like in your organisation, we can help you move from discussion to delivery in a way that is deliberate, transparent and measurable.