How AI creates real value.
An impressive AI demo still leaves the crucial question unanswered: does it make everyday work better? Value emerges when a clearly defined task, suitable data and a verifiable outcome come together. A good starting point is the process itself.
Start with the task, not the tool
Look for a recurring task that currently takes time: finding information in documents, sorting incoming requests or preparing a draft. First describe who performs the task, what information they need and what a good result looks like.
Not every task needs a language model. Where fixed rules reliably determine the outcome, conventional automation may be simpler and cheaper. AI becomes useful when language, inconsistent documents or interpretation are involved. Even then, the first use case should be small enough to understand in full.
An example: preparing better responses
Suppose a service team receives enquiries about different products. Staff read each message, find relevant documents and compose a response. An AI assistant could summarise the request, suggest useful documents and draft a reply. Initially, the team retains responsibility for approval.
The goal is not simply to have a chatbot. It might be to reduce the effort needed to prepare an accurate response. Processing time, the amount of correction required and the quality of the sources found can show whether this works. This example illustrates a possible application, not a client project or a guaranteed result.
Make knowledge accessible while respecting boundaries
A model does not automatically know a company's internal processes. To work with company knowledge, it needs suitable, up-to-date sources. In a retrieval-based approach, for example, relevant passages are retrieved and given to the model as context for its answer. This can make answers easier to verify, but does not guarantee accuracy.
Access permissions must apply at the document retrieval stage. An assistant must not disclose information that a person cannot access in the source system. It is equally important to assign responsibility for outdated content and define what happens when no reliable source is found.
Test quality on real tasks
Before starting a pilot, compile a set of typical, difficult and incomplete requests. Subject-matter experts should assess the results against predefined criteria. Record incorrect answers, missing sources and cases where the assistant fails to recognise its limits.
NIST's generative AI profile discusses risks including plausible but false outputs, and the importance of evaluation and monitoring. For a business pilot, this means judging quality on more than a few successful demonstrations.
Further reading: NIST: Generative Artificial Intelligence Profile ↗
From pilot to everyday use
Start with a limited group of users and a clear route back to the existing process. Alongside answer quality, monitor waiting times, cost per task and additional review effort. A quick draft offers little benefit if checking it takes longer than the original task.
After the pilot, there are three possible decisions: expand, make targeted improvements or abandon the approach. Any of them may be sensible. A sound decision is more valuable than a rollout driven solely by enthusiasm for the technology.
- Identify a clear process and its current weaknesses.
- Define data sources, access permissions and human approval.
- Set quality criteria and benchmarks before the pilot.
- Decide on further automation only after demonstrating measurable value.
A worthwhile first step with AI is a verifiable improvement to a specific workflow.
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