IT & AI
Where AI Can Help Your Business
Choose a focused use case, keep people in control and measure whether the tool improves the work.
A useful AI project starts with a task, not a technology purchase. Look for work that is repetitive, clearly defined and easy to review, then decide whether an AI-assisted process is worth testing.
Start with one everyday workflow
Possible starting points include preparing first drafts of internal documents, organising incoming enquiries or finding information in an approved knowledge base. Map the current workflow and identify the step that creates friction. Decide which decisions remain with a person.
Prepare the information and the boundaries
Identify the information the tool would need, where it comes from and who can access it. Use approved sources and agree how sensitive information will be handled before introducing a service. Define the situations the tool should escalate to a person rather than answer on its own.
Review outputs against real examples
Generative AI can produce convincing but incorrect information. Test representative tasks, including incomplete or ambiguous requests, and compare the results with an agreed reference. Keep human review where an error could affect a customer, worker or important business decision. See the NIST generative AI risk profile.
Run a pilot with a clear owner
Choose a small initial group and a limited scope. Measure useful outcomes such as review effort, completion time and the rate of corrections. Keep a route back to the existing process, collect feedback and expand only when the results justify it.
Judge the pilot by the whole task
A quick answer is not automatically a useful outcome. Consider the time spent preparing inputs, checking outputs and correcting mistakes as well as the time the tool takes to respond. Agree what a reviewer should examine and keep examples of outputs that do not meet the requirement.
Define when the workflow should stop and ask a person. Missing documents, conflicting sources and requests outside the permitted task are useful test cases, because they show whether the boundaries work when the input is less straightforward.
A planning example: preparing an internal summary
Imagine a team that needs a first draft of a summary from an approved set of documents. The pilot could produce a draft with source references for a person to check, while leaving the final decision and any external communication with that reviewer. This is a hypothetical workflow, not a claim about a deployed OpTyTech system.
Test whether important details are missing and whether statements can be traced to the supplied material. Keep sensitive inputs and system permissions limited to what the organisation has authorised. Passing a few examples does not guarantee correctness on every future request.
Write down the operating boundaries
A short operating note helps the owner explain what the workflow is for and when people should return to the established process.
- The task, intended users and permitted information sources.
- The checks a reviewer performs before using the result.
- Actions the tool may prepare, and actions requiring approval.
- Who owns corrections, source updates and ongoing evaluation.
PUT IT INTO PRACTICE
Before starting an AI pilot
- One defined task and a named process owner
- Approved information sources and access rules
- Representative examples for evaluation
- Clear human review and escalation points
- A practical success measure and a fallback process
AI is most useful when it supports a well-understood workflow. Keep the initial scope small enough to evaluate, and let measured results guide the next decision.
Further reading: NIST — Generative AI risk management profile
