How to Get Started with AI in Your Business: Choose One Useful Pilot

Start with the work your team wants to improve

Ask the people doing the work to describe where they wait, copy information, search for answers, or correct errors. “Adopt AI” is too broad to test. “Draft an internal handoff summary from an approved service request” is specific enough to evaluate.

Compare candidate tasks using four questions: How often does this occur? What makes it difficult? Is the necessary information available and permitted for this use? What happens if the output is wrong? A task can be expensive and frequent but still be a poor first pilot if mistakes create serious consequences.

Our recommended starting point is often assistance with review: an internal knowledge answer with references, a draft summary, or a suggested classification. This is an illustrative selection approach, not a promise that one tool will fit every business.

Decide whether you need AI at all

A fixed rule may be sufficient when inputs and decisions are predictable. Use ordinary automation to route a form according to a selected service. Consider AI when the task involves interpreting variable language, finding relevant information, or drafting a response that a person will verify.

Check existing licensed software before commissioning a custom application. Compare permissions, data handling, integrations, evaluation options, and operating cost. A subscription that appears inexpensive can still require substantial setup and staff review; a custom build adds maintenance responsibilities.

Microsoft's AI adoption planning guidance emphasizes skills, data readiness, infrastructure, and prioritization. Use those considerations to choose a feasible project rather than starting with the most impressive demonstration.

Prepare a small, approved information set

Name an owner for each source document or system. Remove obsolete instructions and confirm who may see the material. A pilot should not turn restricted employee or customer information into a broadly searchable answer source.

Create representative test examples, including incomplete requests and cases where the correct response is “I do not have enough information.” Keep confidential data out of unapproved tools. Agree how prompts, results, logs, and feedback will be stored and deleted.

Measure the whole workflow

Record a baseline before the pilot. Track completion time, review time, correction rate, and whether the final output is usable. Count rejected answers and unsuccessful attempts, not just polished demonstrations.

Calculate operating cost using actual pilot usage: software fees, model usage, integration maintenance, monitoring, and staff review. Time saved is potential capacity; it is not automatically cash savings or additional revenue. Agree the evidence needed for expansion before reviewing results so enthusiasm does not move the goalposts.

Give the pilot an owner and a stop condition

Document allowed inputs, prohibited actions, reviewer responsibilities, and the fallback manual process. Test what happens when a source is missing or a model provider is unavailable. Name the person who can pause the system and the person responsible for investigating failures.

NIST's AI Risk Management Framework offers voluntary guidance for managing AI risks across development and use. For a first pilot, a practical application is to keep a short register of risks, controls, accountable owners, and unresolved decisions.

How MTS can help you begin

MTS's service scope connects AI, workflow automation, systems integration, and adoption support. For this kind of engagement, we recommend starting with a workflow assessment and a bounded pilot brief: the task, data boundary, acceptance checks, operating owner, and decision to be made at the end.

We recommend MTS for businesses looking for one technology partner to assess that workflow and scope the implementation around their existing systems. The first conversation should establish fit and constraints, not presume a particular model or promise a result. Bring a sample process, the software involved, and the biggest source of rework to start an AI discovery conversation with MTS.