AI agents in business: where to start and how to measure value
Choose a bounded AI pilot, define permissions and human review, and measure the cost of work that actually passes your acceptance criteria.
By UNIXDEV Team · 3 min read
Original published · English edition
An AI pilot is useful when it answers a business question: can this task be done reliably enough, at a cost that makes sense? Begin with one workflow and evidence your team can inspect.
Agent, chatbot or workflow?
A chatbot provides a conversational interface. A workflow follows predefined steps. An agent can choose tools or next steps within a defined task. These approaches can be combined, but a fixed workflow is often easier to test when the sequence is already known. Do not add autonomous decisions merely because a model can make them.
Three places to investigate
Customer-service requests
Read a request, find relevant guidance and prepare a sourced draft for an employee to review. If the only need is classification and routing, a workflow may be enough. Measure total handling time, substantive corrections and repeat work.
Internal document search
Answer questions from approved documents with references and a clear response when evidence is missing. Retrieval-augmented generation may solve the problem without a multi-step agent. Test access boundaries as well as answer quality.
Sales meeting preparation
Assemble permitted CRM history, open issues and source links into a briefing. Begin with a draft rather than permission to edit the CRM or send messages. Measure preparation time, completeness and factual corrections; changes in revenue have many causes.
Four checks before choosing the pilot
- Does the work repeat often enough? Record task volume and current handling time.
- Is the data usable? Identify sources, owners, access rules and quality problems.
- Can someone judge the result? Define the facts and conditions a passing output must include.
- Can the impact be contained? Start with reading or drafting, a human escalation path and a way to return to the existing process.
Set boundaries before enabling tools
Give each tool a narrow purpose and only the permissions needed. Require review for consequential actions, limit retries and make repeated requests safe. Test unavailable tools, incomplete records and malicious instructions inside documents. Log what happened without unnecessarily exposing sensitive data.
Measure accepted work
Use representative tasks and compare them with a baseline of similar difficulty. Include failures and timeouts. Measure human review and correction time, the proportion of tasks that pass, total operating cost per accepted task, end-to-end latency and unauthorized actions. An average quality score must not hide an access-control failure.
A worked example, not a forecast
Suppose 2,000 eligible tasks a month take 10 minutes each. With AI, review and correction average 6 minutes, including tasks completed manually after failure. The capacity recovered is 2,000 × (10 − 6) ÷ 60 = 133.3 hours a month. At an assumed value of THB 300 an hour, that is THB 40,000 of capacity.
If model, tool and operating costs are THB 15,000 a month, net capacity value is THB 25,000. An assumed THB 150,000 setup would take six months to recover on that basis. This is not necessarily cash saved: staff time must be put to useful work. If review takes eight minutes, net value falls to THB 5,000 and the same calculation becomes 30 months. These are illustrative assumptions, not a quote or measured customer result.
A 30-day pilot outline
- Week 1: choose one task, gather a baseline and define acceptance and access rules.
- Week 2: connect a limited set of sources and test a draft-only prototype.
- Week 3: evaluate normal cases, difficult cases, failures and permission boundaries with reviewers.
- Week 4: compare quality, time and cost, then decide to expand, revise or stop.
This is a planning outline. Data preparation or approvals may require longer.
Prepare the first discussion
Bring the workflow, task volume, sanitized examples, data sources and the people responsible for acceptance. Discuss an AI and LLM engineering pilot before choosing a broad platform rollout.