How to Choose Your First AI Automation Project

If you manage a business with recurring digital work, choose a first AI automation project that tackles one small, frequent task with a measurable outcome and manageable consequences if the result is wrong. AI automation uses software to move work through steps and artificial intelligence to interpret or create information, such as reading a request and drafting a response. A person or software rules can check the result, route it, or approve the next action.

The best candidate is not necessarily the task that sounds most advanced. It is a task where AI has a specific advantage, the people responsible can judge its output, and a pilot can show whether the complete workflow improves. Start by understanding the work, then compare AI with simpler ways to handle it.

Choose a task within a process

Choose one clearly defined task instead of trying to automate a whole department or customer journey. For example, a pilot might sort incoming support requests into categories or extract possible fields from documents for a person to verify. Choose a task that happens often enough to measure. Assign someone to review the result and decide what to do when the AI is uncertain.

First, ask the people who do the work to describe how cases actually move. Written procedures may miss exceptions, repeated data entry, or informal handoffs. When business software records event logs, process mining can analyze those records to show common paths and bottlenecks. For individual desktop actions, task mining examines how people perform specific tasks. These are ways to investigate a process, not requirements to buy a particular tool.

Simplify unnecessary steps before automating them. If employees enter the same information in two systems that do not share data, connecting the systems or using rule-based automation may solve the problem more directly than AI. A stable rule, such as routing a record when a required field matches, does not need a model that interprets language. AI fits better when inputs vary and someone needs to interpret text or other content. IBM’s overview of business process automation distinguishes predictable automation from AI-supported work that handles less structured tasks.

Check whether the task fits AI

A promising task has a result that a knowledgeable person can recognize as correct. A request classifier, for instance, can be checked against the category an experienced employee would assign. If reviewers cannot agree on what a good result looks like, define the acceptance standard before building anything.

Also consider what happens when the AI makes a mistake. If a person can catch and correct an error before it reaches a customer or changes a financial record, the task is easier to contain. If an error would be difficult to detect or reverse, the project needs stronger controls or a lower-risk task. The NIST AI Risk Management Framework recommends defining the intended use and identifying impacts. It also recommends testing the AI in conditions similar to real use and planning how people will oversee it.

Human review only helps when the reviewer has the time, context, and authority to reject or correct an output. A person who must approve a long queue without seeing the source information is unlikely to provide an effective check. The NIST profile for generative AI identifies overreliance on AI outputs as a human-AI interaction risk. Build review into the design. Show the reviewer the relevant input and let them edit or reject the result. Route ambiguous cases to someone qualified.

Workplace evidence also cautions against choosing a project based on the model alone. In a field experiment at a multinational pharmaceutical company, researchers found different outcomes after changing how workers interacted with an AI-based sales system. They changed the system's procedures and decision authority. They also changed training and incentives. The findings concern that particular workplace and system, but they show why the working conditions belong in the project plan alongside the AI tool. The study in Management Science reports that an untailored interaction reduced performance relative to the legacy system, while a tailored interaction produced positive effects relative to the untailored and control conditions.

Measure the work before and after

Before testing automation, record how employees handle the task today. Depending on the work, useful measures include how many cases arrive, how long employees spend handling them, how often they correct mistakes, and how long cases wait for a response. Choose measures that reflect the outcome the business wants, not just the amount of activity the software produces.

For an AI-assisted task, measure the result after a person reviews it. Track corrections and rejected outputs. Also record missed issues and cases escalated for help. A fast first draft does not show a gain if an employee then spends longer checking and repairing it.

Count all operating costs, including setup, integration, model or platform charges, review, training, monitoring, and exception handling. Then distinguish time employees free up from money the business saves. When employees spend less time on one task, they free up time. The business saves money directly only if it uses that time to avoid an expense or reduce costs elsewhere. NIST recommends considering both benefits and costs, including costs tied to errors and trustworthiness.

Run a pilot with limited authority

Before live use, test the AI on examples from ordinary work. Include incomplete, unusual, and ambiguous cases too. Set pass criteria in advance. Specify the quality level the output must meet. Decide which cases the AI must defer and what action the person responsible takes when the AI fails. Compare the pilot with the baseline across quality, handling time, rework, exceptions, service, and total cost.

Limit what the pilot can access and change to what the task requires. For example, an AI system that drafts a response for review does not need permission to send that response directly. Name one person to review results and track problems. That person should decide whether the pilot should continue, change, or stop.

The OECD’s workplace case studies describe deployments where workers shifted from routine task steps to checking or correcting AI outputs. The study’s cases are not a representative sample, so they do not establish how common that shift is. The cases still point to a practical need: measure the work employees do after the AI responds, as well as the work it appears to take on.

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