AI is changing work. Start with one workflow.

Microsoft’s 2025 Work Trend Index describes a progression from individual AI assistance toward teams working with agents and more connected processes. It is a vendor’s research and outlook, not a timetable that every organization will follow. Its useful implication for a smaller business is to examine how tasks fit together, rather than count how many AI subscriptions the team has.[1]

Consider a weekly customer update. Someone gathers notes, checks outstanding work, writes a summary, gets approval, and sends it. AI might help draft the summary. A connected workflow could also collect the approved inputs, flag gaps, present the draft for review, and record the final version. The value depends on whether this whole sequence becomes easier to run.

Start with something frequent enough to observe and narrow enough to understand. A useful candidate has a recognizable input, a clear owner, and an output that someone can check. Preparing a first draft from approved source material is easier to evaluate than asking an agent to run an entire marketing function.

Write down the current path before choosing tools. Include the quiet work: looking for files, correcting missing information, waiting for approval, and recovering when a system fails. These steps often determine whether an automation feels helpful in daily use.

• What starts the task, and how often does it happen?
• What information is needed, and where does it live?
• What does a good result look like to the person using it?
• Which decisions need a person, and who handles exceptions?

Some steps need ordinary software: move a file, check a required field, or send a reminder at a set time. Others involve language or judgment, such as sorting a free-form request or proposing a summary. An agent becomes useful when the next step depends on context and the system needs to choose between tools.

Anthropic’s engineering guide distinguishes predefined workflows from agents that choose their own sequence of actions. It recommends starting simply and adding complexity when it improves the outcome. That distinction helps keep a project understandable and easier to maintain.[2]

For the customer-update example, a fixed sequence with an AI drafting step may be enough. You can still get a useful result without giving a system permission to decide whom to contact or when to send a message.

Use a small set of representative examples before connecting a workflow to everyday operations. Include incomplete records, contradictory notes, and a failed connection. Decide what the system should do when it cannot produce an acceptable answer. A visible request for review is often more useful than a confident guess.

Record a baseline for time spent, corrections, and missed handoffs. Then compare the pilot using the same measures. Include review time and ongoing costs. A faster draft is only a gain if checking and repairing it does not consume the time saved.

Give the workflow a named owner and a simple way to pause it. Agree what information it may access and what changes it may make. These are operating decisions to make alongside the design, before the first live run.

AI capabilities will keep changing. Your process for evaluating them can stay consistent: identify a need, test a bounded solution, learn from use, and expand where the result earns it. That gives a team a way to adapt without replacing its tools every time a new model appears.

At PraoAI Labs, our proposed starting point is a workflow conversation. We map the task, identify where software or AI could help, and define a first version with clear review points. Bring one task you keep doing by hand. That is enough to begin a useful discussion.