Where to start with AI in your company: a 90-day roadmap for SMBs

You have heard it all about artificial intelligence: it will reshape your market, your competitors are already using it, and you need to move now. Yet on Monday morning your team is still copying data between spreadsheets, your finance lead rebuilds the same report every month, and you are not sure where to begin. The good news is that starting does not require a massive program. It requires a roadmap. Here is a 90-day plan built for small and mid-sized businesses, from a 15-person agency to a PE-backed company with a few hundred employees.

Before day 1: decide what you want to fix

The most common mistake is starting with the tool. The right question is not which AI should we buy, but which problem is costing us the most today. Spend one hour with your leadership team and answer three questions:

• Which task eats the most staff hours without creating direct value?
• Where do errors, rework, or delays pile up?
• What knowledge lives in the heads of one or two people?

The answers become your candidate list. AI works best where there is volume, repetition, and reasonably clear rules.

Days 1 to 30: map and choose

Month one is about diagnosis, not technology. Do the following:

1. Map five to eight candidate processes. For each, note who does it, hours per week, which systems and spreadsheets are involved, and where it tends to break.
2. Score each process from 1 to 5 on impact (hours or dollars), feasibility (accessible data, clear rules), and risk (what happens if the AI gets it wrong).
3. Pick a single pilot with high impact, high feasibility, and low risk. Resist the urge to pick three.
4. Set a measurable goal. For example: cut weekly order reconciliation from 6 hours to 1 hour.

By the end of month one you should have one chosen process, a baseline of what it costs today, and an internal owner for the pilot.

Days 31 to 60: build and test with supervision

In month two the pilot comes to life. Keep the scope small and keep a human in the loop.

• Get your data in order. A large share of the work is here: consolidating scattered information, standardizing names, agreeing on one source of truth.
• Build the minimum version. It might be an agent that answers customer questions from your product catalog, an automation that reads emails and creates orders, or a dashboard that consolidates sales.
• Test on real past cases and compare the AI output with what your team actually did.
• Set guardrails. Decide what the AI can do alone, what needs approval, and which data it must never touch. This also keeps you on the right side of privacy rules such as the California CCPA or HIPAA, if they apply to you.

Hypothetical example: a regional distributor spends 20 hours a week keying in orders that arrive by email and text. In the pilot, the AI reads the messages and drafts the order entry while a team member only reviews it. If the time drops to 5 hours, that is 15 hours freed every week, roughly $450 to $600 in labor at typical rates, repeated every week of the year.

Days 61 to 90: measure, adjust, decide

Month three is real operation. Put the pilot into daily use and track a handful of indicators: hours saved, error rate, team satisfaction, and total cost of the solution. Compare everything with the month-one baseline.

At the end, hold a decision meeting with three possible outcomes: scale (the result was good, expand it), adjust (promising but needs fixes), or stop (it did not pay off, and that is fine because you learned cheaply). Every outcome is valid as long as it rests on numbers.

Mistakes that sink AI projects

• Starting too big. Projects that try to fix the whole company die halfway.
• Ignoring people. If staff fear being replaced, they will resist. Be clear that the goal is to remove repetitive work, not jobs.
• Not measuring. Without a baseline, nobody can prove the return.
• Leaving everything to a vendor. Keep an internal owner who understands the process.

What you have on day 90

If you follow the roadmap, you will end with one improved process, real return numbers, a team that has seen AI work, and a prioritized list of next use cases. That is the start of an ecosystem: each new case reuses data, integrations, and lessons from the last one, and the gains compound.

Remember, the goal of 90 days is not to transform the company. It is to prove value at low risk and build the confidence for the next step.

Want to put this to work? Convertty’s senior team runs an AI assessment of your workflows and delivers an implementation plan with 30-day quick wins. Book a call.