Where AI fits in a $2M–$10M business: 5 workflows to start with

Where AI fits in a $2M-$10M business: five practical workflows to start with, from quotes to reporting, and how to pick the first one. Rochester, NY.

A company with $2M to $10M in revenue has a lot of manual work: retyping data from one system into another, copying details between email and the CRM, sorting a shared inbox, chasing missing information. The same few people also handle sales, customers and operations, so those hours come out of work that needs their judgment, like pricing a job or handling a difficult account. Companies this size also have few approval layers, so a change can go live in weeks. That combination makes it a good size for AI. Below are five workflows, ordered from least setup to most, followed by a test for choosing your first one.

1. Customer email and inbox triage

Requests go missing in shared inboxes like info@, orders@ and service@ because one person has to read every message, work out what it is, and pass it along.

AI can summarize long threads, tag incoming messages (order, complaint, quote request, billing), and produce a daily list of open items. Tagging and the open-items list stay inside the company, and a mis-tagged message gets caught and moved in seconds, so they can run without anyone checking each one. Replies go to customers, so AI drafts them and a person approves them before sending.

2. Quotes, proposals and estimates

The usual routine: find an old proposal, copy it, change the details, and hope no mention of the previous customer survives.

AI can draft the first version from the customer's request, your standard terms and past proposals. It can pull the scope out of the customer's email and compare a finished draft against the original request.

Consider a commercial contractor that receives a request for quote by email, with drawings and a list of requirements. An AI tool reads the email and attachments, lists the scope items, and drafts a proposal on the company template with standard exclusions. A proposal goes to a customer and commits the company to a price, so the estimator still sets every price and reviews every line, starting from a draft.

3. Internal reporting and month-end summaries

Someone exports data from the accounting system, the CRM and a few spreadsheets, then rebuilds the same summary for the owner every month.

AI can combine the exports, flag changes from last month, and write a plain-language summary. The exports remain the official numbers, and AI produces the assembly and first-draft commentary. Claude Cowork can read several files in one request and return a finished summary. If your reporting lives in Microsoft 365, Copilot can do part of this inside Excel.

4. Onboarding, SOPs and internal knowledge

Key procedures live in a few employees' heads and in a folder of outdated documents.

AI can turn a recorded walkthrough or rough notes into step-by-step procedures, draft onboarding checklists for each role, and make existing documents searchable so new hires ask the documents before they ask a coworker. It saves little time in the first week. The payoff comes each time someone is hired or a senior person is on vacation.

5. Data entry between systems

Reading a PDF invoice, purchase order or form and typing it into another system, or copying customer details from email into the CRM and then into the job tracker.

AI can extract the fields, check them against expected values, and enter them. Records that move money, such as invoices and payments, go to a queue for someone to approve first. This is where AI meets traditional workflow automation, connecting apps so data moves without retyping. It needs the most setup of the five, so it usually comes second or third.

Choosing the first one

A workflow is a good first project if it:

• runs weekly or daily
• involves reading or writing email, documents, notes or reports
• follows roughly the same steps each time
• stays internal, so a mistake is cheap and easy to catch and fix
• produces output reliable enough to run without a person checking every result
• takes hours from people whose time is needed elsewhere

Leave customer-facing and money-related outputs for later projects, where you keep a person reviewing each one. Skip anything that depends on judgment calls that differ every time.

Among the candidates that pass, compare three things:

1. Hours per week. Ask the people who do the work, since managers tend to guess low.
2. Cost of a mistake. Pick one where a wrong result stays internal and is easy to spot and correct.
3. An owner. Name one person responsible for whether it works.

Run it for a few weeks, time the task before and after (rough estimates are fine), and use what you measured to choose the next one.

Training, implementation, or both

Inbox summaries and proposal drafts mostly require your team to use an AI tool well, so hands-on AI training comes first. Moving data between systems and automating month-end reporting require connecting tools, which is AI implementation. Most companies end up doing both, in whichever order fits the first workflow they choose.

Working with an AI consultant in Rochester, Buffalo, or remotely

I'm based in Rochester, NY, and work on-site with businesses across Rochester, Buffalo and Western New York, and remotely with companies elsewhere. The process is the same in both cases: map how the business runs, pick one workflow, get it working, then move to the next. More on my AI consulting work in Rochester, NY.