AI strategy and roadmap for a smaller manufacturer or distributor

An AI strategy is a short written plan that says which business problem AI will work on first, which data it may use, what rules it runs under and how you will know it worked. An AI roadmap puts that plan in order, month by month, for the first year. At a smaller manufacturer or distributor, both fit on a few pages, and the owner can write them with the people who do the work. This guide walks through seven steps, from picking the bottleneck to planning months 1 to 12.

Derik Lawlis Founder, ThriveAI

What an AI strategy and an AI roadmap are

An AI strategy records decisions: the problem AI should work on, the data it may read, the rules it runs under and the measure of success. An AI roadmap turns those decisions into an order of work with dates. You revise the roadmap each time a project finishes.

Statistics Canada’s survey for the second quarter of 2026 shows where most firms start. That quarter, 13.1% of manufacturers and 7.9% of wholesalers said they had used AI to produce goods or deliver services in the previous 12 months, against 19.2% across all industries.

A public framework can give the plan a structure. The US National Institute of Standards and Technology (NIST) released its AI Risk Management Framework on January 26, 2023, and says it is “intended for voluntary use.” Its four functions are Govern, Map, Measure and Manage, and the mapping to the steps below is ours. AI governance covers the framework and the Canadian rules.

Step 1: Pick the bottleneck

Start with the business problem, before any tool. A bottleneck is the step that limits how much work the whole company can get through. At a machine shop it might be quoting, when only the owner prices jobs. At a distributor it might be order entry, when one person types every emailed purchase order into the ERP, the enterprise resource planning system that holds orders, stock and costs.

To find it, ask where work waits. Look for the desk with a queue and the person everyone waits on. Write the bottleneck as one sentence with today’s number in it, such as how many days a quote waits for a price.

The AI readiness assessment scores a plant on four things that decide whether AI works there, and AI for manufacturing goes desk by desk through what software can draft in a plant.

Step 2: List the jobs AI could take

Around the bottleneck, list the computer work people do by hand: reading documents, retyping data from one system into another, looking things up, matching documents against each other and drafting replies. Test AI on these jobs first, because a person can check each result.

An AI agent is software that uses an AI model, the program that reads a request and writes the answer, to carry out several steps of a job in a row, such as reading an email, looking up a part and drafting a reply. AI agents for business covers them, and AI for ERP covers what works with the ERP you already run.

Step 3: Check the data

For each job on the list, write down where its data lives and whether a program can read it. The usual places are the ERP, email, the shared drive, drawings and spreadsheets. Three questions sort the list:

• Can a program reach it? A system with an export or an API is easier to connect than one that only shows data on screen. An API is a connection one program uses to call another. If your ERP is an older version on your own server, legacy ERP automation covers the options.
• Does it agree with itself? Part numbers, customer names and prices should match across systems. Check a sample of real records by hand before you rely on them.
• Does it contain personal information? Customer contacts and employee records do, and privacy law then applies.

You do not need to train a model on your files. With retrieval-augmented generation (RAG), the system finds the records that answer a question and hands them to the model with the question, which AWS says works “without the need to retrain the model.” Live systems can be connected through the Model Context Protocol (MCP), “an open-source standard for connecting AI applications to external systems,” in the words of its maintainers.

In Quebec, section 3.3 of the private-sector privacy act requires a privacy impact assessment, a written review of the risks, for “any project to acquire, develop or overhaul an information system or electronic service delivery system” that involves personal information. If the first project will read customer or employee records, plan that assessment at the start and ask your counsel how the rule applies.

Step 4: Set the rules for security and approvals

Write the rules down before the first project starts. The Canadian Centre for Cyber Security’s generative AI guidance says: “Your organization should establish a plan that identifies policies on how AI should be used and the content that is allowed to be generated.” At a smaller company, the rules answer four questions:

1. Which AI tools are allowed? List them, and list the ones staff may not use for work. The AI policy template gives a starting text.
2. What data may each tool see? The same guidance says: “Avoid providing PII or sensitive corporate data as part of the queries or prompts.” PII is personally identifiable information. If AI is to read that kind of record, it needs a setup your company controls, which private AI for business describes.
3. Where may data be stored and processed? Ask for storage and processing as two separate answers. Data sovereignty in Canada and sovereign AI explain the difference.
4. Who approves the output? Name the person who checks each kind of AI draft before it reaches a customer or changes a record, a practice called human in the loop. The Cyber Centre’s top 10 AI security actions, published in May 2026, include: “Ensure that human-in-the-loop oversight and execution controls are in place.” Human in the loop shows how to set up that step.

Two legal points shape the rules. Principle 4.1 of PIPEDA, the federal private-sector privacy law, asks each organization to “designate an individual or individuals who are accountable” for its compliance. In Quebec, section 12.1 of the private-sector act applies to a decision “based exclusively on an automated processing” of personal information: the business must tell the person concerned and give them the chance to submit observations to a staff member who can review it. Ask your counsel how both apply, and see AI security for the technical side.

Step 5: Choose the first project

Pick one job from the list and finish it before you start a second. A good first project passes most of these tests:

• It sits on the bottleneck, so a gain shows up in what the company ships.
• It happens every day, so there are enough real examples to test on.
• The data it needs passed the checks in step 3.
• A person can check each result quickly, and a mistake is caught before it reaches a customer.
• You can measure it today, before any AI is involved.

Order entry from emailed purchase orders and first drafts of quotes are worth testing against this list. A job that runs a few times a year makes a poor first project, and so does one where a mistake would reach a customer unchecked.

Step 6: Decide how you will measure it

Record the numbers before the project starts, because a baseline is hard to rebuild afterwards. NIST describes its Measure function as using “quantitative, qualitative, or mixed-method tools, techniques, and methodologies to analyze, assess, benchmark, and monitor AI risk and related impacts.” For one project at a smaller company, three measures are enough:

• The business number. Minutes per order, days per quote, errors per hundred documents or overtime hours on the bottleneck desk.
• The test result. The share of real past jobs the system gets right, rerun after every change. AI evals shows how to build that test.
• Use. How much of the eligible work goes through the tool, and how often people correct its drafts.

Set a review date and write down the result that would make you stop, change course or continue. What the system costs to run belongs in the same review, and the cost of AI for a small business breaks that cost into parts.

Step 7: Plan months 1 to 12

The table shows one way to lay out the first year. Treat the months as the order of the work, because how long each step takes depends on the job, the data and your team’s time.

AI implementation covers each build and AI training the training step. For outside help, AI consulting sets out who sells it and what to ask, and AI for small business gives a wider view for companies your size.

How ThriveAI helps

ThriveAI is an AI engineering company in Ottawa that serves smaller manufacturers and distributors in Ontario and Quebec. It builds private AI systems on your own data and works hands on with your team to clear the manual computer work behind your bottleneck. Derik Lawlis, the founder, leads every project and stays close to the build.

ThriveAI picks the one job costing you the most and builds it, for a fixed price, in weeks. It runs on your own systems, and if you stop there it is yours to keep. The full build, which connects the systems you already run, only happens if that first stage earned it.

The platform ThriveAI builds on is designed to keep each client’s data on its own server in Canada. You choose the AI model that reads it: one on that server, or a hosted model, one that runs on the provider’s servers, under a written agreement that the provider keeps nothing after answering. A hosted model may process requests outside Canada, so the contract names the model. Nothing is sent or saved in your systems until the person responsible approves it. About ThriveAI covers the company, and ThriveAI vs alternatives compares other options.