Why small businesses need forward deployed engineering

I have spent most of my career standing in an awkward middle. I understand the business owner who says, “I know this process is costing us, but I cannot stop the company for six months to replace everything.” I also understand the builder looking at the same process and thinking, “There is a cleaner system hiding in here.”

For years, I called that work business automation, product development, AI consulting, or simply figuring things out. Forward deployed engineering gives it a better name.

I did not read about a hot role and decide to borrow the title. I recognized the title because it described what I had already learned the hard way: the closer the builder gets to the real work, the better the system becomes.

The FDE idea is simple: put the builder close to reality

A Forward Deployed Engineer, or FDE, works directly with the customer and owns the path from a messy problem to a useful deployed system. OpenAI describes the role as spanning discovery, technical scoping, system design, build, production rollout, adoption, and measurable workflow impact. Palantir's earlier description makes the same point in plainer language: the engineer embeds with the customer and implements in collaboration with end users.

That sounds obvious until you look at how technology is normally bought. The business explains the problem to a salesperson. The salesperson summarizes it for an account manager. The account manager hands it to a project lead. The project lead creates tickets for developers who may never meet the person doing the work.

Every handoff tidies the story. Unfortunately, the mess is usually where the truth lives.

The strange spreadsheet, the exception only Joanne knows how to handle, the approval that happens over text message, the customer who refuses the portal, the system that technically has an API but nobody trusts: those details decide whether the new workflow works.

Small businesses cannot afford a long chain of translation

A large enterprise can hire a strategy firm, a systems integrator, a software vendor, a change-management team, and an internal program office. A 25-person service business cannot. It should not try.

Small and medium businesses need a tighter loop between understanding and doing. The person mapping the workflow should understand what is technically possible. The person building the proof should know why the exception matters. The person training the team should know what compromises were made and where the system needs a human.

This is why my background as a business owner matters as much as my technical work. I have had to make payroll decisions, sell services, support customers, maintain products, and live with yesterday's architectural choices. I do not see a workflow as boxes on a slide. I see what happens when one of those boxes breaks on a Tuesday morning.

AI makes the forward deployed model more important

Traditional software is mostly deterministic. If the rule says a complete form moves to the next stage, it moves. AI is different. The same model can be impressive on ten examples and unreliable on the eleventh because the language is unusual, the document is incomplete, or the business rule was never written down.

That does not make AI unusable. It changes the deployment job.

An AI workflow needs representative examples, an agreed definition of a good result, explicit boundaries, and a clear escalation path. It needs to run beside the current process before it is trusted to act alone. It needs somebody to watch what happens after launch and turn failures into better tests.

This is the logic behind our AI Workflow Proof Sprint. We choose one workflow, collect the awkward cases, build a working proof, and document where it succeeds or needs review. The point is not to force every idea into production. Sometimes the most valuable result is learning that ordinary software would solve the problem more reliably.

The workflow comes before the model

The fastest way to waste money on AI is to begin with the sentence, “We want an agent.” An agent is an implementation choice, not a business requirement.

I would rather begin with: “Every Friday, two people spend the afternoon reconciling these records, and the exceptions still reach the owner.” Now we have something to examine. Where does the information come from? Which decisions are rules? Which cases require judgment? What happens when the data is missing? What would improve if this worked?

Our deployment loop is deliberately plain:

1. Map the real work. Follow the process as it actually happens.
2. Choose where AI belongs. Use regular software where regular software is better.
3. Prove it works. Test representative cases and define the failure boundaries.
4. Deploy it safely. Start with observation and human approval before expanding authority.
5. Measure and improve. Watch the workflow, not just the model output.

You can call that forward deployed engineering. You can call it good implementation. I care more that it happens.

An FDE should be willing to tell you not to use AI

This may be the most important test. If the person advising you makes more money every time the answer is “AI,” the incentives are wrong.

Some work needs a form with better validation. Some needs a dashboard. Some needs an integration between two systems. Some needs a staff conversation because the process is unclear. AI becomes valuable when the inputs are messy, the task involves language or judgment, and the business can define what acceptable performance looks like.

A useful FDE separates three layers:

• Software for predictable rules, records, routing, and permissions.
• AI for documents, language, classification, drafting, extraction, and context-heavy support.
• People for accountability, exceptions, relationships, and consequential decisions.

The best workflow is rarely “AI does everything.” It is a well-designed agreement between all three.

Forward deployed does not mean permanently embedded

Small businesses hear “embedded engineer” and picture an expensive person occupying a desk indefinitely. That is not the only model.

The important thing is access to reality. A Strategy Day can put the right people in one room and expose the real workflow. A focused proof sprint can test the highest-risk assumption. A build engagement can move through staged deployment. Ongoing support can shrink once the team and the system are stable.

Sometimes I need to be on-site. Sometimes I need access to sample documents, the current software, and the person who handles the exceptions. Sometimes the best thing I can do is watch a screen share and ask, “Why did you open that spreadsheet when the CRM was right there?”

That question usually has a very good answer.

What a small business should expect from an FDE

The title is new to many business owners, so the standard needs to be concrete. A Forward Deployed Engineer should leave behind more than clever code.

• A map of the current workflow, including exceptions and ownership.
• A clear reason for using AI, software, or a human at each important step.
• A working proof tested against representative examples.
• Documented failure modes and escalation rules.
• A staged deployment plan that protects the current operation.
• A way to measure adoption and workflow improvement.
• Enough documentation and training that the business is not trapped by the builder.

At BotLogix, that sequence connects directly to our client journey: a workflow fit call, Strategy Day, proof sprint, production deployment, and ongoing AI operations only where they are useful.

I am comfortable claiming the role because I am accountable for the work

There is always a risk in adopting a new title. The internet will fill up with people who add FDE to a profile after reading three articles. I do not want to contribute to that.

My claim is narrower and practical. I have been building business systems since 2018. I operate my own software products. I work directly with owners and operators. I map the business problem, make technical decisions, contribute to the build, deploy the system, and live with what happens next. The proof-of-work archive exists so you can inspect that history rather than taking the title on faith.

Forward deployed engineering is not a badge that makes someone credible. It is a promise to stay close to the work and own the distance between “the demo worked” and “the business works better.”

The practical starting point is one workflow

Do not begin by transforming the whole company. Bring one repeated workflow that wastes time, delays customers, creates avoidable rework, or depends too heavily on one person.

We will map it. We will decide whether AI belongs. If it does, we will prove the riskiest part before asking you to trust it. If it does not, I will tell you.

That is the part of forward deployed engineering I want small businesses to have access to: not the title, the proximity, or the hype. The accountability.

Sources and further reading: OpenAI's Forward Deployed Engineer role, Palantir's explanation of the FDSE role, and Scale AI's GenAI FDE responsibilities.