Forward Deployed Engineers Are a Sign That Enterprise AI Has Hit the Hard Part

Numerous companies have given their teams access to AI tools. That was the easy part.

The harder part starts when Boards and leadership ask a basic business question, “Where is the return?”

The answer rarely comes from a better prompt. It comes from whether AI has been connected to the actual work of the business. The systems of record. The approval paths. The security rules. The field workflows. The data relationships. The handoffs between teams that look simple in a process diagram and become painful in real life.

MIT’s researchers reached the same conclusion. Pilots stall because the tools “don’t learn, integrate poorly, or match workflows” — not because the underlying models fall short.

Forward deployed engineers exist because that work does not happen from a distance. A human has to sit close enough to the operation to see where the data breaks, where other humans currently work around the system, where a dashboard looks clean but the underlying process is not, and where AI would create more risk than value if it were deployed too early. This is the part many AI conversations skip. Observers focus on capability. Operators focus on fit.

A disclosure before I go further: Our team at OneVizion lives in this implementation layer, serving telecom and utility operators every day. This is in large part why we watch this shift closely — and why I will describe it plainly.

Telecom does not run on clean abstractions. The work moves through sites, assets, permits, leases, engineering drawings, construction milestones, vendors, materials, field crews, inspections, closeout packages, finance reviews, and customer commitments. A missed handoff does not stay inside a workflow diagram. It turns into a delayed build, a bad forecast, a customer escalation, or a field team standing in the wrong place with the wrong information.

AI that is disconnected from that reality will stay shallow. It may summarize a document. It may answer a question. It may automate a small task. Those uses have value, but they do not change how infrastructure work gets governed or executed.

We believe that telecom leaders need AI that understands how the work is connected. Not in theory. In the system.

If a project delay is tied to a permitting issue, a vendor dependency, a missing field update, and a downstream revenue commitment, AI has to understand those relationships before anyone should trust it to recommend action. A generic tool sitting outside the operating environment will not see enough of the picture.

That is where deployment matters.

Here is the part leaders should watch most carefully.

Every vendor now promises self-sufficiency. AWS says its engagements are designed so customers are self-sufficient when a deployment ends. OpenAI says its engineers will turn workflow gains into durable systems. Those are the right promises. But a promise in a press release is not a deliverable in a contract.

A strong deployment team does not just build something and leave. It leaves behind documentation, runbooks, architecture decisions, trained internal champions, and a clearer understanding of how AI should work inside the business.

That is the standard. The buyer’s job is to make it enforceable, not to take it on faith.

That means the customer understands what was built, where the data comes from, how the model or agent is governed, what the system is allowed to do, who reviews the output, and how the work gets maintained after the outside engineers leave. Write those into the engagement. Ask for them by name.

Without that, AI deployment becomes another version of the old enterprise technology problem: a promising system that works during the pilot and becomes fragile once it meets the normal pressure of the business.

Telecom has seen that movie before.

There is a catch in this model.

Forward deployed engineers usually represent the vendor that sends them. AWS engineers will naturally help customers build inside AWS. OpenAI’s deployment teams will naturally help customers build around OpenAI technology. Other vendors will do the same.

Skeptics go further. Some argue the whole model is consulting rebranded — services revenue dressed up in product language — and that a vendor staffing thousands of embedded engineers starts to look like a consultancy, not a product company. Sometimes they are right. Buyers should ask hard questions about what they are actually paying for and where those costs and revenues really sit.

None of that makes the model wrong. It makes the buyer’s responsibility clearer.

Leaders should ask what they are actually gaining. Are they building a durable AI capability inside their own operating model, or are they creating a new dependency that will be hard to unwind later?

Those are different outcomes.

A good AI deployment should make the customer smarter. It should clarify architecture, improve governance, expose weak data assumptions, document key decisions, and give internal teams a stronger foundation for the next use case.

A poor deployment leaves behind a black box, a backlog, and a team that still has to call the vendor every time the business changes.

The companies that get value from AI will not be the ones with the longest list of pilots.

They will be the ones willing to look directly at the ugly parts of the operation.

Where do teams re-enter the same information? Where do field updates disappear? Where does leadership get a clean report that everybody knows is incomplete? Where do approvals stall? Where does the customer feel the delay before the system shows the risk?

That is where AI belongs first. Not because those problems are glamorous. Because they are expensive. MIT found the same thing: the highest returns came from unglamorous operational and back-office work, not from the visible projects that attract the budget.

Another point for all business leaders and fans of professional sports and stand-up comedy-flight attendant-prone airlines. The first AWS deployment pods are already embedded with the NFL, the NBA, and Southwest Airlines. Most companies — regional operators, mid-market utilities, growing infrastructure contractors — will not get a pod of hyperscaler engineers for 45 days. For them, the same principle applies with a different playbook: choose platforms and partners that already understand the workflows, hold them to the same self-sufficiency standard, and put AI where the work is actually connected — in the systems of record, not on top of them.

Forward deployed engineers are becoming popular because they point to the work enterprise AI requires now. The next phase is not about giving every employee another tool and hoping value shows up later.

The next phase is deployment into the systems, workflows, records, and decisions that already run the business.

For telecom leaders, the practical test is simple.

If AI cannot see how the work is connected, it should not be trusted to change how the work gets done.