Forward Deployed Engineering: Finding the Model That Fits

Forward-deployed engineering may be one of the fastest-growing functions in enterprise AI.

It may also be one of the least standardized.

Ask five companies what a forward-deployed engineering team does, and you'll likely get five different answers. Some use FDEs to bridge product and customer teams. 

Others use them to accelerate adoption. Some treat them as an extension of the product. Others position them as a specialized post-sales function.

At Rocketlane's Propel 26 conference, a panel moderated by Rocketlane CEO Srikrishnan Ganesan brought together leaders from Harvey, ServiceNow, Finn, and Alteryx to explore a question many organizations are wrestling with:

What should a forward-deployed engineering function actually look like?

The answer was surprisingly consistent. There is no universal model.

The right approach depends on your product, your customers, and the specific gap you're trying to close between technology and business outcomes.

What Is Forward Deployed Engineering? Four Companies, Four Definitions

One of the most interesting takeaways from the discussion was how differently each organization approaches forward-deployed engineering.

At Harvey, an AI platform built for legal professionals, the most effective forward-deployed engineers aren't traditional engineers at all.

They're lawyers.

As Alex Laverty explained, domain expertise is often the difference between adoption and resistance. Legal professionals trust people who understand how legal work actually happens. 

Harvey's forward deployed engineers work directly with law firms and in-house legal teams, helping them build and deploy AI-powered workflows that fit naturally into legal operations.

ServiceNow takes a very different approach.

Miku Jha says, forward-deployed engineering function sits inside product and R&D rather than go-to-market teams. Every customer engagement is expected to create reusable assets, product enhancements, or learnings that benefit future customers.

The philosophy is simple: If a solution only helps one customer, it's customization. If it helps every future customer, it's product development.

At Alteryx, forward-deployed engineering serves as a commercial acceleration function.

Teams engage in highly focused, time-boxed projects designed to prove value quickly. Once the objective is achieved, ownership transitions to the broader customer-facing organization while FDE resources move on to the next opportunity.

Finn, formerly Intercom's AI business, operates somewhere in between.

Their deployment engineers are deeply connected to both customer delivery and product innovation, shipping new enhancements continuously while optimizing for automation outcomes. Products and services are increasingly operating as a single motion.

Four companies. Four different structures. Four different objectives. None of them is wrong.

The lesson is that forward-deployed engineering isn't a job description.

It's a strategy.

What Makes a Successful Forward Deployed Engineering Team?

While the operating models varied significantly, the characteristics of successful forward-deployed engineers were remarkably similar.

Several panelists described a combination of skills that's difficult to find in a single person.

First, technical fluency.

Forward-deployed engineers need to understand AI systems deeply enough to build solutions, not just talk about them.

Second, product thinking.

Every customer engagement should generate reusable knowledge that improves the platform over time.

Without that feedback loop, organizations risk creating expensive custom work rather than scalable solutions.

Third, customer empathy.

The most valuable opportunities are rarely handed to teams in a neatly defined requirements document.

Forward-deployed engineers must identify problems that customers haven't fully articulated.

As Rajkumar Raj from Alteryx noted, successful engagements require a business stakeholder whose KPIs are directly tied to the outcome.

Without business ownership, many AI initiatives become what he described as an "AI graveyard"—well-funded experiments that never produce meaningful value.

Landon McCaig added another challenge. Building the team itself.

Organizations can hire software engineers and teach customer engagement skills. Or they can hire implementation professionals and teach technical skills.

Neither path is easy.

Both require deliberate enablement and clear career development paths.

The common thread across all four companies was clear: Forward-deployed engineering succeeds when organizations intentionally develop technical expertise, product thinking, and customer leadership together.

How to Qualify Customers for Forward Deployed Engineering—and When to Walk Away

One of the strongest themes from the session was customer qualification.

The biggest challenge in most AI initiatives isn't the technology.

It's readiness.

Rajkumar shared a simple framework for determining whether a customer is a good fit for a forward-deployed engineering engagement.

Is the problem meaningful enough?

The opportunity needs to be large enough to justify investment and organizational change.

Is the data accessible?

Many AI projects fail because the necessary data is fragmented, unavailable, or governed by restrictive policies.

Do the right stakeholders exist?

Technical contacts alone aren't enough.

Successful projects require executive sponsorship and business ownership.

Is someone accountable for outcomes?

Someone inside the customer organization must ultimately own success after the engagement concludes.

If those conditions aren't present, even strong technical solutions can struggle to create lasting value.

One particularly interesting practice discussed during the session was the concept of a "kill trigger."

Rather than forcing every engagement forward, some teams deliberately disengage when qualification criteria aren't met.

The customer relationship remains intact. The opportunity remains open.

But scarce forward-deployed engineering resources are redirected toward initiatives with a higher probability of success.

It's a reminder that focus is often more valuable than persistence.

Why Delivery Visibility Matters in Forward Deployed Engineering

While every panelist described a different operating model, a common challenge emerged beneath the surface.

Forward-deployed engineering teams often manage highly specialized, time-bound engagements involving multiple stakeholders, evolving requirements, and aggressive timelines.

That creates a visibility challenge. 

• How do leaders know whether engagements are delivering value?
• How do teams track progress across multiple workstreams?
• How do organizations measure outcomes consistently when every engagement looks different?

This is where operational rigor becomes critical.

The most effective teams don't simply build solutions.

They build repeatable delivery systems around those solutions.

Clear milestones. Defined ownership.

Business outcomes tied to project execution.

Platforms like Rocketlane increasingly play a role in helping organizations orchestrate these complex engagements by connecting delivery execution, customer collaboration, project health, and outcome tracking into a single system of record.

As forward-deployed engineering scales, success depends not only on what gets built but also on how consistently organizations can deliver value.

4 Key Takeaways from Four Different Forward Deployed Engineering Models

Your Forward Deployed Engineering Model Should Reflect Your Customer

Harvey's legal experts, ServiceNow's product-centric model, and Alteryx's commercial acceleration approach all solve different problems.

Start with how customers realize value rather than copying another company's structure.

Great Forward Deployed Engineers Combine Three Rare Skills

Technical fluency, product thinking, and customer empathy rarely exist together naturally.

Organizations need deliberate hiring and enablement strategies to develop all three.

Customer Qualification Matters More Than Use-Case Selection

Data readiness, executive sponsorship, and business ownership often determine success long before technology enters the picture.

Start Small and Prove the Model

Several panelists emphasized beginning with small teams and focused engagements before scaling.

Successful forward-deployed engineering functions evolve through iteration, not massive launches.

Conclusion

Forward-deployed engineering is still evolving.

Every company on the panel was actively refining its model, adjusting team structures, improving qualification processes, and experimenting with new ways to measure success.

That's what makes the function so interesting.

There isn't a single blueprint.

What matters is understanding the problem you're trying to solve.

For some organizations, forward-deployed engineering accelerates adoption.

For others, it strengthens product feedback loops.

For others, it becomes a strategic bridge between AI capabilities and customer outcomes.

The structure will vary. The fundamentals won't.

• Start with the right customers.
• Focus on measurable outcomes.
• Build strong feedback loops.
• Create repeatable delivery systems.

And remember that the goal isn't simply deploying technology.

It's helping customers achieve results they couldn't achieve before.

The companies that get that balance right won't just build successful forward-deployed engineering teams.

They'll build stronger customer outcomes as well.