There’s a dirty secret in enterprise AI that nobody wants to talk about: most deployments fail not because the technology doesn’t work, but because nobody can actually get it to work where it matters.

I’ve been thinking about this paradox as generative AI promises to transform every business process imaginable. We’re sold on autonomous agents handling customer support, AI copilots revolutionizing healthcare workflows, and intelligent systems that finally unlock decades of trapped enterprise data. The demos are spectacular. The POCs are promising. The contracts get signed.

And then… nothing. Or worse than nothing, a slow death by a thousand integration issues, security reviews, and “unexpected complexities.”

The demo works. Your infrastructure doesn’t

Here’s what’s actually happening: The AI companies have solved the hard technical problem. LLMs work. The models are remarkably capable. The agents can reason, integrate, and respond with genuine utility.

But they’ve completely underestimated the aggregation problem on the other side—the messy, heterogeneous, often barely-documented reality of enterprise infrastructure.

Think about what it actually takes to deploy an AI agent in a Fortune 500 company:

• You’re not deploying to “the cloud”: you’re deploying to their cloud, their on-premises data centers, their hybrid environments with connectivity constraints you didn’t know existed
• You’re not integrating with clean APIs: you’re integrating with mainframes from 1987, SAP instances configured by consultants who’ve long since retired, and “middleware” that’s actually just decades of accumulated technical debt
• You’re not dealing with standard security: you’re dealing with HIPAA for healthcare, FedRAMP for government, SOC 2, GDPR, industry-specific frameworks, and internal security teams who rightfully treat any new system as guilty until proven innocent

The AI works. The enterprise environment is the problem. And that gap—that last mile—is where the vast majority of enterprise AI initiatives go to die.

Enter the Forward Deployed Engineer

This is where the Forward Deployed Engineer becomes not just valuable, but existential for AI companies with enterprise ambitions.

The FDE is the person who actually makes it work. Not in a demo environment. Not in a sandbox. In production, with real data, real constraints, and real consequences.

The gap between AI’s promise and production reality—someone has to bridge it.

What makes this role critical, and fundamentally different from traditional sales engineers or support, is the combination of deep technical capability with ground-truth understanding of customer environments. The FDE doesn’t just understand your product; they understand the customer’s infrastructure better than the customer does.

Consider what Palantir figured out years ago: You can’t just ship software to classified government networks and hope for the best. You need engineers who can get clearances, work in air-gapped environments, and debug issues that can never be reproduced in your corporate office. Their “Baseline” team of FDEs became a competitive moat—competitors with better technology consistently lost because they couldn’t actually deploy in the environments that mattered.

Now multiply that complexity by generative AI’s unique challenges:

The deployment surface area exploded. It’s not just infrastructure anymore—it’s prompt management, RAG pipelines, vector databases, fine-tuning workflows, and real-time integration with enterprise data that wasn’t designed to be accessed this way. Each customer needs their AI configured differently based on their specific workflows, data structures, and use cases.

The stakes got higher. When your AI agent is handling customer interactions, clinical decisions, or financial transactions, “move fast and break things” is not an option. You need someone who can architect reliability from day one, implement proper monitoring, and ensure compliance frameworks are actually followed, not just checked off in a spreadsheet.

The customization became non-negotiable. Unlike SaaS where you could push customers toward standard configurations, AI agents need to deeply understand each enterprise’s specific context. The healthcare AI agent needs to work with their EHR system. The customer support agent needs to integrate with their CRM, ticketing system, and knowledge base. The FDE is the person who makes that integration seamless.

Why this matters now more than ever

Here’s the strategic insight that I think many AI companies are missing: In the age of commoditizing AI capabilities, deployment excellence is becoming the primary competitive differentiator.

OpenAI, Anthropic, Google—they’re all racing toward model capability parity. The foundational models are becoming better and more accessible. The actual AI technology is rapidly becoming table stakes.

But you know what’s not commoditizing? The ability to deploy sophisticated AI systems into complex enterprise environments and actually make them work.

This is why companies like Avaamo are betting heavily on their “AgentOps” teams (their version of FDEs). They understand that having 1,000+ pre-built integrations means nothing if you can’t actually configure them for each customer’s unique environment. They recognize that their “Trust Layer” for security and compliance is only valuable if someone can implement it correctly in healthcare’s HIPAA requirements or financial services’ regulatory frameworks.

The FDE becomes the ultimate moat because they accumulate context that can’t be easily replicated:

• Technical depth across deployment scenarios. After deploying to 100 different enterprise environments, you’ve seen every edge case, every legacy system, every security review objection. This knowledge is invaluable and almost impossible to acquire except through experience.
• Cross-functional translation ability. The FDE speaks technical to engineers, business value to executives, and security requirements to compliance teams. They bridge worlds that traditionally don’t communicate well.
• Rapid iteration capability. When something breaks in production (and something always breaks), the FDE can diagnose and fix it in hours, not weeks. They don’t need to wait for the core product team’s roadmap—they can deliver pragmatic solutions that unblock customers immediately.

The unit economics actually work

Here’s what surprised me as I studied this role: Despite the high cost of deploying skilled engineers to customer sites, the unit economics are remarkably compelling for AI companies.

Traditional enterprise software had to amortize massive sales and implementation costs across relatively fixed license fees. Every hour of customization ate into margins.

But AI systems, particularly those targeting autonomous agents and workflow automation, promise ongoing value that increases over time. The initial deployment investment from FDEs pays back through:

1) Dramatically higher retention. When AI is deeply integrated and actually working, customers don’t churn. They expand.

2) Faster expansion. The FDE who successfully deployed your customer support agent can quickly expand to HR, IT support, and other use cases—they already understand the infrastructure.

3) Premium pricing sustainability. Customers will pay significant premiums for AI systems that actually work reliably in their environments versus demos that theoretically could work.

4) Competitive protection. Once an FDE has successfully navigated a customer’s security reviews, compliance requirements, and infrastructure constraints, replacing your system requires a competitor to do the same—a significant switching cost.

What this means for AI strategy

If you’re building or investing in enterprise AI, the presence or absence of a strong Forward Deployed Engineering capability should be a primary evaluation criteria.

Ask these questions:

• Do they have FDEs who’ve actually deployed in environments similar to yours?
• Can their team get the necessary clearances or accreditations for your industry?
• What’s their track record on time-to-production for complex deployments?
• How do they handle ongoing support in environments their core product team can’t access?

The companies that figure this out—that invest in FDEs before they feel ready, that empower these engineers to make architectural decisions in the field, that treat deployment excellence as a core competency rather than a cost center—these are the companies that will actually deliver on AI’s enterprise promise.

The rest will continue to have impressive demos and disappointing deployments.

Because in the end, the best AI technology in the world is worthless if it can’t actually run where your business happens. And making that work, in all its messy complexity, requires human expertise that can’t be automated away.

That’s what Forward Deployed Engineers do. And in the age of generative AI, they might just be the most important role nobody’s talking about.

The gap between what AI can theoretically do and what it actually does in production is where fortunes are made and lost. The Forward Deployed Engineer is the bridge across that gap.

Sriram Chakravarthy is CTO of Avaamo, an enterprise AI company that has helped Fortune 500 companies successfully deploy enterprise AI at scale.