The Role of Forward Deployed Engineers in Shaping AI Startups and Business Success
Artificial intelligence startups face a unique challenge: they often create entirely new workflows rather than improving existing ones. This means founders cannot simply build products in isolation. Instead, they need technical experts embedded directly with customers to understand complex, real-world problems and rapidly prototype solutions. This is where Forward Deployed Engineers (FDEs) become essential. Bob McGrew highlights how FDEs are central to AI startups because they help discover workflows, turn customer challenges into product features, and avoid becoming custom project shops.
This post explores what Forward Deployed Engineers are, how they differ from consultants, and why they are crucial for AI startups and business owners aiming for success.
What is a Forward Deployed Engineer?
A Forward Deployed Engineer is a technical professional who works closely with customers, often on-site or embedded within customer teams. Unlike traditional engineers who focus on building software in isolation, FDEs engage directly with users to:
Understand messy, real-world workflows
Prototype solutions quickly based on customer feedback
Identify patterns in customer problems that can improve the core product
FDEs act as a bridge between product development and customer needs. Their role is not just to solve one-off problems but to extract insights that make the product better for all users.
How FDEs Differ from Consultants
Consultants typically solve a specific problem for a single customer and then move on. FDEs solve customer problems while simultaneously gathering knowledge to enhance the product for future customers. This dual focus helps AI startups avoid becoming custom project shops, where each deployment is unique and hard to scale.
Instead, FDEs use customer deployments as a product discovery engine. They turn repeated challenges into reusable product capabilities, allowing startups to build scalable solutions that grow with their customer base.
Why FDEs Are Vital for AI Startups
AI startups face a different landscape compared to traditional SaaS companies. Traditional SaaS products often replace existing workflows with better tools. AI startups, especially those building AI agents, frequently need to discover the workflow itself. This discovery requires deep customer engagement and rapid iteration.
Discovering New Workflows
AI agents are a new category of software with no clear incumbent products. This means startups cannot rely on existing workflows or user habits. FDEs help by:
Embedding with customers to observe and understand how work is done
Identifying inefficiencies and opportunities for automation
Rapidly prototyping AI-driven solutions that fit real needs
Doing Things That Don’t Scale at Scale
The phrase “doing things that don’t scale at scale” captures the paradox of FDE work. High-touch, hands-on customer engagement may seem inefficient, but it generates valuable insights that lead to scalable products. FDEs turn these early, labor-intensive efforts into reusable features that benefit many customers.
How FDEs Impact Business Owners
For business owners, especially those leading AI startups, understanding the value of FDEs can shape strategy and growth.
Building Products That Solve Real Problems
FDEs ensure that product development is grounded in actual customer needs. By working closely with users, they help founders:
Avoid building features based on assumptions
Prioritize development based on real pain points
Create products that customers want to adopt and expand
Measuring Success Differently
Traditional SaaS metrics focus on seats sold or usage rates. For AI startups using FDEs, success metrics shift toward:
Outcome value: How much the product improves customer results
Contract expansion: Whether deployments lead to larger agreements
Product reuse: How often solutions can be applied across customers
These metrics reflect the deeper impact of FDEs in turning customer insights into scalable products.
Avoiding the Custom Project Trap
Without FDEs, startups risk becoming custom project shops, where each customer requires a unique solution. This limits growth and drains resources. FDEs help avoid this by:
Extracting patterns from customer problems
Building reusable product capabilities
Enabling the company to scale without replicating work
Practical Examples of FDE Impact
Example 1: AI Customer Support Agent
An AI startup building a customer support agent embedded an FDE with a large client. The FDE discovered that the client’s support workflow involved multiple handoffs and manual data entry. By prototyping an AI agent that automated data capture and routing, the FDE helped reduce support time by 30%. The insights gained led to a product feature that other clients could use, increasing contract size and product adoption.
Example 2: AI in Supply Chain Optimization
A startup developing AI for supply chain management placed FDEs at several pilot sites. The engineers uncovered hidden bottlenecks and manual processes that were not documented. Their work led to new AI models that predicted delays and optimized inventory. These models became core product features, helping the startup expand contracts and improve customer outcomes.
How Business Owners Can Leverage FDEs
Business owners should consider the following to maximize the value of FDEs:
Embed FDEs early in the customer engagement process to discover workflows before building products.
Encourage rapid prototyping and iteration based on real customer feedback.
Focus on pattern recognition to turn custom solutions into reusable features.
Measure success by customer outcomes and contract growth, not just usage numbers.
Avoid treating FDEs as consultants who deliver one-off projects; instead, use them as product discovery engines.
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