Most AI pilots fail due to poor change management and a lack of business context. A successful model involves embedding vendor engineers within the client's team to handle agent onboarding, systems integration, and process customization, ensuring the AI works within the company's unique environment.

Embed engineers directly with customers to hear feedback and ship solutions, often on the same day. This radical structure eliminates layers of communication (Product Managers, Customer Success) and scales the 'founder energy' of talking to users and immediately building what they need.

To successfully automate complex workflows with AI, product teams must go beyond traditional discovery. A "forward-deployed PM" works on-site with customers, directly observing workflows and tweaking AI parameters like context windows and embeddings in real-time to achieve flawless automation.

Instead of a traditional product roadmap, give engineers ownership of a broad "problem space." This high-agency model pushes them to get "forward deployed" with customers, uncover real needs, and build solutions directly. This ensures product development is tied to actual pain points and fosters a strong sense of ownership.

Harvey's Forward Deployed Engineering team isn't just for building custom solutions. It's a strategic product discovery tool. By embedding engineers with large clients who have undefined GenAI needs, Harvey identifies and builds the next set of platform features, effectively using customer problems to pave its future roadmap.

Unlike typical software companies that build addictive products or simply fulfill requests, Palantir's approach is to anticipate and build what its partners *ought* to want in the future. This radical, value-driven strategy builds deep trust and creates an indispensable long-term position with the client.

Engineering often defaults to a 'project mindset,' focusing on churning out features and measuring velocity. True alignment with product requires a 'product mindset,' which prioritizes understanding the customer and tracking the value being delivered, not just the output.

Walmart reframed planning around desired outcomes, not feature lists. This gave engineering teams the flexibility to innovate on solutions, increasing engagement and productivity, despite initial resistance from leadership accustomed to feature-based roadmaps.

When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.