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.

Companies that experiment endlessly with AI but fail to operationalize it face the biggest risk of falling behind. The danger lies not in ignoring AI, but in lacking the change management and workflow redesign needed to move from small-scale tests to full integration.

AI agent tools require significant training and iteration. Success depends less on software features and more on the vendor's commitment to implementation. Prioritize vendors offering a dedicated "forward-deployed engineer" who will actively help you train and deploy the agent.

For AI tools that fundamentally alter workflows, a simple software deployment is insufficient. Success requires a dedicated team of 'forward deployed' experts (e.g., ex-lawyers for legal tech) to manage the enormous change management undertaking, ensuring adoption and proficiency across the client organization.

Companies once hired siloed 'digital experts,' a role that became obsolete as digital skills became universal. To avoid repeating this with AI, integrate technologists into current teams and upskill existing members rather than creating an isolated AI function that will fail to scale.

Unlike traditional SaaS, AI agents require weeks of hands-on training. The most critical factor for success is the quality of the vendor's forward deployed engineer (FDE) who helps implement, not the product's brand recognition or feature superiority.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

To successfully implement AI, approach it like onboarding a new team member, not just plugging in software. It requires initial setup, training on your specific processes, and ongoing feedback to improve its performance. This 'labor mindset' demystifies the technology and sets realistic expectations for achieving high efficacy.

To overcome high AI pilot failure rates, companies like Pace use "forward deployed engineers" (FDEs). These founder-type individuals work onsite, deeply understand customer problems, and do whatever it takes—from prompt tuning to data cleaning—to ensure successful production deployment.