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.

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.

The forward-deployed engineer (FDE) model, using engineers in a sales role, is now a standard enterprise playbook. Its prevalence creates a contrarian opportunity: build AI that automates the FDE's integration work, cutting a weeks-long process to minutes and creating a massive sales advantage.

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.

The process of building a custom AI agent forced Newell's teams to collaborate more closely than in traditional software rollouts. It sparked critical conversations about existing versus ideal workflows, bringing people together to solve problems and improving organizational connectivity as a positive side effect.

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.

Instead of integrating with existing SaaS tools, AI agents can be instructed on a high-level goal (e.g., 'track my relationships'). The agent can then determine the need for a CRM, write the code for it, and deploy it itself.

The primary advantage of building your own AI tool is the ability to instantly respond to customer needs. Unlike off-the-shelf software with long roadmaps, non-technical teams can implement and ship simple customer feature requests on the same day, creating a magical user experience.

Visual AI tools like Agent Builder empower non-technical teams (e.g., support, sales) to build, modify, and instantly publish agent workflows. This removes the dependency on engineering for deployment, allowing business teams to iterate on AI logic and customer-facing interactions much faster.