Buyers now use AI to arrive with a full research dossier on your product, pricing, and competitors. This changes the GTM role from persuading customers with clever messaging to enabling their decision-making. The new focus is helping buyers quickly experience your product's value on their own terms.

AI's most significant impact is not just campaign optimization but its ability to break down data silos. By combining loyalty, e-commerce, and in-store interaction data, retailers can create a holistic customer view, enabling truly adaptive and intelligent marketing across all channels.

Customers now expect DaaS vendors to provide "agentic AI" that automates and orchestrates the entire workflow—from data integration to delivering actionable intelligence. The vendor's responsibility has shifted from merely delivering raw data to owning the execution of a business outcome, where swift integration is synonymous with retention.

The long-discussed alignment of sales and marketing is no longer optional; AI makes it mandatory. To effectively use AI insights for GTM, organizations must operate as a single, harmonious unit, possibly even merging the departments organizationally to ensure seamless, data-driven execution.

Historically, channel agents focused on front-end sales and were often blind to back-end customer churn. Sophisticated partners now use data analytics and AI to identify churn risks, pinpoint cross-sell opportunities, and actively manage their existing revenue base.

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.

The next frontier in e-commerce is inter-company AI collaboration. A brand's AI will detect an opportunity, like a needed digital shelf update, and generate a recommendation. After human approval, the request is sent directly to the retailer's AI agent for automatic execution.

Go beyond using AI for data synthesis. Leverage it as a critical partner to stress-test your strategic opinions and assumptions. AI can challenge your thinking, identify conflicts in your data, and help you refine your point of view, ultimately hardening your final plan.

In a B2B supplier or distributor model, success depends on going downstream. You must understand not only your direct partner's business drivers and KPIs but also the needs of their end-customer. This allows you to align strategy across the entire value chain.