CASE STUDY · PRODUCT LEADERSHIP
Enterprise AI product strategy
Connecting business priorities, product investment, and architecture decisions for an enterprise AI product.
The challenge
An enterprise AI product only works when the technology clearly connects to business value. My job is to set that direction: which problems the product should solve, where AI actually helps, and what the organization needs to deliver it.
How I lead it
I lead product direction at the executive level, tying together roadmap priorities, investment and architecture decisions. With our product and architecture leaders, I look at each capability against the business process it supports, how it fits with the systems customers already run, and what it will ask of delivery and operations.
The decisions that matter
Part of that is deciding where ordinary automation is enough and where AI is worth the extra complexity. Another part is working out how outside agent capabilities can fit into a governed product. Our recommendation is to adapt selected agents to the product’s existing runtime, and we are upfront about the tradeoffs in compatibility and maintenance.
The goal
The goal is a product customers can use for meaningful work, and one we can deliver and support consistently. My part is keeping the commercial purpose, product priorities, and technical choices pointed at that goal.
What I’d do differently
I’d get the product direction and the reasons behind it to engineers sooner. I was too slow to do that, and the team needed that context to understand the priorities and take ownership.