Instead of large, multi-year software rollouts, organizations should break down business objectives (e.g., shifting revenue to digital) into functional needs. This enables a modular, agile approach where technology solves specific problems for individual teams, delivering benefits in weeks, not years.
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During a transformation from services to product, identify and commercialize the reusable tools that services teams have already built to support clients. Instead of starting from scratch, productizing these existing "mini-products" aligns them with the broader product strategy, saves development time, and leverages proven solutions.
The path to adopting AI is not subscribing to a suite of tools, which leads to 'AI overwhelm' or apathy. Instead, identify a single, specific micro-problem within your business. Then, research and apply the AI solution best suited to solve only that problem before expanding, ensuring tangible ROI and preventing burnout.
Treat AI initiatives as two separate strategic pillars. Create one roadmap focused on internal efficiency gains and cost reduction (productivity). Maintain a separate roadmap for developing new, revenue-generating customer experiences (growth). This prevents conflating internal tools with external products.
When employees are 'too busy' to learn AI, don't just schedule more training. Instead, identify their most time-consuming task and build a specific AI tool (like a custom GPT) to solve it. This proves AI's value by giving them back time, creating the bandwidth and motivation needed for deeper learning.
To get product management buy-in for technical initiatives like refactoring or scaling, engineering leadership is responsible for translating the work into clear business or customer value. Instead of just stating the technical need, explain how it enables faster feature development or access to a larger customer base.
Forcing innovations to "scale" via top-down mandates often fails by robbing local teams of ownership. A better approach is to let good ideas "spread." If a solution is truly valuable, other teams will naturally adopt it. This pull-based model ensures change sticks and evolves.
Many B2B companies begin by customizing software for one client, then stacking new custom projects for subsequent clients. They believe they are building a product, but are actually creating a complex, unscalable monolith that is difficult to maintain and evolve.
In government, digital services are often viewed as IT projects delivered by contractors. A CPO's primary challenge is instilling a culture of product thinking: focusing on customer value, business outcomes, user research, and KPIs, often starting from a point of zero.
To sell large transformation projects, present the ambitious "North Star" goal but break it into sequential stages. Critically, Stage 1 must deliver tangible business value on its own. This approach wins over skeptics by providing an early return on investment, securing the momentum and buy-in needed for subsequent stages.
To create transformational enterprise solutions, focus on the core problems of the key buyers, not just the feature requests of technical users. For healthcare payers, this meant solving strategic issues like care management and risk management, which led to stickier, higher-value products than simply delivering another tool.