Effective AI adoption isn't about force-fitting a new technology into a workflow. Leaders should start by identifying a significant business challenge, then assemble an agile team of business experts and technologists to apply AI as a targeted solution, ensuring the effort is driven by real-world value.

Avoid vague, company-wide AI mandates. Instead, apply a maturity framework to individual processes (e.g., account research). This approach builds a practical roadmap, moving specific use cases up the maturity ladder as needed and preventing costly over-engineering.

To avoid "AI slop"—the proliferation of low-quality AI outputs—Dell's CTO advocates for a disciplined, top-down strategy. Instead of letting tools run wild, they focus on a small number of high-impact use cases with clear business outcomes, ensuring quality and preventing chaos.

Instead of randomly applying AI, a better approach is to journey map the internal process of how product, design, and development teams collaborate. This analysis reveals the biggest bottlenecks and points of friction, which then become the most valuable and targeted places to apply AI for genuine process improvement.

Successful AI strategy development begins by asking executives about their primary business challenges, such as R&D costs or time-to-market. Only after identifying these core problems should AI solutions be mapped to them. This ensures AI initiatives are directly tied to tangible value creation.

To avoid the common 95% failure rate of AI pilots, companies should use a focused, incremental approach. Instead of a broad rollout, map a single workflow, identify its main bottleneck, and run a short, measured experiment with AI on that step only before expanding.

Without a strong foundation in customer problem definition, AI tools simply accelerate bad practices. Teams that habitually jump to solutions without a clear "why" will find themselves building rudderless products at an even faster pace. AI makes foundational product discipline more critical, not less.

Personio adapted the 'Jobs to be Done' framework, typically used for product development, to analyze their internal go-to-market roles. By shadowing employees like account managers, they identified significant time sinks—such as switching between eight systems—and prioritized AI projects with the highest impact.

Don't just assume a new AI workflow is better. Treat internal process changes with the same rigor as product features. Apply a hypothesis-driven framework to how your team operates, experimenting with new AI tools and methods, and validating whether they actually improve outcomes before committing to them.