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.

Don't try to optimize your strongest departments with your first AI project. Instead, target 'layup roles'—areas where processes are broken or work isn't getting done. The bar for success is lower, making it easier to get a quick, impactful win.

Leaders feeling pressure to deploy AI should focus it internally first. Using AI to enrich and manage product data catalogs is a low-risk, high-reward application that improves efficiency and builds the necessary foundation for future, more complex customer-facing AI features.

Instead of starting with simple generative AI tasks, Airbnb focused on the most difficult application: resolving urgent customer issues like lockouts. This high-stakes approach allowed them to build a robust agent that can now be applied to less critical, "up-funnel" use cases like travel planning.

Instead of pursuing broad, top-down AI governance, leaders should first target specific business problems where departments intersect and cause delays, such as Sales and Legal on contracts. Use AI as a "thought leader" in a cross-functional team to solve these high-friction issues.

The common "start small" approach creates a sprawl of low-value AI agents without proper governance. Instead, TrueFoundry's Nikunj Bajaj advises focusing on five critical, high-impact workflows. This justifies building a robust, scalable infrastructure from the outset, which can later support smaller initiatives and ensure success.

With AI accelerating development, the key challenge is no longer building faster; it's getting completed features through legal, marketing, and other operational hurdles. Organizations must now re-engineer these internal processes to match the new pace of 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.

The path to enterprise AI adoption follows a typical change curve. To bypass initial fear and rejection, organizations should first apply AI to transform familiar, high-friction workflows. This strategy builds momentum and demonstrates value before tackling entirely new, innovative business models.