The successful approach to AI isn't applying the technology broadly and searching for value. Instead, leaders must first define a specific business outcome, such as improving pipeline conversion. From there, they can work backward to identify and procure the exact data needed to enable AI to solve that targeted problem.

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.

The biggest barrier to getting value from AI isn't the technology itself, but a lack of internal clarity. Teams that haven't defined their goals, customers, and core work processes will get poor AI outcomes, as the technology exposes pre-existing strategic weaknesses.

Organizations that default to treating AI as an IT-led initiative risk failure. IT's focus is typically on security and risk mitigation, not growth and innovation. AI strategy must be owned by business leaders who can align its potential with customer needs, talent decisions, and overall company growth.

AI isn't a technology to be applied to existing processes. It's a foundational layer, like an operating system, that fundamentally reshapes how businesses create value, make decisions, and operate. This perspective forces a complete rethink of strategy, not just an upgrade.

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.

In AI's nascent stage, leaders shouldn't aim for a perfect multi-year strategy, as this indicates a misunderstanding of the evolving landscape. Instead, they should identify one or two key business challenges and pilot AI solutions for those specific use cases, learning and adapting along the way.

Housing AI strategy within IT is a critical error. The most valuable applications of AI are not technological but rather business innovations. The conversation must be led by business leaders asking what is now possible for customers and partners, with IT acting as an enabler, not the primary owner.

Framing AI adoption as an IT initiative is a critical mistake. IT's role is to ensure security and responsible use, but business leaders must own the transformation. This includes driving strategy, identifying use cases, reskilling talent, and managing the cultural shift.