Putting AI to Work

The program begins with a primer on developing a Phase 1 AI Strategy. Phase 1 is simple. Each direct report to the CEO develops a plan to harness the fewest number of AI solutions required to achieve a 20% productivity lift within their respective business unit or functional area.

This can manifest either as a 20% increase in the quantity or quality of output without raising costs, or as a 20% reduction in costs while maintaining the current level of output. The exercise begins by specifying a well-defined performance measure as the target for each unit’s efforts.

Each plan is designed to achieve productivity gains as swiftly as possible. We focus on plans that have an expected payback period of no more than two years.

Upon receiving plans from each direct report, the CEO ranks the plans in the order in which they will be put into action. This is the Phase 1 AI strategy.

The longer-term Phase 2 AI strategy is not as simple. We introduce CEOs and their senior leadership teams to a framework for doing this in the latter half of the program. The framework is based on the best-selling book: Power & Prediction: The Disruptive Economics of Artificial Intelligence, authored by some of the co-creators of Putting AI to Work.

Implementation is focused on putting the strategy into action.

The process begins by setting up the infrastructure needed to support AI-based solutions, followed by a structured rollout. Peers, experts, and vendors provide input and advice through the critical steps of integrating AI tools with existing systems, training staff on new processes, and monitoring progress against the specified performance measures.

To facilitate a successful implementation, the program provides introductions to experts for hands-on support, including regular check-ins and troubleshooting sessions. This ensures that any challenges encountered during the rollout are promptly addressed, minimizing disruption and maximizing the impact on productivity.

By the end of this phase, participants will have not only implemented their AI solutions but also started to see tangible improvements in efficiency and cost-effectiveness within their operations.

Putting AI to Work is structured in cohorts to maximize the speed of learning and implementation. A cornerstone of the program is the emphasis on peer sharing and collaboration. As companies progress at different rates in their AI journey, those who lead in the implementation of specific applications (e.g., demand forecasting AI systems to enhance supply chain management ) will share their insights, experiences, best practices, and lessons learned. In turn, these companies will benefit from learning from other companies who are ahead in a different application domain (e.g., customer support AI systems).

Companies implementing different AI applications still face some common challenges: collecting and preparing data for training and inference, managing prediction errors, integration with enterprise systems, change management, regulatory compliance, etc. Peer sharing is especially helpful in these areas.