[08:36] Why AI Adoption Needs Process Discipline — Jonathan explains that IBM has mapped processes across his organization to understand where AI can help automate, simplify or eliminate unnecessary work. But the bigger point is that AI adoption is not just about adding tools. Teams need training, time and a clear understanding of how workflows change when different people use different tools. Without that discipline, organizations risk creating thousands of agents that do not talk to each other, silo data and create runaway token costs.
[10:07] Client Zero and the AI Proof Point — IBM calls its internal AI implementation “client zero.” The work started with processes, efficiencies and better outcomes, and has helped drive $4.5 billion in savings over three years, with another billion committed this year. But Jonathan is careful to make the sharper point: this is not only about cost takeout. The real value is becoming more effective, more efficient and more growth-focused. IBM is using its own operations as the case study.
[10:28] Half the Events, More Leads — One of the clearest examples of IBM’s AI impact is event strategy. Jonathan says IBM now does half the number of events it did two years ago, but gets 10 to 20% more leads and interest from those events. The reason is better AI-enabled targeting, personalization and relevance before and after the event. It is a useful correction to the “do more” instinct in marketing. Better targeting can beat more activity.
[11:15] The Jobs Conversation Cannot Be Avoided — Jenny raises the reality that some people lose jobs when organizations automate work. Jonathan does not dodge it. He acknowledges that some roles will change, while new opportunities will also emerge. His point is that the average life of a technology skill is now under three years, so companies have to keep hiring, reframing jobs and helping people adapt. AI leadership requires operational change, but also honesty about what changes for people.
