Just 6% of Oliver Wight advisors say their clients are extremely satisfied with the AI planning tools they've adopted. Not because the technology is failing — because it's aimed at the wrong problem. For most planning teams, the real bottleneck isn't the decision itself — it's everything that happens before the meeting: chasing down data across systems, reconciling variances, and building the narrative that explains what changed and why.   That's exactly where artificial intelligence is creating real value in Integrated Business Planning today — and where most teams haven't looked yet.   

In this session, JT Taylor will introduce a simple but often-missed distinction — Number Crunching Intelligence, the math that solvers and optimization engines already do well, versus Contextual Intelligence, the language, communication, and judgment work where AI is actually creating value today — and show why most AI adoption stalls when it targets the wrong one.   

Using a practical, nine-step framework and real examples from demand and supply planning, including AI-generated scenarios that surface capacity risks before a Supply Review even begins, JT will show what's possible today, what's still emerging, and how to avoid the governance pitfalls that derail AI adoption.  

Takeaways

1. How to map your IBP process onto a 9-step decision flow to find your biggest AI opportunity  

2. The difference between Number Crunching Intelligence and Contextual Intelligence — and why most of today's AI value comes from the latter, not advanced math  

3. Why explainability — not just accuracy — determines whether an AI actually gets used  

4. What AI should never be allowed to do in an IBP process, and how to build the right guardrails  

5. Why ""wait and see"" is no longer a safe strategy, and one practical step you can take starting Monday to begin closing the gap