Scott Jennings of Anaplan talks about operationalizing AI in planning, eliminating latency tax, the shift to autonomous multi-agent systems, & your next steps.

IN THIS EPISODE WE DISCUSS:

[02.52] Scott's role at Anaplan, what they do, and their ideal customer profile.

"If you're not collaborating, you're going to get stuck in siloes - and that's what we hear from companies time and time again. It's that ability to collaborate with your peers, and with all the different technology that's available, that determines success or failure."

[04.38] From use case selection and success criteria to cost, why – despite huge investment in AI experimentation – many organizations are still struggling to demonstrate tangible business value, and why 2026 is the year of change when it comes to businesses moving from AI experimentation to proving ROI.

"Companies are beginning to ask questions, about trade offs – did they pick the right use cases to experiment with AI? They're not sure. Did the experiment work, and can it be scaled? They're not sure. And then the bill comes, and it turns out that tokens are expensive."

[08.10] What 'AI ROI' actually looks like in planning, and the metrics executives should be looking at to know whether an AI investment is genuinely paying off.

"Finance talks a lot about working capital, but the moment you move to supply chain you're talking units. Bringing those two concepts together, so you can understand the financial impacts across the board instantly, is what's been driving most of the AI projects I've seen."

[12.18] Why planners are often reluctant to rely on AI recommendations, and what organizations need to do to move from a 'black box' to a more explainable, transparent approach.

[15.56] Why 'AI for AI's sake' can actually create more problems, and why it's critical to ensure your data is ready for AI use.

"When you think about AI, you want to think about what's going to drive the big metrics that are important to your organization, and then work backwards. What you don't want to do is just start running AI projects… sometimes the cost is higher than running the exact same project with humans!"

[20.17] The importance of real-time planning, the cost of the 'latency tax' when decisions are delayed because data, teams and systems aren't connected, and how significant that impact can be for large organizations.

"You can lose 5 cents on every dollar due to decision delay. And that's millions of dollars for a large company."

[23.43] Why a common business ontology and semantic layer are critical in helping AI understand how demand, supply, inventory, capacity and financial outcomes are connected to drive more effective results.

"Ontology is a map of your business that AI can use to better understand how it operates, because AI needs to be told a lot about your business to be effective."

[26.40] How an AI system can move from identifying a change in demand to understanding its implications for things like bills of material, plant capacity, lead times, inventory buffers and ultimately the P&L.

[30.09] How organizations should think about combining probabilistic AI – for things like demand sensing and pattern recognition – with deterministic calculation engines for financial math, rules and governance.

[34.48] How the role of the planner shifts when AI stops simply answering questions and begins continuously monitoring the business, generating scenarios and routing exceptions for people to act on.

[37.23] Multi-agent AI systems – what a world of specialized AI agents look like in a planning environment, and where humans should remain in control.

[41.11] What organizations should be looking for in a planning platform, and the importance of capabilities like an embedded calculation engine, business-owned models, connected data and native AI.

"From an agentic AI perspective, you have to have a deterministic and scalable calculation engine with purpose built supply chain planning models that are connected to all relevant internal and external sources."

[44.05] How organizations can identify a high-value planning use case, prove ROI quickly, build trust and scale AI across the enterprise.

RESOURCES AND LINKS MENTIONED:

Head over to Anaplan's website now to find out more and discover how they could help you too. You can also connect with Anaplan and keep up to date with the latest over on LinkedIn or YouTube, or you can connect with Scott on LinkedIn.

If you enjoyed this episode and want to hear more from Anaplan, listen to 514: Turn Strategic Planning Into Retail Profitability, with Anaplan or 499: Navigating the EV Revolution, with Anaplan.

Check out our other podcasts HERE.

568: Operationalizing AI in Planning: Go From Experimentation to ROI, with Anaplan

Let's Talk Supply Chain

568: Operationalizing AI in Planning: Go From Experimentation to ROI, with Anaplan