From the real world to digital automation: Presentation to Toyota
Warren B. Powell
Professor Emeritus, Princeton University
Chief Innovation Officer, Optimal Dynamics
On July 10, 2025, I gave a presentation at Toyota’s North American Headquarters titled The Road to Digital Automation at Toyota: Learning How to Be an Informed Consumer. The talk was designed for a general business audience and was attended by over 300 people who were enthusiastic and receptive to the message — it applies to every person who is “making decisions.” This page contains the video recording (in five parts), along with a short summary of the core messages.
Jump to a section
- Core messages
- Part 1 — Introduction and the 7 levels of AI
- Part 2 — Framing the problem
- Part 3 — The universal modeling framework
- Part 4 — Making decisions
- Part 5 — The path to digital automation
Core messages
- There are 7 levels of AI, where LLMs such as ChatGPT are level 4. Level 5 is traditional deterministic optimization, and level 6 covers sequential decisions — the central topic of the talk. The concepts are illustrated with business-friendly images.
- The central theme is a new step in the modeling process — “Framing the Problem” — which involves describing real-world problems (any real-world problem) in English (no math) that provides a bridge from the original problem to my universal modeling framework, which can be used to model any sequential decision problem. Framing the problem starts with identifying metrics, decisions, and uncertainties.
- The universal modeling framework fits entirely on one PowerPoint slide. I present it only to make the case that metrics, decisions, and uncertainties form the basis of a sequential decision problem.
- The four classes of policies for making decisions get a brief introduction. Most important from a business perspective: policies help identify what information is needed.
- LLMs (level 4 AI) vs. sequential decisions (level 6 AI). LLMs are useful, but primarily for administrative support. If you want to improve physical systems, you need to make better decisions — which means level 6 AI. In automotive terms, LLMs are like windshield washers, while sequential decision problems are like engines. If you want to have an impact, you have to make better decisions.
- A five-step process for designing and implementing methods for making decisions.
Video segments
The 90-minute presentation is split into five segments. You don’t need to watch them in order — feel free to pick the topics that are most interesting to you.
Part 1 — Introduction and the 7 levels of AI
The presentation makes the distinction between machine learning (levels 2, 3, and 4), where the goal is to match a training dataset, versus levels 5 and 6 for making decisions — which require an explicit model of the underlying problem, including user-specified metrics such as cost minimization.
Part 2 — Framing the problem
This is the heart of the talk. I introduce the need for people who can translate real applications (such as business problems) into language that captures the information needed by a modeler.
Part 3 — The universal modeling framework
I introduce the notation for the universal modeling framework on a single PowerPoint slide. Although it is quite simple, it is not necessary to know the framework. But this framework is guiding the questions that need to be answered if a decision problem is going to be solved on a computer.
Part 4 — Making decisions
I give a very brief overview of the four classes of policies (with no math!). This material is being taught by only a few universities, but it is critical for solving the complex problems that arise in problem settings such as supply chain management (among others). This part also covers the importance of including uncertainty in forecasts, along with the need to model the ability to make decisions in an uncertain future.
Part 5 — The path to digital automation
I describe five steps in the path to implementing the process of making decisions. I recommend watching Part 2 before Part 5, but it is not essential.