The vendor pitch
The demo is flawless. The accuracy slide says 98%. Nobody in the room knows what question to ask next.
Physical AI for Leaders · 2.5 weeks · Live and AI-guided · Online
Get ahead with Physical AI & Robotics
AI is entering the physical world. Most leaders don't yet have a plan for it. Build yours in 2.5 weeks, with a Certificate of Completion from Harvard Data Science Initiative to show for it.
Led by
Prof. Natesh PillaiHarvard University
Megan SmithRobotics engineer & technical founder
Prof. Natesh PillaiHarvard University
Megan SmithRobotics engineer & technical founder
78+
NPS, senior leaders
100%
Refundable before start
Past participants across Harvard Data Science Review intensives come from:
NVIDIA
NASA
Amazon
Toyota
Ford
Caterpillar
Sound familiar
You have already been in this meeting.
The board question
Someone senior asks what the robotics plan is. The answer is a glance toward engineering, and a promise to come back next quarter.
The competitor headline
A rival announces an automated facility. Your team calls it hype. Your CFO asks if it is.
Three weeks from now
A vendor quotes 98% accuracy. You ask about the denominator: how many trials, under what conditions, with what intervention rate. The room notices. That is what this program changes.
Why this matters now
Everyone has seen the robot demos. Almost no one can make the call.
The demos are everywhere. Humanoids on keynote stages, robot arms on your feed, autonomous forklifts in vendor decks. What is scarce is the leader who can stand in front of a proposal and separate what works in a video from what survives a production floor, a hospital corridor, a farm, or a portfolio company. A 95% accurate chatbot is useful. A 95% accurate forklift is a liability.
60% of CEOs have deliberately slowed AI deployment over reliability concerns, and they are not wrong to hesitate. They are wrong to hesitate without a framework. 58% of companies already use physical AI in some form, 80% expect to within two years, and the organizations that made the call well are already reporting order-of-magnitude outcomes: the warehouse automation case taught in this program runs at 9x throughput with 99.99% accuracy and 50% lower cost per case.
When we ran our live Physical AI working session, a participant asked the question this entire program exists to answer: what separates the workflows that deliver enterprise-scale value from the ones that make interesting demos but never change outcomes?
One trap
The hype repeater
Extrapolates from the demo, takes the vendor's accuracy number at face value, and signs off on a pilot that never leaves the pilot.
The other trap
The wait-and-see
Defers the question to engineering, or to next year, while the operational data and simulation advantages compound elsewhere.
This program trains
The leader who can evaluate
Fluent enough to question the claims. Equipped to decide what to deploy, what to skip, and how to govern what ships. Knows the best answer is usually the simplest reliable tool that solves the workflow.
What you leave with
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Proof, not notes.
Every learning unit adds a section to working documents built on an operation from your own organization, designed to survive scrutiny in whichever room you answer to.
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Your Physical AI strategy document
Your organization's context, sector maturity, highest-value use cases, and a prioritized opportunity map.
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Your Physical AI framework
A workflow-level implementation blueprint across five phases: deployment readiness, an honest demo-to-production audit, safety architecture, human-robot collaboration design, and an ROI model.
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Physical intuition
A live robot demonstration where you watch a real system sense, plan, and act, and question it as it works.
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The certificate
A Certificate of Completion from Harvard Data Science Initiative, plus a resource for turning your strategy into a presentation-ready deck.
Who's in the room
Decide alongside the people who run, fund, and advise the physical world.
Cohorts across Harvard Data Science Review intensives mix senior operators, executives, investors, and founders across manufacturing, logistics, healthcare, agriculture, energy, and beyond. No engineering background required, and technical participants report it sharpens how engineering realities translate into strategy.
Chief operating officers
Heads of manufacturing & supply chain
Healthcare & pharma operations leaders
VPs of engineering & automation
Strategy & transformation leads
Investors & venture partners
Robotics & autonomy founders
Consultants & non-technical senior leaders
What senior leaders say
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From past Harvard Data Science Review cohorts.
As CIO it is my responsibility to craft our AI strategy and budget for 2026 and beyond. This course was in the right place at the right time. It gave me a solid foundation for building and scaling our strategy in a very comprehensive manner.
David FriedmanChief Information Officer · Agentic AI Intensive
The deeper learning is possible because of the live interaction and questions from both professors and students. There is always a wow factor or moment of eyebrow raising reflection in the HDSR courses.
Carl NunnChief of Police-EMC · Agentic AI Intensive
It teaches you where AI can create genuine strategic advantage versus expensive automation. If you're building anything that needs to work in production with real consequences, HDSI gives you the frameworks to do it right.
Kyle Klain Principal Consultant & Founder · Agentic AI Intensive
Fluency first. Then a strategy you can defend. In 2.5 weeks.
View the course outline
How the 2.5 weeks work
Map it. Engineer it. Defend it.
Two live faculty sessions per week with Harvard Data Science Review board members and authors, anchored around the AGENT framework. Between sessions, Paski, your AI tutor, personalizes every tutorial and reading to your sector and assembles your documents as you go.
Week 1 · Map
Locate the real opportunity
- What is genuinely new versus prior automation waves
- The sector landscape: manufacturing, logistics, healthcare, agriculture, defense, autonomy
- The ROBOT framework and opportunity mapping
- Audit a workflow from your own operation
Live faculty sessions December 1 + December 3
Week 2 · Engineer
Get close to the machines
- The tech stack: perception, world models, Vision-Language-Action models
- The reliability gap and how to interrogate vendor claims
- Live robot demonstration: watch a system sense, plan, and act
- Redesign your workflow for robot-first execution
Live December 8 + December 10 · Robot demo December 9
Week 3 · Defend
Make it defensible
- Governance, safety, and ethics: OSHA, ISO, emerging AI guidance
- Change management and the future of work
- ROI and success metrics beyond payback
- Executive summary and student showcase
Live December 15 + December 17 · Showcase December 17
Week 2 · The moment it gets real
You stop watching videos.
In a live online demonstration with robotics engineer Megan Smith, you watch a real robot system sense, plan, and act, streamed to your screen. You ask questions as it works. You see what happens when the world surprises it, and how it recovers. No travel and no lab visit: this is where Physical AI stops being abstract, and where your judgment about it starts being earned.
Built for a full calendar. Live faculty sessions run Tuesdays and Thursdays at 12pm ET for 60 minutes (the opening session runs 90), all recorded. Expect roughly 4 to 6 hours per week, with a 1-month extension period to finish your deliverables at your own pace.
Program leadership
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Led by the people who define the field.
Lead faculty · Harvard University
Prof. Natesh Pillai
Professor of Statistics, Harvard University · Distinguished Engineer, LinkedIn · Amazon Scholar
A statistician and AI leader whose work spans academic research and large-scale industry application, known for his expertise in GenAI, responsible AI, and statistical reasoning. Leads the sessions on the Physical AI technology stack, applied probability, and the reliability gap.
Lead faculty · Working robotics engineer
Megan Smith
Robotics Engineer, Technical Founder & Advisor
A robotics and systems engineer whose career spans aerospace, autonomous systems, and advanced manufacturing, known for moving technology from prototype to production and translating complex, interdisciplinary systems into deployable, scalable platforms. Leads the ROBOT framework session and hosts the live robot demonstration.
Stephanie Dick
Assistant Professor, Simon Fraser University · Historian of computing & AI
A historian of mathematics, computing, and artificial intelligence, with a PhD in History of Science from Harvard University, who co-edits the Mining the Past column at Harvard Data Science Review. Co-leads the opening session on the history of AI and robotics.
Miguel Paredes
AI & Data Science Executive, Educator & Advisor
Formerly VP of AI & Data Science at Albertsons, where he co-led its Generative AI Center of Excellence, with a PhD in applied AI and master's degrees from MIT. Leads the closing unit on governance, ethics, ROI, and leading Physical AI change.
Dirk Hofmann and the DAIN team
Co-founder, DAIN Studios · Harvard Data Science Review author
Nearly 20 years leading data and AI strategy, transformation, and product initiatives at companies including Siemens, Nokia, and Deutsche Telekom, and co-architect of the AGENT framework published in Harvard Data Science Review. Teaches real-world Physical AI use cases across sectors.
Why now
Three clocks are running.
Budget season
2027 planning starts in weeks
This cohort delivers your strategy in time to shape next year's capital allocation. A strategy built after budgets lock waits a year to matter.
The data clock
Waiting has a compounding cost
Physical AI runs on operational data. The sensor data, movement data, and simulation models being captured now cannot be bought back later. Every quarter of wait-and-see widens the gap.
Enrollment
2 seats left at $1,495
The cohort starts December 1.
Physical AI for Leaders
December cohort · Starts December 1
$1,995
$1,495
2 seats left
Fully refundable until December 1, 2026. Includes six months of community and platform access.
Full refund available until December 1, 2026. Ready now? Apply directly, or book a call with a program advisor.
Questions, answered
Before you decide.
Is the program fully online?
Yes. Everything happens online, including the live faculty sessions and the robot demonstration, which is streamed live so you can ask questions as the system works. Sessions run Tuesdays and Thursdays at 12pm ET and are recorded. There is no travel and nothing to install.
Do I need a technical background?
No. The program is built for decision-makers, and an AI tutor personalizes every tutorial, reading, and briefing to your sector and role. Participants with technical backgrounds report it deepens how engineering realities translate into business strategy.
How much time does it take each week?
Two live faculty sessions of 60 minutes each (the opening session runs 90), plus roughly 6 to 8 hours of self-paced work. Most participants spend 9 to 12 hours per week. A 1-month extension period follows the live weeks, so a busy stretch never costs you the deliverables.
What if I miss a live faculty session?
Every live session is recorded and available in your course platform, so a missed session never puts you behind.
My sector feels early for Physical AI. Is this still relevant?
Sector maturity mapping is part of the first learning unit, and every exercise runs on your industry, not a generic case. Knowing when not to deploy, and what simpler alternatives beat a robot, is treated as a first-class outcome.
What exactly do I build?
Two working documents on an operation you choose: a Physical AI strategy document (context, sector landscape, prioritized opportunity map) and a Physical AI framework (a workflow-level blueprint covering deployment readiness, safety architecture, human-robot collaboration, governance, and ROI).
Is it refundable?
Yes. A full refund is available until December 1, 2026, when the cohort starts.
Can I enroll with colleagues?
Yes you can. For group enrollments, book a call with a program advisor.
