This Tom Brady clip perfectly captures the essence of Phase 0 readiness and agent-based modeling in action—not on a football field, but as a metaphor for enterprise transformation.
Brady describes his “superpower”: not physical speed, but the ability to diagnose the opposing defense’s intentions faster and more accurately than they can execute them. He achieves this through exhaustive preparation—weeks of film study mapping body movements, tendencies, alignments, and reactions—until he can predict plays before the snap.
“I knew Kansas City’s defense better than they knew themselves. I knew their body movements, the way their linebackers move, the way their safeties move. I knew everything they were doing. It wasn’t how fast I could run. It was how fast I could diagnose what they were doing. I could figure out what they were doing before they did it—because that’s how I learned to play the game.”
Translate that to procurement and enterprise AI:
The “defense” = the complex ecosystem of human and non-human agents (buyers, suppliers, systems, stakeholders) with their own incentives, constraints, and “tells.”
The “film study” = Phase 0—the deliberate, diagnostic mapping of current-state alignment, decision rights, exception patterns, taxonomies, and behavioral signals before any solution is deployed.
The “pre-snap read” = the Hansen Fit Score’s ability to surface misalignment early, so you know where friction will occur before the play (implementation) begins.
Brady didn’t win because he had the strongest arm or fastest legs. He won because he had superior situational awareness built on rigorous pre-game readiness.
Most organizations skip the equivalent of film study. They deploy new platforms—ERP, S2P, GenAI suites—without mapping the “defense” first: unclear ownership, conflicting incentives, shadow processes. Then they wonder why execution breaks down mid-play.
Hackett has the playbook. Their archive tells you what top performers do—cost per transaction, cycle times, process benchmarks. It answers: “What does world-class look like?”
Gartner names the plays. They forecast spending trends, define categories like “Guardian Agents,” and tell the market what’s coming. They answer: “What should we be talking about?”
But neither answers the question Brady spent two weeks in the film room to solve: “What will this specific defense do when I snap the ball?”
Phase 0 is the film room. It’s the diagnostic work that maps how your and other organizations’ agents actually behave—where decision rights are unclear, where incentives conflict, where resistance will emerge, and how suppliers, partners, and systems will respond under pressure. It answers the question the playbook and the play-caller can’t: “Are we ready to execute this, here, now?”
Brady’s magic was agent-based modeling in its purest form: understanding every agent’s likely behavior in context, then orchestrating his response accordingly.
“I knew Kansas City’s defense better than they knew themselves.”
That’s the Phase 0 promise. You understand the organization’s agents—their incentives, their resistance patterns, their decision behaviors—better than they do. Not because you’re smarter. Because you did the diagnostic work they skipped.
Without the film room, you’re stepping to the line blind—hoping speed or talent compensates for lack of diagnosis.
With it, you call the blitz before it happens.
That’s why 80% of initiatives fail (and have failed for the past four decades). When you lead with technology (equation-based), you are skipping the film room.
-30-
PROCUREMENT’S PHASE 0 FILM ROOM
This isn’t theory. Here’s what it looked like in practice—in 1998.
The Department of National Defence had an MRO procurement platform supporting their IT infrastructure. The contract required 90% next-day delivery. They were hitting 51%. The incumbent was about to lose the contract.
They came to me and said: “Automate our system.”
I said: “Hold on. Let me ask a few questions.”
The first question: “What time of day do orders come in?”
They looked at me like I was crazy. But they answered: “Most orders come in at 4:00 PM.”
That was the film room moment.
Mapping the agents:
The service technicians were incentivized to complete as many calls per day as possible. Policy said order parts after each call. But the system was cumbersome, so they “sandbagged”—held all orders until end of day to hit their service call targets first.
The suppliers were mostly small-medium enterprises sourcing from the US. Not sophisticated with customs documentation or courier systems.
The products were dynamic flux commodities—a part that cost $900 at 9:00 AM was $1,000 by 4:00 PM.
The customs process was holding up shipments because forms weren’t completed properly.
None of these agents knew they were connected. But their behaviors were destroying delivery performance.
The pre-snap read:
We didn’t automate the broken system. We mapped the agents:
Built a self-learning algorithm that weighted supplier performance on delivery, quality, price, and geography—and let buyers adjust the weighting based on urgency
Integrated UPS directly into the system so purchase orders auto-generated waybills and dispatched couriers
Pre-formatted customs documentation so parts cleared on a priority basis
Created visibility so a centralized manager could see what each buyer was doing in real time
The service technicians gradually realized: if we order after each call, we get the part on time, and our call-close rates improve.
The outcome:
51% next-day delivery → 97.3% next-day delivery
Within three months.
Over seven years, cost of goods went down 23%—in line with dynamic flux pricing. The collective buying group compressed from 23 companies to 3.
The Government of Canada’s Scientific Research and Experimental Development program funded the underlying research—a theory I called Strand Commonality: seemingly disparate strands of data have attributes that are related and collectively have an impact.
That was 1998. Agent-based modeling. Phase 0 readiness. The film room.
The lesson:
Brady spent two weeks studying Kansas City’s defense. I spent months studying how technicians, buyers, suppliers, couriers, and customs agents actually behaved—before writing a single line of code.
The industry is only now catching up. Everyone’s talking about AI and GenAI. But the structure—agent-based modeling, readiness assessment, mapping the defense before you snap the ball—that’s been proven for 27 years.
“I knew Kansas City’s defense better than they knew themselves.”
Procurement Insights
Tom Brady Speaks To Procurement Professionals: Don’t Skip The Film Room!
Posted on December 26, 2025
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This Tom Brady clip perfectly captures the essence of Phase 0 readiness and agent-based modeling in action—not on a football field, but as a metaphor for enterprise transformation.
Brady describes his “superpower”: not physical speed, but the ability to diagnose the opposing defense’s intentions faster and more accurately than they can execute them. He achieves this through exhaustive preparation—weeks of film study mapping body movements, tendencies, alignments, and reactions—until he can predict plays before the snap.
“I knew Kansas City’s defense better than they knew themselves. I knew their body movements, the way their linebackers move, the way their safeties move. I knew everything they were doing. It wasn’t how fast I could run. It was how fast I could diagnose what they were doing. I could figure out what they were doing before they did it—because that’s how I learned to play the game.”
Translate that to procurement and enterprise AI:
The “defense” = the complex ecosystem of human and non-human agents (buyers, suppliers, systems, stakeholders) with their own incentives, constraints, and “tells.”
The “film study” = Phase 0—the deliberate, diagnostic mapping of current-state alignment, decision rights, exception patterns, taxonomies, and behavioral signals before any solution is deployed.
The “pre-snap read” = the Hansen Fit Score’s ability to surface misalignment early, so you know where friction will occur before the play (implementation) begins.
Brady didn’t win because he had the strongest arm or fastest legs. He won because he had superior situational awareness built on rigorous pre-game readiness.
Most organizations skip the equivalent of film study. They deploy new platforms—ERP, S2P, GenAI suites—without mapping the “defense” first: unclear ownership, conflicting incentives, shadow processes. Then they wonder why execution breaks down mid-play.
Hackett has the playbook. Their archive tells you what top performers do—cost per transaction, cycle times, process benchmarks. It answers: “What does world-class look like?”
Gartner names the plays. They forecast spending trends, define categories like “Guardian Agents,” and tell the market what’s coming. They answer: “What should we be talking about?”
But neither answers the question Brady spent two weeks in the film room to solve: “What will this specific defense do when I snap the ball?”
Phase 0 is the film room. It’s the diagnostic work that maps how your and other organizations’ agents actually behave—where decision rights are unclear, where incentives conflict, where resistance will emerge, and how suppliers, partners, and systems will respond under pressure. It answers the question the playbook and the play-caller can’t: “Are we ready to execute this, here, now?”
Brady’s magic was agent-based modeling in its purest form: understanding every agent’s likely behavior in context, then orchestrating his response accordingly.
“I knew Kansas City’s defense better than they knew themselves.”
That’s the Phase 0 promise. You understand the organization’s agents—their incentives, their resistance patterns, their decision behaviors—better than they do. Not because you’re smarter. Because you did the diagnostic work they skipped.
Without the film room, you’re stepping to the line blind—hoping speed or talent compensates for lack of diagnosis.
With it, you call the blitz before it happens.
That’s why 80% of initiatives fail (and have failed for the past four decades). When you lead with technology (equation-based), you are skipping the film room.
-30-
PROCUREMENT’S PHASE 0 FILM ROOM
This isn’t theory. Here’s what it looked like in practice—in 1998.
The Department of National Defence had an MRO procurement platform supporting their IT infrastructure. The contract required 90% next-day delivery. They were hitting 51%. The incumbent was about to lose the contract.
They came to me and said: “Automate our system.”
I said: “Hold on. Let me ask a few questions.”
The first question: “What time of day do orders come in?”
They looked at me like I was crazy. But they answered: “Most orders come in at 4:00 PM.”
That was the film room moment.
Mapping the agents:
The service technicians were incentivized to complete as many calls per day as possible. Policy said order parts after each call. But the system was cumbersome, so they “sandbagged”—held all orders until end of day to hit their service call targets first.
The suppliers were mostly small-medium enterprises sourcing from the US. Not sophisticated with customs documentation or courier systems.
The products were dynamic flux commodities—a part that cost $900 at 9:00 AM was $1,000 by 4:00 PM.
The customs process was holding up shipments because forms weren’t completed properly.
None of these agents knew they were connected. But their behaviors were destroying delivery performance.
The pre-snap read:
We didn’t automate the broken system. We mapped the agents:
The service technicians gradually realized: if we order after each call, we get the part on time, and our call-close rates improve.
The outcome:
51% next-day delivery → 97.3% next-day delivery
Within three months.
Over seven years, cost of goods went down 23%—in line with dynamic flux pricing. The collective buying group compressed from 23 companies to 3.
The Government of Canada’s Scientific Research and Experimental Development program funded the underlying research—a theory I called Strand Commonality: seemingly disparate strands of data have attributes that are related and collectively have an impact.
That was 1998. Agent-based modeling. Phase 0 readiness. The film room.
The lesson:
Brady spent two weeks studying Kansas City’s defense. I spent months studying how technicians, buyers, suppliers, couriers, and customs agents actually behaved—before writing a single line of code.
The industry is only now catching up. Everyone’s talking about AI and GenAI. But the structure—agent-based modeling, readiness assessment, mapping the defense before you snap the ball—that’s been proven for 27 years.
“I knew Kansas City’s defense better than they knew themselves.”
That’s not football. That’s Phase 0.
Don’t skip the film room.
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