Orchestration Is the System

A human body is built by an estimated thirty-seven trillion cells, and not one of them knows what a hand is.

No cell holds the blueprint. No cell is in charge. Yet the collective reliably builds five fingers, stops at five, and, if you injure it, rebuilds toward the same target and stops again. Cut a flatworm in half and each half knows which end was missing. The biologist Michael Levin (no relation, though I’ve borrowed his thinking) puts it plainly in a 2023 paper: “no individual cell knows what a finger is … and yet … the collective will actively build until precisely the right number” is restored. The intelligence isn’t in the parts. It’s in the architecture that aligns the parts toward a goal none of them can see.

I’ve spent twelve years building enterprise AI (AML systems inside banks, automation for a smart city, AI for a pharmacy chain and a mining operation), and I’ve come to believe that this is the single most important thing anyone can understand about making AI work in a real company.

The model is one cell. The system is the body.

Almost every failed enterprise AI program I’ve seen failed because someone confused the two.

The short version:

• The model is the easy 10%. The system around it, who decides, where humans step in, when to distrust the output, is the other 90%.
• Capability without that architecture is not leverage. It is an accelerant for burning money.
• The hire you actually need is not a Chief AI Officer. It is a Chief Process Officer who speaks AI.

The expensive misunderstanding

A company I’m aware of ran up a nine-figure annual bill on AI and got very little back. The instinct in the room was that this was a pricing problem: negotiate the contract, switch the vendor, tune the model.

It wasn’t a pricing problem. It was an unmapped process with a corporate credit card attached.

The AI was doing exactly what it was told, thousands of times a second, inside a workflow nobody had ever drawn. There was no map of who decides what, where a human should intervene, what “done” looks like, or when the model’s answer should be distrusted. So the model optimized locally: fast, confident, and pointed at the wrong target. And the meter ran.

Here’s the part that should worry every executive buying AI right now: the bigger and cheaper the model gets, the faster an unmapped process burns money. Capability without architecture isn’t leverage. It’s an accelerant.

Why the model was never the hard part

When companies say “we need an AI strategy,” they almost always mean “we need to choose a model.” They run a bake-off, pick a vendor, stand up a pilot, and wait for transformation.

It doesn’t come, because the model was the easy ten percent, and I mean that almost literally. BCG puts successful AI at 10% algorithms, 20% data and technology, and 70% people and process. The failure data fits the ratio: a 2025 MIT study found 95% of enterprise generative-AI pilots produced no measurable return, and RAND found more than 80% of AI projects fail (roughly twice the rate of IT projects without AI), with root causes that are organizational, not algorithmic.

In a regulated bank, the model that flags a suspicious transaction is the trivial part. The system is everything around it: which alerts a human must review, how a decision survives an audit, where the process hands off between the data-science team and the risk committee, what happens when the model is wrong, and it is, regularly, wrong. AI ships in weeks. The process around it takes months, because the process is the strategy. Mapping it is the strategy. The data agrees: McKinsey’s 2025 survey found redesigning workflows to be the practice most associated with real bottom-line impact from AI, and that only about a fifth of companies had actually done it.

This inverts the usual order of operations, and the inversion is the whole point. Most teams start with the model and try to bolt a process on afterward. The teams that succeed start with the process (the actual sequence of decisions, handoffs, and exceptions) and then ask where a model earns its place. Michael Levin makes the same move in biology: he argues the science has it backwards, that you can’t understand the parts until you understand the goal-directed system they serve. Reach for the model first and you’re studying a single cell while ignoring the body.

The orchestration layer

So if the model is one step, what’s the system?

It’s the orchestration layer: the part that routes work between models and humans, holds the goal across many steps, and, most importantly, decides when the model is wrong.

Take AI in M&A, which is what I build at GetDeal.AI. A good model can read a data room and surface in thirty minutes what used to take an analyst three weeks. Genuinely useful. But due diligence isn’t one judgment; it’s a fan-out across financials, commercial, tech and IP, team, and legal, each a different kind of question, followed by a synthesis. Valuation isn’t one of those lanes; it’s what you compute after the lanes resolve. And somewhere in there a human has to decide when to trust the machine, because the first time we let an early system run unsupervised, it didn’t fail by being slow. It failed by being confidently over-escalated: flagging risk that wasn’t there, which in a deal is its own kind of expensive.

The orchestration is the product. The model is a component inside it.

This isn’t only my experience talking. It’s a property of multi-scale systems. The control theorist John Doyle has spent a career showing that robust complex systems, from cells to the Internet, run on layered architecture rather than emergence: shared constraints that paradoxically “deconstrain” the system, letting limited, imprecise parts combine into capabilities none of them has alone, the same modular, layered design biology and advanced engineering both converge on. You don’t get a working system by making one component bigger and hoping capability emerges. A bigger model doesn’t emerge into a solution any more than a bigger cell emerges into a hand. The hand is the architecture.

The hire that actually matters

Which brings me to the title most companies are hiring for, and the one they actually need.

Everyone is racing to appoint a Chief AI Officer: appointments jumped about 70% year over year in 2024. I am one, so I can say this without it being an insult.

Most companies don’t need a Chief AI Officer. They need a Chief Process Officer who speaks AI.

The bottleneck is almost never model access. That’s a commodity now, an API key away. The bottleneck is that nobody owns the map: the end-to-end process, the decision points, the handoffs, the places a human must stay in the loop, the definition of done. That’s not a modeling job. It’s a process-architecture job that happens to require deep fluency in what models can and can’t do. Call it whatever you like, but the function is the orchestration layer of your organization: the thing that aligns a swarm of capable components toward a goal none of them holds on its own.

That is exactly what a body does to thirty-seven trillion cells. It’s what a good operating company does to its people and software. And it’s what the orchestration layer does to your models.

What this means if you’re buying AI

If you take one thing from this: stop shopping for the smartest model and start drawing the process it’s going to live in.

Concretely, before you sign the next AI contract:

• Map the process first. The real sequence of decisions and handoffs, not the slide version. If you can’t draw it, AI won’t fix it; it’ll just run it faster and more expensively.
• Find the “when is the model wrong” points. Every workflow has them. Those are where your humans and your guardrails live. A system without them is the nine-figure bill waiting to happen.
• Hire for the map, not the model. The scarce, valuable person is the one who owns the orchestration: process-first, AI-fluent, not another model evaluator.
• Treat the model as a component, not a strategy. It’s the easy ten percent. Spend your strategy budget on the other ninety.

The companies that win the next few years won’t be the ones with access to the best model. Everyone will have that. They’ll be the ones who understood, early, that intelligence was never in the parts. It was in the architecture that aligned them.

No cell knows what a finger is. The collective does. Build the collective.

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Related reading: A Word Is Not a Concept goes inside the model: what it actually “knows,” and why meaning is built by analogy, not by a body.

References

1. Bianconi, E., et al. (2013). “An estimation of the number of cells in the human body.” Annals of Human Biology 40(6): 463–471. https://doi.org/10.3109/03014460.2013.807878
2. Pio-Lopez, L., Bischof, J., LaPalme, J. V., & Levin, M. (2023). “The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis.” Interface Focus 13(3): 20220072. https://doi.org/10.1098/rsfs.2022.0072
3. Levin, M. (2022). “Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds.” Frontiers in Systems Neuroscience 16: 768201. https://doi.org/10.3389/fnsys.2022.768201
4. Durant, F., et al. (2017). “Long-Term, Stochastic Editing of Regenerative Anatomy via Targeting Endogenous Bioelectric Gradients.” Biophysical Journal 112(10): 2231–2243. https://doi.org/10.1016/j.bpj.2017.04.011
5. Csete, M. E., & Doyle, J. C. (2002). “Reverse Engineering of Biological Complexity.” Science 295(5560): 1664–1669. https://doi.org/10.1126/science.1069981
6. Doyle, J. C., & Csete, M. (2011). “Architecture, constraints, and behavior.” PNAS 108(Suppl. 3): 15624–15630. https://doi.org/10.1073/pnas.1103557108
7. Boston Consulting Group. “The 10-20-70 rule” (AI at scale: 10% algorithms, 20% data & technology, 70% people & process). https://www.bcg.com/capabilities/artificial-intelligence
8. MIT / Project NANDA (2025). “The GenAI Divide: State of AI in Business 2025.” https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
9. Ryseff, J., De Bruhl, B. F., & Newberry, S. J. (2024). “The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed.” RAND Corporation. https://www.rand.org/pubs/research_reports/RRA2680-1.html
10. McKinsey & Company (2025). “The State of AI: How organizations are rewiring to capture value.” https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
11. Altrata (2024). “Private Sector Drives 63% of Chief AI Officer Appointments.” https://www.prnewswire.com/news-releases/private-sector-drives-63-of-chief-ai-officer-appointments-as-ai-influences-corporate-talent-strategy-302330112.html

Bioelectric pattern memory and the substrate-independence of cognition draw on Levin’s TAME framework [3] and the planaria bioelectric-editing work [4]; the “architecture, not emergence” thesis follows Doyle’s layered-architecture program [5, 6], where “constraints that deconstrain” originates with Kirschner & Gerhart’s work on evolvability.