Adjacent Expertise Innovation Model
Combine deep expertise from neighboring fields to produce novel solutions
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 94%
The Adjacent Expertise Innovation Model treats innovation as a productive collision between deep knowledge domains. Begin with a specific unsolved problem and identify its underlying mechanism, such as noise, friction, trust, or information overload. Then search for a neighboring field that has already addressed an analogous mechanism under different conditions. Bring together specialists who can explain both domains deeply enough to distinguish transferable principles from superficial resemblance. Translate the borrowed mechanism, build a prototype, and test it in the original context. Phil uses the bullet train as an example: engineering expertise combined with observations from bird flight to address aerodynamic noise. AI can make this model more accessible by handling supporting tasks while people retain deep specialization.
Origin
On Marketing Against The Grain, Phil Agnew cites Sam Tatam's book Evolutionary Ideas and the collaboration behind Japan's bullet train.
Core principles
- 01Breakthrough ideas often emerge at the intersection of established fields.
- 02Deep specialists contribute stronger ingredients than shallow generalists.
- 03A useful analogy transfers a mechanism, not merely an aesthetic.
- 04AI can support peripheral work while people deepen their core expertise.
How to run it
- 1
Frame the blocked outcome
State the result you need, the current constraint, and why familiar approaches have failed.
Pro tip Describe the problem in causal terms rather than naming a preferred solution.
Watch out A vague innovation brief produces vague analogies.
- 2
Isolate the mechanism
Identify the physical, behavioral, informational, or economic mechanism creating the constraint.
Pro tip Ask what must become easier, quieter, faster, safer, or more trusted.
Watch out Do not confuse a visible symptom with its underlying cause.
- 3
Search adjacent domains
Look for fields that encounter the same mechanism in a different environment or at a different scale.
Pro tip Search nature, engineering, entertainment, and other industries rather than direct competitors alone.
Watch out Surface-level similarities rarely produce useful transfers.
- 4
Pair deep specialists
Bring together experts who understand the source domain and the target problem well enough to interrogate each other's assumptions.
Pro tip Give each specialist time to teach the causal logic of their field.
Watch out A room of broad generalists may lack the depth needed to recognize the key mechanism.
- 5
Translate rather than copy
Convert the source-domain principle into a form compatible with the target context, constraints, and users.
Pro tip Write down which parts of the analogy transfer and which do not.
Watch out Literal imitation can import irrelevant features and new failure modes.
- 6
Prototype the intersection
Build the smallest test that can determine whether the transferred mechanism improves the blocked outcome.
Pro tip Measure the original constraint directly.
Watch out Do not mistake an interesting story for validated innovation.
In the wild
A high-speed train created damaging noise as it moved through the environment. The engineer discussed the problem with a bird watcher and drew on the way birds reduce sound and manage aerodynamics. Those biological insights informed changes to the train's design.
→ The combined expertise helped the train travel at high speed without producing the same window-breaking noise.
Common mistakes
Borrowing only the appearance
Copying how something looks without understanding why it works transfers style rather than mechanism.
Replacing expertise with breadth
Making everyone a shallow generalist removes the depth required to identify valuable cross-domain connections.
Stopping at the analogy
An analogy remains speculation until the transferred mechanism is prototyped and measured in its new context.
Is it for you?
Best for
It is best for specialist teams facing problems that standard category knowledge has failed to solve.
Not ideal for
It is not ideal for routine execution where an established practice already produces an adequate result.
From the transcript
“the best ideas tend to come when there is two experts in two adjacent fields that end up collaborating.”
“That application, that cross-section of people who are very, very skilled in specific areas is where great ideas come from.”
From the episode
The Future Of Marketing In An A.I. World w/ Phil Agnew (#113)
Phil Agnew