AI Business Defensibility Audit
Audit your data, distribution, community, and speed before AI erodes your edge.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 5
- Confidence
- 94%
The audit tests whether a business possesses durable advantages as AI makes product features and educational content easier to reproduce. Leaders first examine whether they own unique data or rely on information controlled by others. They then assess distribution, including domain authority, partnerships, virality, activation, and retention, because reach has little value when customers immediately leave. Next, they determine whether a product-linked community supplies current perspectives, opinions, and interactions that generic models cannot easily reproduce. Finally, they evaluate organizational urgency: existing advantages provide only a temporary lead unless the company rapidly incorporates AI, improves its offering, and willingly disrupts its current model. The output is a prioritized set of assets to strengthen and vulnerabilities to address before competitors erase the company’s head start.
Origin
Extracted from Marketing Against the Grain, where Kipp Bodnar and Kieran Flanagan examined how businesses could remain defensible amid the rapid adoption and commoditizing effects of generative AI.
Core principles
- 01Treat AI as both important and urgent.
- 02Own differentiated data rather than depending entirely on external sources.
- 03Use established distribution to capitalize on rapid innovation.
- 04Tie community directly to the product and its use cases.
- 05Move before AI turns differentiated features into table stakes.
How to run it
- 1
Map AI Commoditization Risk
List the product features, services, and educational content that generative AI could reproduce or make cheaper. Mark anything already becoming a standard platform feature.
Pro tip Include threats outside the technology sector because AI can alter workflows in nearly every industry.
Watch out Do not assume current product differentiation will remain scarce.
- 2
Audit Unique Data
Identify proprietary, current, or behavior-based data that improves your product. Distinguish data you own from data supplied by platforms or third parties.
Pro tip Prioritize data that produces outcomes generic language models cannot replicate.
Watch out A thin layer of segmented data on top of a general model may not be sufficiently original or sticky.
- 3
Stress-Test Distribution
Review domain authority, partnerships, virality, sales reach, activation, and retention. Determine whether these channels can quickly carry new AI-enabled capabilities to market.
Pro tip Use an existing distribution lead to test new channels before search and content behavior shifts further.
Watch out Distribution gains disappear when onboarding or retention is weak.
- 4
Evaluate Product-Linked Community
Assess whether the community is integral to how customers use the product and whether it continuously creates valuable perspectives and interactions. Strengthen the feedback loop between community activity and product value.
Pro tip Treat real-time community interaction as both differentiated experience and differentiated data.
Watch out An ancillary community disconnected from the product is less defensible.
- 5
Set an Urgent Adaptation Plan
Choose where to integrate AI, partner with AI providers, or disrupt the existing business model. Assign near-term experiments and measure whether adoption improves customer and operating outcomes.
Pro tip Move while expertise gaps remain small and incumbency still provides room to experiment.
Watch out A current lead will erode if the organization treats AI as important but not urgent.
In the wild
The hosts suggest that Cameo could respond to AI-generated celebrity media by helping artists license their likenesses and using AI to produce approved messages. Instead of defending only the manual recording model, Cameo would combine celebrity relationships and distribution with a new AI-enabled product.
→ The company converts an existential substitution threat into an extension of its existing marketplace advantage.
A collaborative software company connects its AI features to a community where users exchange templates, workflows, opinions, and emerging practices. The product benefits from current human contributions while the community benefits from increasingly capable tools.
→ Features may be copied, but the combined product, data, and community experience remains harder to reproduce.
Common mistakes
Mistaking an AI Wrapper for a Moat
Building a narrow interface around another company’s model creates exposure when large platforms integrate the same capability directly.
Counting Reach Without Retention
Strong distribution does not create lasting value when weak onboarding or product quality sends users to abundant alternatives.
Moving at the Old Innovation Pace
Planning in years while AI capabilities advance in weeks allows competitors to erase existing advantages.
Is it for you?
Best for
It is best for leaders revising product, marketing, and growth strategy as generative AI changes their market.
Not ideal for
It is not ideal for teams seeking a static long-term moat that requires no continued adaptation.
From the transcript
“Do I have unique and special data? Yes or no?”
“The next thing is distribution. Do I have the ability to scale and get my product to market in a really efficient and quick way?”
“I would say that community is gonna be a core part of differentiation.”
From the episode
How To Protect Your Business From GPT-4 in 2023 (#101)