Why ‘AI inbreeding’ is your new big problem


By Terri Davis
DIGITAL JOURNAL
January 19, 2026


Photo by Tim Witzdam on Unsplash

Terri is a thought leader in Digital Journal’s Insight Forum (become a member).


When using generative AI often and broadly, modern organizations tend to assume their strategies are original by default.

In reality it’s the opposite, with ‘AI inbreeding’ happening at every prompt.

Unfortunately, history offers repeated warnings about what happens when systems become overly self-referential.

Over time, that inward focus (think of it as dipping into the same gene pool) creates sameness to a detrimental extent.

By feeding strategies, language, and decisions back through the same AI systems repeatedly, companies risk reinforcing what already exists and what the competition is doing, rather than expanding what is possible.
What’s new is old again

Large language models (LLMs) excel at identifying probability.

They surface what has worked before, what resembles prior success, and what aligns with dominant patterns. Used indiscriminately, which they most often are, these LLMs compress thinking toward the centre.

Strategic deformity takes familiar forms. Products begin to resemble competitors’ offerings. Marketing adopts the same cadence, metaphors, and vocabulary. Strategy decks differ in format but not in substance. Teams rely on identical prompt frameworks and celebrate efficiency, while gradually abandoning the friction that produces originality.

The organization does not fail outright. It first becomes indistinct.

How do you know you could be experiencing algorithmic inbreeding?Your strategic plan resembles your competitors’ more than it used to.
Your brand voice feels technically sound but emotionally flat.
Your most AI-literate hires are also the least surprising thinkers.
Why this is a leadership problem, not a technology one

AI systems are not designed to imagine what does not yet exist.

They are designed to predict what is most likely to work based on historical data. When executives treat AI outputs as finished thinking rather than informed input, leadership begins to defer rather than decide.

Over time, decisions narrow. Instead of asking what could work, teams ask what the model recommends. Strategic ambition is replaced by statistical reassurance, and judgment becomes optional.

This is a failure of governance.
The governance gap no one is talking about

Many organizations believe they have addressed this risk by appointing a Chief AI Officer.

In theory, this makes sense. AI touches strategy, operations, talent, and brand, and someone should own it.

In practice, most CAIO roles are measured on deployment speed, cost efficiency, and adoption. Very few are accountable for protecting strategic differentiation or intellectual diversity.

As a result, AI leadership often becomes an optimization function rather than a strategic one. Models are implemented correctly, workflows improve, and output accelerates. However, no one is explicitly responsible for asking the most important executive question:

What are we giving up by letting the model lead first?

Without that counterweight, AI becomes the default decision-maker by convenience rather than intent.
The three failure patterns of AI

When algorithmic deference takes hold, it tends to surface in three consistent ways.
Strategic Convergence

Organizations pursue similar growth strategies, pricing models, and product roadmaps because AI systems reinforce what already dominates the market.
Linguistic Flattening

As AI increasingly shapes internal and external communications, brand voice loses texture. Language becomes technically sound but emotionally inert. Customers disengage not because messaging is wrong, but because it feels interchangeable.
Talent Homogenization

AI-driven hiring tools optimize for pattern matching. Candidates who do not resemble previous “successful” profiles are filtered out. Over time, organizations select for tool fluency over original thinking.
What CEOs must do differently in 2026

This problem cannot be solved with better prompts or more advanced models. It requires executive intervention.

The CEO’s responsibility is not to align the organization with AI, but to ensure AI does not collapse strategic diversity and human judgment. That means making deliberate choices, including:Preserving space for dissent in planning cycles.
Protecting ideas that test poorly but feel directionally right.
Treating AI outputs as inputs rather than verdicts.
Leadership must reassert judgment as the final authority.

Organizations that avoid AI inbreeding behave differently. They hire for intellectual friction rather than culture fit, elevating leaders with non-linear experience across industries and disciplines. They slow decision finality to allow for exploration before optimization.

Progress does not come from eliminating variance but from protecting it.
Don’t optimize your way to irrelevance

One path leads to a sleek, efficient, and ultimately forgettable organization.

Automated. Polished. Interchangeable.

The other path is slower and more human. It tolerates disagreement, funds uncomfortable ideas, and accepts that originality often looks inefficient before it looks inevitable.

AI can accelerate execution. Only leadership can preserve evolution.

The companies that define the next decade will not be the most optimized. They will be the most adaptive in the truest sense of the word. They will retain room for the equivalent of the duck-billed platypus among their ranks and ideas: rare, unconventional, outside thinking, resistant to easy categorization.

Written ByTerri Davis
Terri is the founder of ProFound Talent and oolu, an AI-powered platform connecting businesses with fractional leaders. With 25+ years in executive search, she’s redefining how we hire — blending tech, heart, and strategy to grow companies and careers. Terri is a member of Digital Journal's Insight Forum.