When AI Flattens Strategy, How Will You Compete?

A staggering 78% of organizations now report using AI in at least one business function, propelled by the conviction that it will deliver a decisive competitive edge (McKinsey & Company, 2025; Ransbotham et al, 2020). Yet, this is where the new competitive paradox of the AI era emerges. As AI becomes a ubiquitous, commoditized utility, its power to confer a unique advantage diminishes. When every competitor leverages the same powerful, off-the-shelf models, the inevitable outcome is not widespread differentiation but a powerful drift toward algorithmic mediocrity—a state of competitive sameness where strategies converge on a predictable, machine-generated average (Messner, 2025).

The logic is straightforward. The underlying technology of accessible generative AI models is fundamentally derivative, excelling at producing plausible outputs by predicting the most statistically likely result based on vast training data (Messner, 2025). Simultaneously, intense competition among tech giants, the rise of open-source models, and easy access through cloud platforms are rapidly turning state-of-the-art AI into a commodity (McKendrick, 2024). When competing organizations all deploy the same commoditized AI tools to analyze the same public market data, they will inevitably be guided toward similar conclusions. The strategic challenge for leaders is no longer whether to adopt AI, but how to escape the powerful gravitational pull of homogenization it creates.

How Commoditized AI Erodes Advantage

More than two decades ago, Nicholas G. Carr (2003) argued in "IT Doesn't Matter" that as technology becomes ubiquitous, its strategic importance declines. AI is on the same trajectory but at an unprecedented speed. Fierce competition among model providers, the rise of high-performing open-source alternatives, and democratized access to computational power via the cloud mean that sophisticated AI is no longer a rare asset but a subscribed service (McKendrick, 2024). When a resource is universally available, advantage shifts from merely having it to how uniquely it is used.

This commoditization leads to mediocrity because generative AI models are probabilistic engines. Trained on colossal datasets, they generate a response by predicting the most likely sequence of words or pixels based on absorbed patterns (Messner, 2025). This "AI homogenization" pulls creative and strategic output toward a “monolithic” center (Mann, 2024). Research confirms this effect, showing that students using ChatGPT produced "eerily similar" essays and that application essays post-GPT showed a homogenization of the underlying ideas and themes (Chayka, 2025).

The consequences are already tangible. A brand's unique voice is diluted into "corporate beige" as teams rely on generic AI for marketing copy. More dangerously, strategy itself converges. When competitors use the same AI to analyze the same market data, they receive similar recommendations, neutralizing each other's moves. Finally, AI models can amplify societal biases found in their training data at scale (Hall et al., 2022). Amazon famously scrapped an AI recruiting tool that penalized resumes containing the word "women's," a bias learned from historical hiring data (Dastin, 2018). In the pursuit of short-term efficiency, leaders risk outsourcing critical thinking, eroding their organization's most durable assets: brand distinctiveness, unique market insights, and the capacity for original thought.

Designing for Differentiation

Escaping the homogenization trap requires moving beyond generic AI applications and to a unique combination of data, processes, and culture that competitors cannot replicate. This can be achieved by focusing on three strategic pillars.

Pillar I: Forge Your Data Moat

An organization's most defensible asset is its proprietary data (McKendrick, 2024). Success now depends on moving beyond plug-and-play AI and actively shaping it to your needs through fine-tuning—the targeted retraining of a general model using your organization’s unique knowledge base (OpenAI, 2025). A fine-tuned model becomes a strategic asset that embodies your organization's specific knowledge and brand voice, generating deeply contextualized output.

To build this data moat, leaders must conduct a strategic data audit to identify high-value proprietary data, invest in data quality and governance, and implement efficient fine-tuning techniques. For example, many of today’s most successful companies have built enduring competitive advantages by pairing AI with proprietary data:

• Netflix leverages decades of viewing behavior to power its recommendation engine.

• Amazon uses deep insights from user activity to personalize the shopping experience.

• Tesla improves its autonomous driving capabilities through continuous fleet learning.

• Google refines its search quality by analyzing click patterns and user feedback at scale.

Pillar II: Master the Human-AI Symbiosis

Sustainable advantage will be found not in the AI itself, but in the design of human-in-the-loop workflows that fuse the machine's speed with human creativity and critical judgment. This requires a shift from a "command-and-control" relationship with AI to one of creative collaboration.

Instead of asking AI to simply "write a marketing plan," a collaborative approach treats it as a thought partner to explore unconventional angles. Leaders can operationalize this by designing unique human-in-the-loop workflows with checkpoints for expert intervention, training teams in the art of collaborative dialogue, and establishing "AI red teams" to stress-test AI outputs for biases and blind spots. For more information on human-in-the-loop workflows, read our recent article: Developing "Human-in-the-Loop" Skills.

Pillar III: Institutionalize Critical Thinking

The danger of generative AI is the passive acceptance of its plausible, "good enough" outputs, which can erode intellectual rigor (Messner, 2025). Leaders must set the expectation that AI is a brilliant but flawed junior analyst—fast and knowledgeable, but prone to errors, biases, and a lack of real-world context. The human professional's role is to provide senior-level oversight. This requires promoting critical thinking skills, implementing AI fact-checking protocols for important data points, and rewarding employees who challenge AI-generated conclusions (Royce, 2025). By doing so, the technology forces the organization to become smarter to manage it effectively, creating a competitive advantage rooted not just in a superior AI system, but in a fundamentally more intelligent organization.

Leading Beyond the Average

The commoditization of AI is an inexorable force, threatening to pull every organization toward a mediocre center. Leaders who view AI as a simple plug-and-play tool for efficiency will find their strategies, brands, and innovations dissolving into a sea of sameness.

Yet, this threat is also an opportunity. The "Great Flattening" is creating a new basis for competition founded not on privileged access to technology, but on the uniqueness of a company's proprietary data, the ingenuity of its human-AI collaborative processes, and the intellectual rigor of its culture. 

The challenge is not to race for the fastest adoption of AI, but to build the most profound and differentiated symbiosis with it. The ultimate competitive advantage will be found in that which remains uniquely human: creativity, strategic judgment, and the passion to build something that cannot be averaged or replicated.

The future will belong to those who lead beyond the average.

References

Barney, J., & Barney, M. (2024). Why AI will not provide sustainable competitive advantage. MIT Sloan Management Review.

Carr, N. (2003, May). IT Doesn’t Matter. Harvard Business Review. https://hbr.org/2003/05/it-doesnt-matter

Chayka, K. (2025, June 25). A.I. Is Homogenizing Our Thoughts. The New Yorker. https://www.newyorker.com/culture/infinite-scroll/ai-is-homogenizing-our-thoughts

Dastin, J. (2018, October 11). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/

Hall, M. A., van, Gustafson, L., & Adcock, A. (2022). A Systematic Study of Bias Amplification. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2201.11706

Mann, H. (2024, March 5). AI Homogenization Is Shaping The World. Forbes. https://www.forbes.com/sites/hamiltonmann/2024/03/05/the-ai-homogenization-is-shaping-the-world/

McKendrick, J. (2024, February 7). As AI Rapidly Becomes A Commodity, Time To Consider The Next Step. Forbes. https://www.forbes.com/sites/joemckendrick/2024/02/07/as-ai-rapidly-becomes-a-commodity-time-to-consider-the-next-step/

McKinsey & Company. (2025, March 12). The state of AI: How organizations are rewiring to capture value. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Messner, W. (2025, June 9). Is AI sparking a cognitive revolution that will lead to mediocrity and conformity? University of South Carolina. https://sc.edu/uofsc/posts/2025/06/06-convo-messner-ai.php

OpenAI Platform. (2025). Openai.com. https://platform.openai.com/docs/guides/fine-tuning-best-practices

Ransbotham, S., Khodabandeh, S., Kiron, D., Candelon, F., Chu, M., & Lafountain, B. (2000). Expanding AI’s Impact With Organizational Learning. MIT Sloan Management Review. https://web-assets.bcg.com/1e/4f/925e66794465ad89953ff604b656/mit-bcg-expanding-ai-impact-with-organizational-learning-oct-2020-n.pdf

Royce, C., & Bennett, V. (2025, March 10). To Think or Not to Think: The Impact of AI on Critical-Thinking Skills. Nsta.org. https://www.nsta.org/blog/think-or-not-think-impact-ai-critical-thinking-skills

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