WHEN AI BECOMES THE STRATEGIST: HUMAN AI COLLABORATION, STRATEGIC CONFORMITY, AND COMPETITIVE DECISION-MAKING

Generative artificial intelligence and large language models are increasingly used not only to support operational
tasks but to generate and evaluate business strategy itself. This conceptual article examines what happens to
competitive decision-making when AI moves from an analytical tool to something closer to a strategic
collaborator. Drawing on human–AI complementarity theory, institutional theory, and the dynamic capabilities
perspective, the article argues that AI-generated strategic advice carries a structural risk: because large language
models are trained on shared bodies of business text and respond to similar prompts with similar reasoning
patterns, firms that lean heavily on AI for strategic recommendations may converge toward common playbooks
rather than differentiate from rivals. This risk echoes long-standing accounts of institutional isomorphism, but
with a new mechanism a shared algorithmic advisor rather than shared professional norms or regulatory pressure.
At the same time, evidence on human-in-the-loop decision-making suggests that keeping a human's judgment in
the final step of a recommendation process need not weaken decision quality and may improve how
recommendations are received and acted on. The article synthesizes this literature into a conceptual framework
distinguishing condition under which human–AI collaboration is likely to preserve strategic diversity from
conditions under which it is likely to produce conformity, and it closes with managerial implications for firms that
want to use AI's analytical power without surrendering the strategic distinctiveness that competitive advantage
depends on.

Artificial intelligence; human AI collaboration; strategic conformity; strategic decision-making; competitive