When Artificial Intelligence Does Strategy: Learning, Good Times, Lock-in, and Human-Driven Strategic Renewal

Source: Strategy Science (Neshenko, Ryall) · 2026-03

Abstract. What happens in industries where firms delegate strategy choices to private arti­ ficial intelligence (AI) agents? Would markets spiral into hypercompetition or settle into a comfortable status quo? We develop a formal model in which AI agents consider large business-model catalogs, predict performance, select, and learn from realized outcomes. Our representation accommodates existing AI paradigms, allowing for substantial increases in scale and computational capacity. We show that market dynamics converge to a self-confirming equilibrium; along the realized path, AI agents become well calibrated, and their choices become subjectively optimal—even though objectively superior business models may remain unexplored. This convergence can indeed sustain high profits. How­ ever, it also produces strategic lock-in; novel business-model implementations become rare long before catalogs are exhausted. This creates a distinct role for humans. A single episode of human-driven frame expansion—introducing a genuinely new business model to a catalog—can disrupt the AI-induced equilibrium and initiate strategic renewal. Yet, the ability to do so does not imply that it will be done. When the prevailing equilibrium is suf­ ficiently lucrative, managers rationally refrain from triggering renewed learning. Our results clarify where humans still matter in AI-enabled strategy: deciding when to change the frame and not merely optimizing within it.

Introduction. Scholarship at the intersection of artificial intelligence (AI) and business strategy has moved quickly from documenting application-level performance gains (see, e.g., the review by Enholm et al. 2022) to more recent work on how AI reshapes competition, the architecture of strategic decision making, and the design of hybrid human-AI organizations.1 A recurring theme in this recent literature is that because AI technologies diffuse quickly, persistent performance advantages built on AI-based applications must depend less on the technol­ ogy per se and more on embedding it in complemen­ tary resources—data, routines, governance, etc.—that thereby make the resulting capabilities firm specific (e.g., Mikalef et al. 2020, Krakowski et al. 2023, Kemp 2024) and hence, sustainable. Much of this work views the “human-in-the-loop” question in an issue of automation-augmentation design (see, e.g., the reviews by Joshi 2025 and Nikzat 2025). A parallel stream stud­ ies Large Language Models (LLMs) as tools for generat­ ing and evaluating strategic alternatives, generally finding that performance is sensitive to the setting, evaluation architecture, and division of labor between people and machines (e.g., Csaszar et al. 2024, Doshi et al. 2025, L ́opez-Sol ́ıs et al. 2025). Work in the emer­ gent theory-based view emphasizes that current LLMs optimize predictive plausibility rather than causal explanation and therefore, do not substitute for theorybased reframing (e.g., Felin and Zenger 2017, Bender et al. 2021, Felin and Holweg 2024).

In this study, we approach the AI/strategy issue from a substantially different perspective. Rather than considering AI functionality in the role of a constituent part of a single firm’s strategy-making process—choice assessment, resource enhancement, coordination mech­ anism, or business-model modifier—we examine a setting in which the role of AI is promoted to compre­ hensive strategy generation and selection by all firms within a market. We ask the following question. What would happen in an industry in which the rivals dele­ gated strategic decision making to individual AI agents2 operating under existing technological para­ digms? This is not as far fetched as it once may have seemed. Across industries, firms are already delegating components of strategy formulation to AI systems that integrate language models, optimization algorithms, and automated decision making (Belhadi et al. 2021, Haefner et al. 2021). If ours is not already a feasible sce­ nario, increased computing power, refined algorithms, and expanded data availability may soon make it so.

This perspective raises a number of interesting ques­ tions, the following of which we investigate.

1. How would widespread AI adoption for the gener­ ation of business strategies affect firm profits? Would firms experience a downward spiral in profits because of AI-driven hypercompetition—with AI agents exploring the competitive landscape at superhuman speeds and rapidly squeezing out all of the valuable positions? Would this be the analog of modern financial markets in which large investment firms presently use AI to com­ pete at speeds of microseconds or even nanoseconds to make arbitrage profits measured in fractions of a cent per trade. Or, on the opposite side of the spectrum, would AI agents generate a profitability boon by discov­ ering “blue oceans” free of competitive imitators and/or adopting sophisticated collusive behaviors designed to foil detection and maximize incumbent profits?

2. Closely related to the profitability question is the question of strategic renewal. Does AI have the capac­ ity to generate breakthrough strategies based upon genuinely novel business models independent of human involvement? Recent experiments in which AI functions as primary innovator present some promis­ ing results (Arias-P ́erez et al. 2025). However, it is worth pointing out that in all of the studies cited, human involvement is still required in some form or another. Thus, especially in the complex problem domain of business strategy, it remains an open ques­ tion whether strategic innovation can be fully auto­ mated. Nevertheless, we tip the scales toward AI and begin from the premise that AI technologies are capable of independently generating novel strategies in a fash­ ion consistent with existing paradigms. Even with this generous stipulation, is there something in the nature of these technologies that implies that—at some point—strategic renewal inevitably dies?

3. Finally, thinking about scenarios in which strat­ egy making is entirely off loadable to AI raises the question of what productive role, if any, humans might still play? If AI has the ability to discover novel, highly profitable “blue ocean” strategies and continue to do so indefinitely, then the answer to that question is “not much.”

Related work. Work in the intersection of AI and strategy has con­ sidered AI as a constituent element in the firm’s strategy-making process. For example, much of the the­ oretical discussion about AI in strategy has been about whether AI as a resource can be the source of sustained competitive advantage. Because AI technologies are widely available, they are neither rare nor inimitable, and therefore, they cannot be the source of sustained advantage. Thus, much of the discussion in the litera­ ture has been around how to embed AI technologies within resource portfolios in ways that make the port­ folio as a whole rare and inimitable. Human-AI com­ plementarities have been explored as a promising source of productive scarcity.

We take a new tack by exploring a world in which the function of AI systems is promoted to the autono­ mous selection of the entire strategy. At this level, it is not obvious that—even if all of the firms run identical AI agents—low levels of profit are implied. As Gans and Ryall (2017) point out, other things equal, firms have strong incentives not to imitate each other. Given the power of AI agents under existing technological para­ digms to, in principle, generate distinctive, highly prof­ itable business models, it is altogether possible that AI-driven firms learn ε-SCEs in which all enjoy high profits from distinctive strategic positions, with none moving to imitate another even if they could (everyone sails comfortably in their own blue lake).

Although many AI advocates question whether humans will ultimately add anything to functions dele­ gated to an AI agent, we give humans the benefit of the doubt and assume that they can. At the same time, we ask a very relevant question in this context. Even if humans could nudge their AI agents out of a status quo and into a new phase of strategic renewal, would they? The bad news (presumably for advocates on both sides of the human versus AI divide) is that the better AI does at doing its job (achieving high profitability with high reliability), the less incentive either AI agents or humans have to innovate at the strategic level. To the best of our knowledge, this issue has not been raised in any previous works.

Finally, as mentioned above, this paper also adds to an extant stream of work in strategy on self-confirming equilibria. Thus, although we frame our analysis around AI delegation, the SCE concept is not limited to that context. Prior strategy research has shown that purely human organizations can also become trapped in self-confirming patterns; for example, Repenning and Sterman (2002) demonstrate how capability traps arise when managers, learning only from on-path feed­ back, rationally underinvest in process improvement even when doing so would be objectively superior.

Method. Section 2 builds a nontechnical bridge from present AI systems to the formal objects in the model, and it clarifies how we use “awareness,” “frames,” and the within-frame versus expanded-frame distinction. Sec­ tion 3 then presents the formal market model. Sections 4 and 5 establish the convergence and incentive results, and Section 6 discusses implications and caveats.

2. Delegating Strategic Reasoning to AI Agents This section provides a nontechnical bridge between the formal model and how AI systems are currently used in practice. Our goals are to (i) describe a realistic AI agent architecture integrating knowledge retrieval, optimization, and adaptive learning for strategic rea­ soning and guidance; (ii) define what we mean by an AI agent’s awareness frame in a way that is operational (and therefore, robust to how these systems are deployed); and (iii) make explicit what in our model humans add to the system in the context of present deployment practices. We also wish to clarify the dis­ tinction between strategic innovation that can be classi­ fied as within frame versus frame expanding.

2.1. The AI Paradigm Underlying Our Model We are unaware of any AI systems presently deployed as “end-to-end” strategists. Instead, they appear as modules in a pipeline that separates the generation of candidate business options from evaluation and selection. On the generation side, emerging LLM-based tools (often with retrieval) are beginning to assist in mapping a business context into structured proposals: a market posture, a pricing scheme, a channel strategy, a product road map, or a reconfiguration of activities. On the eval­ uation side, organizations rely on established quantita­ tive systems—forecasting demand, churn, conversion, capacity utilization, or risk—and then, choose among policy alternatives using optimization algorithms.

It is not hard to imagine a time too far in the future at which advances in hardware, refinements of algo­ rithms, and increased data availability will permit AI agents operating under this existing technological paradigm to be capable of end-to-end strategic guid­ ance. Such agents will integrate multiple capabilities— synthesis, evaluation, and selection—for end-to-end strategic guidance, regardless of their underlying archi­ tectural implementation. These capabilities map directly. We identify the business model as the object of strategic choice, where we define a “business model” as a description of an activity system (Porter 1996) and the portfolio of resources required to support it (Wer­ nerfelt 1984, Barney 1989). Thus, the generator pro­ poses a set of candidate business models, the evaluator predicts their consequences, and the selector chooses an optimal candidate to implement.

Two features of present deployments matter for our formalization. First, these modules operate over bounded catalogs. In practice, the generator is constrained by admissible action templates exposed to it. In our context, this would include resource manifests (tangible and intangible, with descriptions of their relevant features), libraries of feasible portfolio relationships between resources, and registries of activities that can be sup­ ported by different resource configurations. The evalua­ tor is constrained by the outcome labels and explicated performance measures that the organization tracks (rev­ enue, margin, retention, growth, quality, safety, regula­ tory risk, etc.). Finally, the selector optimizes over finite horizons; this is because our AI agents are assumed to make explicit predictions about the future consequences of business-model choices period by period and store them both as the concrete specifics of what to expect and as the feedback benchmark against which learning is transmitted. Given finite storage constraints (even large ones), this implies that AI agent forecasts must be of finite length.

Discussion. 5. AI Incentives Against the Frame- Expanding Innovation Having established how purely AI-driven businessmodel selection and the greedy benchmark for frame expansion converge to an ε-SCE, we now introduce a single-agent, one-shot episode of frame-expanding innovation. The last section simply assumed that one of the human managers doggedly engaged in novel inno­ vation until her AI agent became aware of every busi­ ness model that she could imagine, allowing for the possibility that her introduction of novel business mod­ els might generate surprise consequences for the agents of her rivals and herself.

In this section, we examine a manager’s decision of whether to do frame-expanding innovation in the first place. This decision creates a challenge. Given the man­ ager’s lack of knowledge of the explicit details of the business models of which she is unaware, how can she assess whether to innovate or not? In the previous sec­ tion, we sidestepped this problem by simply assuming that her vague sense that some valuable business model existed beyond her awareness frame was suffi­ cient to induce her to innovate every period until the set of novel business models was exhausted.

Our approach to intentional assessment of known unknowns follows in the spirit of Karni and Vierø (2013, 2017) and related work. These papers focus on modeling decision makers who are aware that they are unaware of certain relevant aspects of their decision problems.

Let us return to our manager m whose market has converged to an ε-SCE generated by the rolling-T ker­ nels in accordance with Theorem 1. Assume that we pick up the action in period t with m facing the individ­ ual history hm,t and equipped with a stable awareness frame Adisc m ˆ (Bdisc m , Cdisc m , Edisc m , λdisc m ). Let σSCE be the behavior profile at that point, and to help conserve on notation, denote the associated subjective expected dis­ counted payoff to m by maintaining the AI system’s sta­ tus quo t by VSQ m ˆ ̃Em(σSCE m | ̃em,hm,t) as defined in Equation (B.1).

5.1. To Expand the Frame or Not to Expand Suppose m must pay a fixed cost K > 0 to conduct a frame-expanding innovation for an efficiency-improving business model. The agent believes that the search 1. succeeds with probability ̃η ∈(0,1). If so, a single model ˆbm is revealed from her unawareness frame and added to her AI’s business-model awareness frame.

2. fails with probability 1 ̃η, in which case her awareness frame remains unchanged.

Although the agent cannot imagine precisely what she will discover, she has a subjective belief that if her exploration is successful, the new business model will result in a cost reduction per period within some range [ ̃smin, ̃smax]. To evaluate her preferences over intervals, we adopt the approach in Brandenburger and Stuart (2007) and equip her with a preference parameter ̃β ∈ [0,1] such that ̃s( ̃β) ≡(1 ̃β) ̃smin + ̃β ̃smax:

Thus, a larger value for ̃β reflects greater optimism on the part of the agent.

Finally, adopting a new business model may result in unexpected patterns of consequences and surprises for rivals. Two concerns may arise as a result. The first is that the disruptions may cause a punishment response from the market. A simple example is the punishment phase that rivals impose when they are fol­ lowing a Grimm strategy. To account for this, let ̃φ ≥0 denote m’s subjective assessment of the expected pre­ sent value of the opportunity costs that she will incur because of an explicit punishment phase imposed by her rivals or because of poor performance that she may experience as the market enters a new convergence period provoked by the deployment of her novel busi­ ness model.

Conclusion. We analyzed markets in which firms fully delegate the generation of strategy options to AI agents built on tech­ nological architectures of a kind consistent with those that presently exist. By “of a kind consistent,” we mean LLM-style generative models coupled to explicit pre­ diction and optimization modules: large neural net­ works trained on vast corpora that can propose coherent strategic options together with statistical lear­ ners that forecast consequences and score options against declared objectives.

What happens when strategic decision making is fully delegated to AI systems? How does removing human judgment from strategy development affect firm behavior and the long-run potential for strategic renewal? Our analysis shows that when strategic deci­ sion making is fully delegated to AI agents running on systems consistent with modern technological architec­ tures, markets can converge to a (near-)self-confirming equilibrium in which agents optimize business-model choices using feedback generated by their own actions. This convergence is not a failure of reasoning but an implication of rational learning within a bounded awareness frame.

On the one hand, the result that we demonstrate is remarkable. AI agents learn in a competitive setting; in a decentralized fashion, they learn to make accurate predictions about the performance consequences of the business models that they adopt from their awareness frames, and the business models that they adopt are (near) optimal with respect to those predictions. The SCE that the market settles into may be dynamic in the sense of responding to contingencies (such as demand shocks or cycling periods of market leadership).