From Resource Rigidity to Adaptive Capacity:
Dynamic Capabilities in the Age of AI

The emergence of large language models and generative AI systems from 2022 onward represents a qualitative departure from the prior trajectory of automation technology. Earlier waves of digital transformation — enterprise resource planning, cloud computing, robotic process automation — primarily altered the efficiency with which firms executed existing knowledge-based processes. Generative AI, by contrast, encroaches directly on the cognitive tasks — writing, analysis, synthesis, code generation, pattern recognition across unstructured data — that constituted the core of organizational knowledge work and whose tacitness had historically insulated them from substitution pressure. This distinction is theoretically consequential. The tacit knowledge stocks that Barney (1991) identified as the foundation of sustained competitive advantage were valuable precisely because their non-codifiability rendered them imperfectly imitable. AI systems capable of approximating tacit knowledge outputs — legal reasoning, strategic analysis, customer relationship management, research synthesis — erode this imitation barrier in a structurally novel way, not through competitor learning or organizational reverse-engineering, but through the commoditization of cognitive output itself.

The pace of diffusion compounds the theoretical challenge. Historical technological transitions — the adoption of electricity in manufacturing, the spread of enterprise computing — unfolded over decades, providing incumbent firms with adjustment horizons sufficient to enable incremental capability realignment. The generative AI transition, by contrast, compressed the interval between technological emergence and broad organizational applicability to approximately twenty-four months. GPT-4's release in March 2023 was followed within eighteen months by organizational AI deployment frameworks, sector-specific fine-tuned models, and AI-integrated workflow tools across knowledge-intensive industries. This compression eliminates the adjustment buffer that prior theoretical treatments of technological disruption implicitly assumed, and renders the dynamic capabilities framework's emphasis on temporal sensitivity — the notion that capability development trajectories matter as much as capability endowments — analytically urgent in a way that prior technological transitions did not demand.

A second distinctive feature of the AI transition concerns its pervasiveness across industry boundaries. Prior general-purpose technologies — the steam engine, electricity, the internet — exhibited pervasive effects but were mediated by substantial sector-specific adoption barriers in physical capital, infrastructure, and regulatory context. Generative AI's primary delivery mechanism, software-as-a-service and API-based integration, eliminates many of these mediation barriers. The implication is that the AI capability shock is not confined to a technology sector facing incumbent disruption; it simultaneously restructures the competitive environments of professional services, financial intermediation, healthcare administration, education, logistics, and manufacturing knowledge work. For strategic management theory, this pervasiveness implies that no sector provides a stable institutionalized refuge from the capability obsolescence pressure that the AI transition generates — a condition that renders dynamic capability development not a differentiating strategic choice but a baseline survival requirement for incumbent firms across industries.

Barney's (1991) resource-based view established that sustained competitive advantage derives from firm resources that are simultaneously Valuable, Rare, Inimitable, and Non-substitutable — the VRIN framework. The framework's predictive power rests on the assumption that the environmental conditions determining which resource attributes satisfy these criteria are relatively stable. Technological discontinuity of the magnitude represented by generative AI does not merely alter the value of specific resources; it reorganizes the structural conditions under which the VRIN criteria are satisfied. Resources that satisfied all four conditions under the prior technological regime may simultaneously lose value, become replicable through AI-mediated processes, and face substitution — rendering the entire resource portfolio strategically inadequate within a compressed timeframe.

Consider the category of accumulated knowledge assets — proprietary databases, codified analytical frameworks, domain-specific expertise embedded in human capital — that constituted the primary competitive advantage of knowledge-intensive incumbents across professional services, consulting, and research-intensive industries. Under the pre-AI regime, these assets satisfied the VRIN conditions through a combination of historical accumulation barriers (value), organizational embeddedness (rarity), tacit knowledge components (inimitability), and absence of functional substitutes (non-substitutability). The arrival of large language models, trained on corpora that encompass substantial portions of publicly available domain knowledge and capable of generating domain-specific analytical output at low marginal cost, directly attacks all four conditions simultaneously: it reduces value (output is substitutable at lower cost), eliminates rarity (similar capabilities are accessible to all firms through API subscription), undermines inimitability (the tacit knowledge component that generated imitation barriers is partially replicated by model training), and introduces a functional substitute of broad applicability. The theoretical implication is that resource stocks that satisfied the VRIN conditions under the prior technological regime can transition to value-destroying liabilities under the AI regime — not because they generate negative cash flows, but because the organizational commitments, governance structures, and investment patterns built around their protection prevent adaptation toward the capability configurations that the new environment rewards.

This dynamic is precisely what Levinthal and March (1993) identified as the capability trap: the systematic tendency of successful organizations to over-exploit existing capabilities relative to the exploration of new ones. In the AI transition context, the capability trap operates through multiple reinforcing mechanisms. Firms with deep investments in human capital configured around pre-AI knowledge work face dual pressures — the sunk costs embedded in existing talent structures and the internal political resistance of constituencies whose organizational status depends on the continued value of those structures. Firms with established process architectures built around human-mediated knowledge flows face coordination costs in restructuring those architectures around AI-mediated alternatives. And firms with strategic identities built around expertise-based differentiation face organizational identity challenges in repositioning around AI-augmented capability claims. These mechanisms are mutually reinforcing: each increases the perceived switching cost of capability reconfiguration, extending the adjustment horizon precisely when the technological diffusion rate demands compression of it.

Teece, Pisano, and Shuen's (1997) dynamic capabilities framework was developed to explain how firms sustain competitive advantage in rapidly changing technological environments — a context that precisely describes the AI transition. The framework's central insight is that sustainable competitive advantage in turbulent environments derives not from the possession of superior resource stocks but from the higher-order capacity to sense environmental changes before they fully manifest in market signals, seize new resource configurations before competitors establish position, and reconfigure organizational architectures at the pace the environment requires. Each of these three capability classes maps onto a specific adaptive challenge that the AI transition places on incumbent firms, and together they define the organizational capability profile that distinguishes firms capable of sustained competitive repositioning from those that will undergo capability-driven performance decline.

A critical distinction structures the analysis that follows. Operational capabilities — the organizational routines that execute existing activities efficiently — and dynamic capabilities differ not in degree but in kind. An incumbent firm may achieve world-class operational performance in AI-augmented workflow execution while simultaneously lacking the dynamic capabilities required to reconfigure its fundamental business model, knowledge architecture, or competitive positioning in response to AI-driven industry restructuring. The firms that achieve sustained competitive advantage through the AI transition are those that invest not merely in AI tools deployment (an operational capability enhancement) but in the organizational capacity to continuously realign their entire capability portfolio with the evolving technological frontier — a dynamic capability investment of fundamentally different character.

A central theoretical proposition emerging from this analysis is that the AI transition generates a capability sequencing problem that prior applications of the dynamic capabilities framework have not adequately addressed. In the supply chain fragmentation context analyzed in Brief No. 1, the sensing–seizing–reconfiguring sequence operated at broadly compatible temporal horizons: geopolitical signals, while difficult to interpret, unfolded over years, providing adjustment time consistent with organizational reconfiguration capacity. The AI transition compresses this sequence: the interval between the emergence of a materially disruptive AI capability and its broad adoption by competitive actors has contracted to quarters rather than years. This compression means that firms lacking pre-existing sensing and seizing routines — organizations for which dynamic capability development was not a prior strategic investment — face the AI transition with capability deficits that cannot be remedied within the available adjustment window. Path-dependence thus generates a structurally disadvantaged cohort of incumbents: those whose prior competitive success in stable environments provided no organizational impetus to develop the dynamic capabilities that the current environment demands.

The theoretical complement to the capability trap is what might be termed the complementary asset recombination opportunity — a concept that Teece (1986) anticipated in his analysis of how incumbent firms can leverage co-specialized assets to appropriate returns from technological innovation even when they do not originate it. Incumbent firms possess organizational assets that AI-native entrants lack: established customer relationships, regulatory approvals and compliance infrastructure, domain-specific operational knowledge, and institutional trust accumulated through sustained market presence. These complementary assets retain substantial value under AI-intensive competition and, critically, can be recombined with AI capabilities to generate competitive positions unavailable to new entrants. The dynamic capability required to execute this recombination — sensing the AI capabilities that most powerfully augment existing complementary assets, seizing the organizational configurations that activate the recombination potential, and reconfiguring governance and knowledge structures to sustain the combination's competitive advantage — represents the theoretically precise account of how incumbents achieve sustained advantage through the AI transition rather than despite it.

For incumbent firms in knowledge-intensive industries, the primary strategic imperative is what the dynamic capabilities framework would identify as complementary asset recombination: the deliberate organizational investment in identifying which elements of the existing resource portfolio retain VRIN conditions under AI-intensive competition, and developing the sensing and seizing capacity required to combine those assets with AI capabilities before competitive actors establish dominant positions. The analytical error to be avoided is the conflation of AI tool deployment — an operational capability enhancement — with dynamic capability development. Firms that achieve high rates of AI tool adoption without investing in the organizational capacity to continuously realign their resource portfolio with evolving AI frontiers will generate one-time productivity improvements that competitors rapidly replicate, without achieving the dynamic capability endowment required for sustained competitive repositioning. The distinction between operational AI adoption and dynamic capability development is the central strategic diagnostic challenge of the current transition period.

A second strategic implication concerns human capital architecture. The complementary asset that most clearly retains value under AI-intensive competition — and that AI-native entrants cannot rapidly replicate — is accumulated domain-specific judgment: the capacity to evaluate AI-generated outputs, identify their failure modes in specific institutional and organizational contexts, and integrate them with tacit relational knowledge that AI systems cannot generate. Incumbent firms that invest in reconfiguring their human capital architecture toward this complementary function — developing what might be termed AI-augmented judgment capacity rather than AI-substituted labor — position themselves to extract sustained competitive advantage from the human–AI complementarity that the AI transition enables. This reconfiguration requires not merely reskilling investments but the redesign of organizational roles, performance evaluation systems, and knowledge management structures to reward and develop AI-augmented judgment as the primary human capital asset.

For firms in emerging markets and developing economies, the AI transition creates a structural opportunity analogous to — and potentially larger than — the GVC integration opportunity of the prior two decades. AI capabilities accessible through API-based interfaces can compress the knowledge and analytical capacity gaps that historically constrained knowledge work quality in resource-constrained organizational environments. However, the conversion of AI access into sustained competitive advantage requires the same dynamic capabilities — sensing, seizing, reconfiguring — that the framework identifies as the determinants of sustained advantage under technological discontinuity more broadly. The risk of AI-enabled capability catch-up being reversed by more rapid AI frontier advancement among leading-economy incumbents is real and requires a strategic response centered on the development of organizational dynamic capabilities rather than mere technology adoption. The theoretical implication is that dynamic capability development is not a luxury investment for resource-constrained organizations in developing economy contexts; it is the primary mechanism through which AI access translates into sustained organizational improvement rather than temporary efficiency enhancement.

For academic and research institutions, the AI transition generates a distinctive reconfiguration challenge. Universities and research organizations built competitive positions on the scarcity of expert knowledge access — a scarcity that AI systems are systematically eroding. The dynamic capability response required is not resistance to AI adoption but a fundamental reconfiguration of institutional value claims: from knowledge access provision toward the cultivation of AI-augmented analytical judgment, critical evaluation capacity, and the institutional trust that sustains the credibility of knowledge claims in AI-saturated information environments. The RBV assets that research institutions retain — methodological rigor, reputational credibility, peer validation infrastructure, and the relational networks that sustain research collaboration — represent precisely the complementary assets that AI-native information providers cannot replicate. Institutions capable of sensing this strategic landscape, seizing AI capabilities that amplify rather than substitute for these complementary assets, and reconfiguring their organizational identity around the resulting capability combination are positioned for sustained relevance in the AI-intensive academic environment.