Why Organizational Context Not AI Models Determines Your Competitive Edge

The Invisible Layer That Drives Performance

The promise of artificial intelligence has centered on a seductive proposition: deploy advanced models and watch productivity soar. Yet organizations investing heavily in AI are discovering an uncomfortable truth. Two companies can implement identical AI systems, follow the same best practices, and hire from the same talent pools, yet produce dramatically different results.

Rohan Narayana Murty and Ravi Kumar S, writing in Harvard Business Review, argue that this performance gap stems from organizational context—the largely invisible patterns of judgment, coordination, and trade-offs that shape how work actually unfolds. Their central thesis: when everyone has access to the same AI models, context becomes the primary competitive differentiator.

This argument deserves serious examination. It challenges prevailing assumptions about AI strategy and raises fundamental questions about where competitive advantage resides in an age of technological parity.

Why AI Amplifies the Value of Context

The authors open with a compelling comparison of two B2B technology companies serving different markets. On paper, their sales processes look identical. Both use similar CRM systems, follow comparable stage gates, and maintain standard forecasting cadences. Yet their execution diverges in ways that materially affect revenue and risk.

The first company serves regulated industries where delivery certainty trumps speed. Sales teams routinely pause deal advancement while delivery leaders validate feasibility assumptions. Risk and legal teams engage early, even when terms remain fluid. Escalations occur only after multiple internal validations.

The second company serves digitally-driven enterprises competing on speed. Early momentum matters more than perfect certainty. Sellers pull delivery leaders into exploratory conversations before scope is finalized. Signals of customer urgency trigger escalation even when information is incomplete.

Neither pattern is captured in their respective CRM systems. These systems record stage changes and forecast updates but miss the crucial layer of judgment: how feasibility was debated, which risks were weighed, why escalation was deferred. This ephemeral layer—living in emails, chats, working documents, and conversations—constitutes organizational context.

The observation is astute. Traditional enterprise systems capture outcomes but not the process by which those outcomes were reached. This gap has existed for decades, but AI makes it consequential in new ways.

The authors argue persuasively that AI democratization paradoxically increases the value of organizational context. As frontier models become cheaper and more accessible, model quality ceases to differentiate. Instead, how precisely intelligence is grounded in operational reality determines AI return on investment.

AI systems are general by design. They cannot infer which signals matter, which trade-offs take precedence, or how risk and speed should be balanced unless these patterns are made explicit. When AI is layered onto generic processes, it standardizes behavior. When grounded in organizational context, it amplifies what makes that organization distinctive.

This explanation helps account for a puzzling phenomenon: why so many AI pilots fail to scale. Early experiments show promise in controlled environments, but when deployed across real workflows, impact plateaus. The authors attribute this to a lack of grounding in how teams actually operate. Without understanding institutional trade-offs and coordination norms, AI struggles to navigate operational complexity.

The diagnosis rings true. Organizations typically respond to scaling challenges by tuning prompts, adding documents, or expanding training data. These interventions help at the margins but rarely address the core problem: the model lacks the organization's operating logic.

Yet this framing also raises questions. If context is so valuable, why has it remained largely uncaptured? The authors suggest that historically, organizations embedded context by redesigning workflows and systems—a costly, time-consuming process. Today, they argue, contextual knowledge can ground models directly, shifting leverage from redesigning systems to amplifying judgment.

This is an important claim, but it warrants scrutiny. Capturing context at scale requires solving difficult technical and organizational problems that the authors acknowledge but perhaps understate.

Context as a Strategic Resource

To strengthen their case, Murty and Kumar invoke the resource-based view of strategy. They argue that organizational context meets the VRIN criteria for sustainable competitive advantage: it is Valuable, Rare, Inimitable, and Non-substitutable.

The logic is compelling. Context is valuable because it shapes revenue, risk, speed, and customer trust. It is rare because it reflects a unique mix of customers, constraints, and accumulated decisions. It is difficult to imitate because competitors can copy processes but not years of tacit learning embedded in execution. It is non-substitutable because without context, even advanced AI systems flatten differences that matter.

This framework provides theoretical grounding for the main argument. However, the resource-based view also teaches that competitive advantages erode. Resources must be continuously renewed to remain valuable. The authors acknowledge that context must be “coherent, aligned with strategy, and reinforced through daily action,” but they say less about how context evolves or when it should be deliberately disrupted.

Consider the risk of over-optimization. Organizations that excel at capturing and reinforcing existing context may inadvertently cement current ways of working, making adaptation harder when markets shift. Netflix’s context around DVD-by-mail distribution was sophisticated and effective, yet the company had to deliberately disrupt that context to build streaming capabilities. Too much emphasis on preserving context could inhibit necessary strategic pivots.

The authors might respond that context engineering includes mechanisms for updating contextual models based on performance data, creating a feedback loop that allows context to evolve. This is plausible, but it assumes that organizations can reliably detect when historical patterns have become liabilities rather than assets—a challenge that has defeated many established firms.

The Privacy and Culture Challenge

The article’s most significant omission concerns organizational culture and employee privacy. The authors recommend investing in technologies that “map how work unfolds across systems and collaboration environments, creating a continuously updated view of decision sequences, coordination patterns, and trade-offs.”

This vision of comprehensive work tracking raises immediate questions about surveillance, autonomy, and trust. Knowledge workers may resist systems that monitor their emails, chats, and documents to extract behavioral patterns. The authors acknowledge that “trust is foundational” and note the need for “clear privacy, security, and access controls,” but they devote only a few sentences to what may be the most substantial implementation barrier.

Research on workplace monitoring consistently shows that employees react negatively to surveillance, even when the stated purpose is benign. A 2024 study published in the Journal of Applied Psychology found that comprehensive digital monitoring decreased employee creativity, increased stress, and led to higher turnover—outcomes that would undermine the very context organizations seek to preserve.

The authors’ light treatment of this issue is particularly striking given their focus on trust and coordination. Effective coordination depends on psychological safety and candid communication. Will teams communicate as openly if they know that every email and chat is being analyzed to extract behavioral patterns? Will they experiment with unconventional approaches if deviations from established patterns trigger algorithmic alerts?

These are not hypothetical concerns. They point to a fundamental tension in the context engineering vision: the act of systematically capturing context may alter the context itself, potentially degrading the very resource the organization seeks to leverage.

From Concept to Implementation

The article’s second half shifts from argument to prescription, outlining four actions for leaders: instrument work to capture context, make the context library a runtime layer for AI, establish governance and trust, and monitor impact to close the ROI loop.

This framework is sensible as far as it goes, but it underestimates implementation complexity. Building a “work graph” that accurately captures decision sequences and coordination patterns across diverse systems and collaboration tools is a substantial technical challenge. Existing enterprise integration architectures struggle with far simpler tasks.

Moreover, the recommendation that “every AI system should connect to the context library” assumes a level of technical standardization and interoperability that few organizations possess. In reality, most enterprises operate dozens of AI tools and hundreds of business applications, each with different architectures, data models, and integration capabilities. Creating a unified context layer that can serve all these systems reliably and in real time would require massive infrastructure investment.

The authors’ prescription that “too little context produces generic output” while “too much introduces noise and increases the likelihood of error” is undoubtedly correct. But determining the right amount of context for any given task is itself a complex problem requiring sophisticated intent inference and retrieval mechanisms. The article gestures toward these challenges without fully reckoning with them.

This is not to say the vision is unachievable, but rather that the path from current state to context-driven AI enterprise is longer and harder than the article suggests. Organizations will need to make substantial investments in new technologies, restructure data governance, retrain IT functions, and manage significant change management challenges.

Alternative Perspectives on AI Differentiation

The article’s focus on context as the primary differentiator deserves comparison with alternative views on AI competitive advantage.

Some strategists argue that proprietary data, not organizational context, will be the decisive factor. According to this view, organizations with unique datasets—customer behavior, operational metrics, market signals—can fine-tune models in ways competitors cannot replicate. While Murty and Kumar would likely argue that proprietary data is a form of context, the distinction matters: data can be captured and stored relatively easily, while behavioral context is far more elusive.

Others contend that execution speed and cultural adaptability matter more than contextual depth. In rapidly changing markets, the ability to quickly experiment, fail, learn, and pivot may trump the advantage of deeply embedded historical patterns. Organizations optimized for past contexts may struggle when discontinuous change renders that context obsolete.

Still others point to ecosystem orchestration as the key differentiator. Companies that build platforms, cultivate partner networks, and shape industry standards may capture value regardless of their internal operational context. Amazon’s competitive advantage stems partly from its sophisticated logistics context, but perhaps more from its position as an orchestrator of third-party sellers, cloud customers, and advertising relationships.

These alternative views are not necessarily incompatible with the context thesis, but they suggest that context is one differentiating factor among several, not the sole determinant of AI performance.

Context Across Different Organizational Functions

One of the article’s strengths is its recognition that context matters across functions, not just in sales. The authors note that in procurement, systems capture purchase orders but not how exceptions are negotiated. In customer service, ticketing systems log resolution codes but not coordination patterns that prevent escalation. In finance, ERP systems record transactions but not the judgment behind credit decisions.

This cross-functional perspective is important. It suggests that context engineering cannot be a siloed initiative owned by a single department. Instead, it requires enterprise-wide commitment and coordination—a governance challenge that many organizations will find daunting.

However, the article could go further in exploring how context varies by function and what that means for implementation. Sales context in B2B environments may involve relatively structured decision sequences with identifiable stakeholders and clear escalation paths. Engineering context around technical problem-solving may be more fluid, involving rapid iteration, informal collaboration, and tacit knowledge that is difficult to articulate even for the participants.

Similarly, regulatory and compliance contexts in industries like healthcare and financial services may need to be captured with extreme precision and auditability, while creative functions like marketing and product design may benefit from more loosely defined contextual frameworks that preserve room for experimentation.

A more nuanced treatment of functional differences would strengthen the implementation guidance and help leaders prioritize where to begin context engineering efforts.

The Broader Implications for Management

Beyond its specific prescriptions, the article makes a larger point about the changing nature of enterprise technology. The authors argue that in a model-driven enterprise, context becomes infrastructure: “a continuously updated representation of how the organization works.”

This shift has profound implications for how technology is governed and managed. Historically, enterprise technology was primarily designed, governed, and maintained by centralized IT or engineering functions. If context becomes infrastructure, business functions must take a much more active role in defining, validating, and updating contextual models.

This redistribution of responsibility could be healthy, forcing closer collaboration between business and technology functions. But it also creates new coordination challenges and accountability questions. Who owns the context library? Who determines which patterns should be reinforced versus disrupted? How are conflicts between different functional contexts resolved?

The article also has implications for organizational learning and knowledge management. For decades, companies have struggled to capture and transfer tacit knowledge, particularly as experienced employees retire or leave. Context engineering offers a potential solution: systematically capture behavioral patterns before they disappear.

Yet this technological approach to knowledge preservation may miss crucial elements. Tacit knowledge includes not just observable patterns but underlying mental models, values, and judgment frameworks that shape behavior. Can these deeper layers be captured through work graphs and behavioral analytics, or do they require different approaches—storytelling, mentorship, communities of practice?

Practical Steps for Leaders

Despite these critiques and caveats, the article provides a valuable framework for leaders navigating AI strategy. Several practical implications emerge:

• Stop treating AI as purely a technology decision. If context drives AI performance, then AI strategy must be intimately connected to operational excellence and process design. Chief Digital Officers and Chief AI Officers need to work closely with operational leaders who understand how work actually unfolds.
• Audit where critical organizational context currently resides. Map the workflows, decision points, and coordination patterns that drive performance in key functions. Identify where context is well-understood versus where it remains tacit and vulnerable to loss.
• Start small with context capture. Rather than attempting to instrument all work across the enterprise, focus on high-value workflows where better context could materially improve outcomes. Sales forecasting, as described in the article, is a natural starting point for many B2B companies.
• Address trust and privacy head-on. Engage employees in designing context-capture approaches that feel supportive rather than surveillant. Be transparent about what data is collected, how it is used, and who has access. Consider whether some contexts should remain informal and unmonitored to preserve psychological safety.
• Build feedback loops between AI performance and context refinement. As the authors recommend, instrument outcomes alongside AI usage. Where AI adds value, understand which contextual signals made the difference. Where it falls short, identify what contextual information was missing or misleading.
• Maintain strategic flexibility. Context should guide AI but not constrain innovation. Build mechanisms for questioning whether established patterns still serve the organization’s goals, particularly as markets evolve and strategies shift.

The Road Ahead

Murty and Kumar have identified an important shift in the locus of competitive advantage. As AI models commoditize, the ability to capture, encode, and operationalize organizational context will increasingly separate high-performing organizations from average ones.

Their argument is strongest when describing the problem: traditional enterprise systems miss crucial behavioral context, and generic AI implementations fail to capture what makes organizations distinctive. The prescription—context engineering as enterprise infrastructure—is conceptually sound but raises significant implementation challenges around technology complexity, organizational culture, privacy, and change management.

The article would benefit from deeper engagement with the tensions inherent in systematic context capture: between learning from the past and adapting to the future, between coordination efficiency and creative experimentation, and between capturing patterns and preserving autonomy.

Nonetheless, the core insight stands: in an age of abundant AI capability, scarcity shifts to understanding. Organizations that deeply understand how they create value, can articulate that understanding as operational context, and can ground AI systems in that context will possess a sustainable advantage over competitors who treat AI as generic productivity infrastructure.

The question is not whether organizational context matters—it clearly does. The question is whether organizations can capture it systematically without destroying the trust, autonomy, and adaptability that make valuable context possible in the first place. That challenge will define AI implementation success over the coming decade.

Leaders who take context seriously, invest in capturing it thoughtfully, and deploy it judiciously will be better positioned to realize AI’s potential. Those who pursue AI strategies untethered from operational reality will continue to see promising pilots that fail to scale. The difference between these outcomes will not be found in the models themselves, but in the invisible layer of context that shapes how work actually gets done.