The Missing Semantic Shift

Most AI conversations revolve around AI as a productivity booster—a tool that helps us do more of what we’ve always done, faster. This perspective misses the actual value system restructuring and implications for organizations and markets.

We’re experiencing a profound shift in value creation and capture. If AI can automate some existing processes, it also forces us to engage with clarity and intentionality to transform outdated work practices and market inefficiencies.

In this essay, I’ll show why properly describing, defining, packaging capabilities and operationalizing work is emerging as the new competitive battlefield that will determine which organizations thrive and which become obsolete in the evolving landscape.

AI Adoption Exposes Our Superficiality in Organizational-Product-Market Modeling

We’ve accumulated massive semantic debt across our organizations—ambiguous language, inconsistent frameworks, and conceptual gaps. Many of the organizations I collaborate with—particularly the larger ones—appear confused when asked about their customer segments and the problems their products address. Even scaleups struggle; when questioned about how their capabilities are structured and integrated to create value, their best answer often amounts to an outdated organizational chart presented in a PowerPoint slide.

Humans can intuitively navigate this and accept this sloppiness—sometimes just “live with that.” However, that breaks AI systems in significant ways: they hallucinate and, therefore, require much more clarity. This debt has always existed, but generative AI is exposing it mercilessly.

We’re building AI tools as extensions of our cognitive and executive capacities requiring stable ontological foundations. They enable and require the explicitation of the implicit. We’ve seen this principle at work with the rise of the Model-Context-Protocol (MCP), where generative AI performs well when guided by a coherent ontological context that describes workflows, tools and interactions.

As always with AI, things are moving fast. A few days ago, I watched how serial entrepreneur Hayden Miyamoto uses his “Master Prompt Method,” as presented in a video with Tiago Forte. It creates a comprehensive “context layer” that encodes the organization’s entire operational model—from strategic vision to tactical execution—into a structured prompt framework that supports the execution of Standard Operating Procedures. These master prompts act as executable knowledge systems for team members to invoke for consistent, high-quality outputs.

It’s worth explaining here that what’s remarkable isn’t merely the consistency achieved (though that alone is valuable). The method transforms organizational knowledge from static documentation into dynamic, executable capabilities – essentially embodied knowledge! It makes middle management roles largely obsolete and creates significant leverage for companies.

Post-Industrial Dynamics: Returns on Composability beats Returns on Scale

When a 5-person team with a well-designed prompt system can match the coordination capacity of a 500-person division, traditional economies of scale become less relevant. Are we witnessing the swift dissolution of industrial-era organizational logic?

In the post-AI landscape, once adoption is widespread, organizations can gain an advantage not from scale but from the speed at which they create and recombine options, and reconfigure capabilities to respond to new opportunities.

From an investor perspective, this changes the fundamental definition of value. Companies will be valued not by their headcount or revenue per employee, but by their semantic coherence and composability quotient — how effectively they’ve thought out and structured their capabilities to be recombined. Note that such recombination will increasingly involve AI agents. More often the players interacting with your organization’s services and products will be AI agents.

Check yesterday’s release of Google Project Mariner: how long before this enters the enterprise and starts to be used to select and negotiate supplier contracts instead of planning a dinner or holiday?

Last week, in a podcast conversation (upcoming), Sangeet Paul Choudary shared with us a historical analogy about AI today:

“Much like the rise of the shipping container, you could have misunderstood it as an automation story. Ports are getting automated, dock workers are losing jobs. But the real value was in moving from an old system of trade to a new system of trade by solving coordination problems.”

This perspective captures what’s happening now. We’re not merely witnessing task automation but a need for a fundamentally new system of work. This new system will be one where value derives from thoughtful coordination of modularized resources and capabilities rather than solely from production or distribution.

Just as the shipping container unbundled trade and shipping and enabled global component-level competition (giving rise to companies like Intel instead of vertically integrated IBM), AI is unbundling the economy’s capabilities, creating new competitive landscapes we can’t imagine today.

But when most of the economy is programmable, what do we program it for? Intention and the ability to define what’s valuable for us and our customers will be crucial. There won’t be space for shallowness.

Rethinking Value Creation Units and Exposing them to the Market

As AI reshapes organizational practices, we need to revisit our fundamental theory of value. The atomic unit of value becomes the capability module—a holistic integration of an offering with the underlying organizational capabilities.

This represents a profound shift: value no longer resides merely in what is delivered to a given customer but rather in the unified system of automated delivery, in the embedded organizational knowledge, in a solid capability based profit and loss, and in the interfaces that make the capabilities accessible to the market. Products and services remain central, but their value is inseparable from the productive capabilities that bring them to life.

In the same conversation we had last week for our podcast, Sangeet Choudary articulated a multi-dimensional framework for understanding value that clarifies the transition. He distinguished between “intrinsic value” (what has value in itself), “economic value” (what the market rewards under conditions of scarcity and relevance), and “contextual value” (the importance of a capability within a specific system based on that system’s constraints).

As AI reshapes organizational boundaries, this distinction becomes critical. Capabilities that once commanded economic value due to scarcity (pure economic value) may suddenly see that value collapse when AI makes them abundant. Meanwhile, capabilities delivering intrinsic operational value may see their worth surge within the multiplicity of fragmented contexts that the new system lets emerge. A well-defined modular capability that integrates delivery and organizational knowledge maintains relevance across changing contexts by adapting to shifting system constraints. It’s valuable because it’s actionable, unequivocal, and composable, without needing larger systems of premises or dependencies to be seen as valuable.

These modularizations will enable multidimensional pricing beyond monetary exchange. A capability might be priced differently—in different contexts—according to its carbon footprint, latency, or cultural profiles (diversity, equity,…). This will create richer markets that account for varying priorities in pricing externalities.

As everything becomes programmable with clear inputs, outputs, and accountabilities, AI agents can evaluate these multidimensional attributes with unprecedented ease and precision. Such agents will assess, compare, and optimize dozens of performance indicators that human decision-makers would struggle to process. The ability of such autonomous systems to select, capture, package, and reuse modular capabilities dynamically to match specific contextual requirements, will significantly multiply the theories of value.

The implications for competition are profound and potentially ruthless. As AI-enabled capability modules become the dominant organizational paradigm, we face a competitive landscape that will systematically eliminate inefficiencies from economic systems with remarkable speed and precision. Inefficient organizational designs, suboptimal resource allocations, and information asymmetries—tolerated for decades in traditional markets—will become competitive liabilities almost overnight.

The resulting competitive pressure cooker will force organizations to evolve toward radical efficiency or face extinction. Organizations maintaining legacy coordination structures while competitors leverage AI-powered operations based on modular capabilities will experience what we might call “ontological arbitrage“, a phenomenon where organizations with superior clarity—where concepts, processes, and capabilities are clearly defined and interconnected—exploit the gaps and inconsistencies in less coherent competitors.

When a company achieves greater ontological coherence, it can make decisions and reconfigure resources at speeds that confused organizations cannot match. This creates extractable value from competitors’ confusion, much like traditional arbitrage extracts value from price discrepancies.

Crucially, this clarity can’t remain purely theoretical or confined to strategy documents. Competitive advantage emerges when such semantic clarity translates directly into operational structures. Each capability must be well-defined, consistently delivered, and programmatically accessible. This operational clarity—transforming abstract understanding into concrete, modular capabilities with standardized interfaces—becomes the practical mechanism through which semantic advantages convert to market dominance.

For example, a company with precise customer segment definitions can quickly reconfigure its offerings when market conditions change, while competitors with fuzzy segmentation frameworks waste weeks debating what the changes mean for their business. The ontologically coherent organization doesn’t just move faster—it harvests value from its rivals’ delay and confusion. The result is economic natural selection operating over quarters, not decades.

For companies navigating this transition, the challenge is identifying which capabilities to modularize and how to structure them for maximum reuse and recombination. On the other hand, the challenge is figuring out what makes them unique, which, at least in the short term, can be integrated and coordinated with other resources to extract value from specific market contexts.

What can you do about it?

If your organization relies on outdated, misaligned, and scattered PowerPoint presentations to represent its capabilities, organizational structures, and products, you’re in trouble.

If you can’t understand your customers, operational costs, value, and metrics that validate performance and align them with products and services (see Diagnosing a Product Portfolio with Brian Balfour’s Four Fits and the Portfolio Map Canvas), you’re in trouble.

If you don’t ensure modular services are structured, standardized, have programmable interfaces, and clear operating procedures, you’re in trouble.

If you don’t maintain a coherent representation of your organization’s purpose, you risk lagging behind competitors with superior ontological clarity and intentionality.

It’s time for your organization to invest in adaptive information architectures and build a practice of competitive agility and entrepreneurial autonomy. You need to build operational flexibility through deliberate modularization, allowing rapid reaggregation of capabilities as markets evolve ever faster.

But what does this transformation look like in practice? To illustrate how organizations can implement these concepts, let’s explore a fictional case study – based on our real customer experiences – that demonstrates the journey toward ontological clarity and modular capability organization. While fictional, this example draws from real patterns we’ve observed across multiple industries as they respond to the transformational pressures of AI-driven markets. Check it out here: AvantComposite and Customer-Centric Ontological Transformation.

At Boundaryless, we help organizations understand themselves deeply and become more intentional about what they build and how they open market interfaces.

This process typically involves mapping with the Portfolio Map, adopting an entrepreneurial platform organization topology (with the 3EO) – because you need widespread and distributed execution, not centralized but coherent and harmonized – and then adopting rigorous approaches to product-platform development and growth hacking with the Platform Design Toolkit.

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