The business world is grappling with an AI paradox: Adoption of generative and agentic AI is growing, investment is accelerating, but sustained impact on performance is elusive. This dynamic echoes the “Solow Paradox,” inspired by economist Robert Solow’s quip that “you can see the computer age everywhere but in the productivity statistics.”1 As of the end of 2025, almost nine out of ten companies had deployed AI in at least one business function, a recent survey shows, but 94 percent of respondents report not seeing “significant” value from those investments.2 And while many executives view AI as essential to their companies’ future competitiveness, their focus to date has been largely on pilots seeking incremental productivity improvement rather than deeper transformations.
This disconnect suggests a misunderstanding of how AI creates and redistributes economic value. Productivity improvement is unlikely to expand profit pools or provide companies with a durable advantage because competition tends to erode productivity gains, benefiting customers more than the companies that implement them. Real value from AI will come from reshaping offerings, business models, and market structures in ways that expand or reallocate profit pools. What matters most is not how AI will change your industry but how it will change the economics of competition.
The path of past revolutionary technologies such as railroads, electricity, and computers points to how that disruption may play out. General-purpose technologies rarely create value in a single wave. Initial productivity improvements enhance efficiency, but true economic impact comes later—when new products emerge, business models change, and value chains are reconfigured—often redistributing value across industry players rather than uniformly increasing it. For example, when electricity first arrived in factories, many businesses simply replaced the steam engine with an electric motor, capturing efficiency gains but leaving the line-shaft layout unchanged. The breakthrough came later, when small motors enabled managers to rearrange machines around workflows and ultimately when companies redesigned their factories around electricity, creating new operating models.
The questions business leaders should ask are: Through which mechanisms and at what pace will AI create, expand, or shift profit pools? And what does that mean for our strategy? In this article, we explore three overlapping waves of change: productivity gains, differentiation, and reduction of transaction costs. Productivity gains will matter, but they are unlikely to define the winners. Executives need to look beyond efficiency to product and service innovation, and ultimately to how AI will reshape industry structures and redistribute profits.
Productivity gains: Rapidly becoming table stakes
AI’s most immediate impact has been on productivity. By automating tasks and streamlining workflows, AI tools can materially improve speed, accuracy, and quality while lowering cost.
Most current applications of AI are tools that accelerate existing work. JPMorgan Chase, for example, uses AI to scan transactions for signs of fraud in real time,3 while BMW uses computer vision (a form of AI that replicates human sight) to automate quality inspections on production lines.4 These applications reduce manual effort and improve consistency, but they largely preserve underlying workflows.
Larger gains emerge when AI is embedded across entire processes. At Siemens, AI helps to coordinate predictive maintenance and production planning and flow to reduce operational variance and downtime.5 Rather than optimizing isolated tasks, the company uses AI to improve system-level performance.
At the leading edge of productivity improvement, AI is beginning to orchestrate execution dynamically and at scale. Amazon’s fulfillment centers use AI to manage robot fleets, routing, inventory placement, and capacity decisions.6 Airlines and hotels are integrating demand forecasting, pricing, crew scheduling, and asset utilization to compress cycle times and improve load factors. In these environments, AI does not simply support decisions—it continuously coordinates complex systems, with humans increasingly focused on oversight and exception management.
While productivity improvements are valuable, they rarely increase profit pools—they typically affect the floor of industry performance, not the ceiling. Productivity’s strategic role is mainly defensive and facilitative: It resets cost, speed, and quality expectations, and frees up resources to invest elsewhere. The main exception in the case of AI is when productivity gains change unit economics from variable to fixed costs on a large scale. In such settings, lower costs enable greater production volume, which in turn further reduces unit costs, creating a self-reinforcing advantage.
To capitalize on this dynamic, companies need a head start. Early movers can scale faster, lock in lower cost positions, and make it harder for competitors to catch up once the benefits begin to compound. Even when this pattern plays out on a modest scale, companies that capture productivity gains ahead of their competitors can temporarily expand margins, reinvest the gains, and reset the industry cost baseline in their favor. These advantages erode as adoption spreads, but they can be vital—particularly in industries in which cost position shapes pricing power and investment capacity.
Differentiation: Fueling growth
Product, service, and business-model innovation is where AI moves beyond improving how work is done to opening the door to new growth. As with earlier transformative technologies, the greatest value will not come from AI alone but from complementary innovations: redesigned processes, new products and business models, and new ecosystem structures built around the core technology.
Organizations capturing outsized value from AI-enabled innovation tend to build a few reinforcing advantages that compound and provide the foundation for new competitive moats. As AI capabilities spread, advantage shifts away from fixed product features, cost position, or brand and toward AI-enabled strengths that deepen with use: proprietary data that improves performance over time; customer benefits, created by network effects or by embedding AI directly into customer workflows, that reduce incentives to switch; and faster learning and iteration cycles.
To come back to our factory example, the introduction of electricity improved efficiency, but it wasn’t until distributed electric motors allowed factories to be reorganized around workflows rather than proximity to power sources that assembly lines, mass production, and new industrial supply chains became possible. Similarly, electrification enabled refrigeration, which reshaped food retail and global supply chains, and powered urban infrastructure, which transformed cities. Electricity was essential, but the biggest expansion of profit pools emerged from complementary innovations that reconfigured industries around the new form of energy.
AI appears to be following a similar trajectory. Foundational models (including large language models) are powerful enablers. However, lasting differentiation will not stem from access to these technologies—which will be increasingly commoditized—but from the way organizations use them to redesign offerings, revamp business economics, and reconfigure ecosystems.
This progression tends to unfold in stages. The first stage improves customer experience within existing industry structures. Consider ride-hailing services. Traditional taxi networks allocated supply largely through physical queuing—the next available driver in a taxi line. Companies like Uber and Lyft allocate rides algorithmically to the available driver closest to the customer, reducing wait times, increasing transparency, and improving asset utilization. The underlying asset—a car with a driver—remains unchanged; what shifted was the coordination method and the customer experience. AI now extends this logic further: Allocation, pricing, routing, and incentives can be optimized in real time, continuously improving both the user experience and system economics.
The second stage expands the product frontier. Platforms built around some ride-hailing services have extended the model into adjacent segments such as food delivery, where the same coordination logic orchestrates interactions among restaurants, couriers, and customers, creating new on-demand consumption patterns. The next frontier may be AI-powered driverless taxis, which, by removing the human driver, will fundamentally change the cost structure and scalability of the service and potentially transform the economics of urban mobility.
More broadly, AI enables offerings that were previously infeasible. In financial services, American Express uses AI to tailor offers and recommendations in near real time based on transaction behavior and risk signals.7 In life sciences, AI-designed molecules allow pharmaceutical companies to accelerate drug discovery.8 In these cases, AI does not merely improve delivery—it changes what can be offered.
The third and most consequential stage—and the biggest catalyst of profit pool expansion—is business model reinvention. Here, AI reshapes the economics of competition itself. New AI-native businesses illustrate this shift. Harvey, for example, is redefining legal services by combining gen AI with proprietary legal workflows and data to deliver offerings that traditionally required teams of lawyers, blurring the line between software and services. Similarly, Khanmigo is reimagining education through AI-powered tutoring that provides personalized, always-on instruction, challenging the economics of classroom-based and one-to-one teaching models. In both cases, the competitive advantage lies in embedding AI into the core offering in a way that changes the cost structure, expands access, and creates new value.
Amazon Prime is another example of complementary innovation creating structural differentiation. The service shifted competition from transaction-level to relationship-level economics. Its subscription model increased purchase frequency and predictability of demand, while AI-driven personalization and logistics optimization improved customer experience. Expanding Prime offerings to include shopping, streaming, and storage deepened engagement and increased switching costs. Over time, behavioral data, ecosystem breadth, and multiple customer touchpoints reinforced one another. The algorithm mattered, but the enduring advantage came from redesigning the business model around data, ecosystem integration, and lifetime customer value.9
Unlike productivity, product, service, and business-model innovation can expand profit pools—but only when these innovations are protected by strong competitive moats. Advantage accrues disproportionately to organizations that move early, learn faster than peers, and build capabilities that compound even as AI-assisted practices become industry norms.
Transaction cost reduction: Driving deep systemic shifts
The last wave of change is the most impactful—and the least intuitive. AI promises to not only change products or operations but also market structures by radically reducing transaction costs.
Many industries are built on friction. Customers struggle to compare options, coordination across multiple providers is complex, switching is costly, and intermediaries thrive by managing information asymmetry or operational complexity. AI (particularly agentic systems) undermines these foundations by making information transparent, coordination automated, and decision-making near instantaneous. As a result, value shifts away from friction management and concentrates instead around new technological or strategic control points such as ownership of the customer interface or data, privileged access to demand, or the ability to orchestrate complex ecosystems. Which control point provides the strongest leverage will vary by industry, but the underlying pattern is consistent: As transaction costs fall, advantage migrates to companies that occupy the critical positions in a lower-friction value chain (see sidebar, “The Coasean singularity: Why transaction costs shape markets and how AI agents could redraw them”).
Two scenarios illustrate how this systemic disruption could happen. In the energy industry, AI agents could connect customers directly to energy providers, automatically optimizing price and usage, and reducing the traditional role of retail intermediaries or bypassing them entirely. In banking, AI agents could boost individuals’ savings by reallocating deposits or switching providers to optimize rates, overcoming the inertia that keeps customers from changing banks and putting financial institutions’ core value pools under pressure. According to a McKinsey analysis, a third of the $70 trillion in global consumer deposits sits in near-zero-yield checking accounts, and if 5 to 10 percent of that amount were reallocated to the highest rates available, banks’ deposit profits could fall by 20 percent or more, reshaping retail banking economics. To be sure, regulations, lack of consumer trust, and institutional inertia may slow the pace and shape the pathway of these shifts, but since they will produce simpler choices and better outcomes for customers, the direction of travel is likely to hold.
As AI becomes embedded in customer-facing interactions and eliminates friction, it has the potential to reshape markets in three fundamental ways. First, it may change how customers discover and choose providers. That’s because as AI agents translate intent and preference into curated recommendations (as in the case of Amazon’s AI-powered assistant Rufus10), they shift competition away from visibility and marketing investment and toward relevance and positioning within AI-powered ecosystems. Unlike human decision-making, which is episodic and constrained by limited attention, agents continuously search, evaluate, and refine choices. Discovery becomes persistent rather than occasional, and competition increasingly hinges on algorithmic ranking and structured product data rather than brand recall. In such environments, value moves to those that consistently shape or influence the rankings. As agents learn from interactions and improve, small advantages in relevance or data quality may compound, directing a disproportionate share of demand toward a limited set of providers and making those positions difficult to displace.
Second, the inclusion of AI in purchase decisions sharply reduces transaction and switching costs. By automating comparison, onboarding, and migration, AI agents weaken advantages that had stemmed from customer inertia. This is already visible in startups such as Taupia, which enable small businesses and households to seamlessly switch service providers to ones offering better deals.
Third, AI reconfigures intermediation as customer interfaces move from websites, call centers, and brokers toward conversational agents and platforms. As a result, some legacy intermediaries are likely to see their roles reduced. OpenAI’s partnership with Klarna, for example, aims to enable conversational product discovery with built-in price comparison, eliminating the need for traditional comparison websites and affiliate-driven platforms.
Given these three shifts, incumbents may need to rethink where and how they compete as friction steadily diminishes across value chains. Some will partner with AI platforms to remain visible as discovery shifts away from traditional channels. Others will specialize further—for example, in offerings where trust, judgment, and human accountability remain decisive, with AI augmenting rather than replacing expertise. In healthcare, for instance, AI can support diagnosis while clinicians provide empathetic patient care and remain accountable for treatment decisions. Still others will build or join ecosystems that deliver seamless end-to-end solutions, simplifying customer experience and creating a disincentive to switch.
Each of these pathways is less about achieving a technological advantage than securing a position that enables continued profitability as market structure shifts. Organizations that merely adapt to changes under way may preserve profitability, at least for a time. Those that identify where AI can fundamentally reshape the customer experience and invest in delivering that experience in distinctive ways can capture a disproportionate share of value.
Systemic disruption is where AI’s impact is most uneven—and most decisive. As transaction costs fall, profit pools are not simply expanded but reallocated, with value shifting toward new control points in discovery, coordination, and customer relationships. Traditionally attractive markets may shrink or disappear entirely as barriers to entry crumble, supplier–intermediary–buyer power shifts, and substitution becomes easier. Incumbents that continue to rely on legacy sources of competitive advantage risk being squeezed into narrower, more commoditized roles. Those that act early to position themselves where value is likely to concentrate can redefine their roles before new, lower-friction industry structures take hold.
Competing in an economy reshaped by AI: Implications for leaders
The AI disruption is fundamentally changing what it means to achieve and maintain a competitive advantage. To lead this transition, CEOs need to move beyond pilots focused on productivity and seek a deeper understanding of how AI can change the economics of the business. Leaders can start with the following four steps:
- Assess the impact of AI on profit pools. Leaders should develop a detailed view of how AI will reshape industry economics—where value is created, lost, or shifted. They should also assess the impact of AI-powered productivity improvements and, more fundamentally, of innovation and lower transaction costs on revenue, costs, and margins.
- Build or strengthen AI-powered competitive moats. As AI adoption spreads, differentiation will stem from how companies build and combine hard-to-replicate competitive moats. Leaders should evaluate which sources of advantage can endure in an AI-driven environment, from proprietary data to network effects to structural cost advantages. The priority is to develop a reinforcing system of moats tailored to your position and deepening them before competitors do or before advantage solidifies elsewhere.
- Turn speed into a structural advantage. The ability to test, learn, and scale faster than competitors is a primary source of advantage in an AI-shaped economy. As experimentation cycles compress and the cost of iteration falls, organizations that move quickly improve at a higher rate. Faster experimentation generates more data; more data improves models and decision quality; better performance attracts more users and activity. Over time, this compounding dynamic widens the gap between leaders and laggards. CEOs should therefore invest in increasing organizational velocity—removing bottlenecks, accelerating deployment, and enabling continuous learning—recognizing that advantage accrues not just from what you build but how fast you improve.
- Rewire the business. Capturing the AI opportunity requires a fundamental rewiring of business units and functions around a scalable AI backbone. This includes redesigning end-to-end processes, modernizing data and technology infrastructure, embedding AI into decision-making and operations, and reshaping roles, governance, and ways of working. Leading organizations are not just experimenting with AI; they are rebuilding how the business runs so that AI can scale across functions and continuously improve performance.
In time, AI will affect every industry, but it will not create value in the same way everywhere—or for everyone. The companies best positioned for this disruption will treat AI as a strategic inflection point. They will use productivity gains to stay in the game, innovation to expand and defend profit pools, and early, deliberate choices to shape emerging market structures and their role within them.
It’s an uncomfortable truth for executives: AI is not a productivity revolution—it’s a competitive reset. History is littered with examples of companies that mistook efficiency for advantage. They optimized while others reinvented. They cut costs while others captured dominant market share—and the profit pools that came with it. When the dust settled, the winners were not those who adopted the technology fastest but those who understood earliest where value was moving—and positioned themselves to capture it.
AI will not create value evenly. In many cases, it will simply redistribute it. The window to act is narrower than it may seem. In an AI-driven economy, advantage compounds early—and value capture locks in fast.
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