AI Won't Reward Those Who Use It the Most

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Technology is becoming more accessible, but its benefits are not being shared equally. The best-positioned companies aren't necessarily those running the most artificial intelligence pilots, but those combining hard-to-replicate assets, data that improves with use and an organisation capable of reinventing itself at the speed of AI.

AI won't reward those who use it the most, but those best prepared to capture its value.

Over the past few years, the business conversation around artificial intelligence has focused on adoption: how many use cases have been launched, how many employees are using copilots or how much time can be saved by automating tasks.

But that perspective is starting to fall short.

The decisive question is no longer who uses AI, but who manages to capture the value it creates. And the answer doesn't depend solely on the quality of the models or the technology budget. Above all, it depends on market structure, the assets each company controls and its ability to transform its business.

This is the central argument of the BCG article, “Who Will Win as AI Rewrites Competitive Advantage?”.

AI won't distribute its benefits evenly. It will expand some margins, compress others and redistribute value among companies, customers, employees and infrastructure providers.

Historic Investment Doesn't Guarantee Widespread Returns

The scale of the transformation is difficult to overstate. BCG estimates that capital expenditure by major cloud providers could reach $750 billion in 2026. However, the first major beneficiaries are concentrated in AI's physical infrastructure: chip designers, memory and equipment manufacturers, energy suppliers, power grids and owners of sites with access to available power capacity.

For other businesses, adopting AI creates a new economic equation. Computing capacity becomes a structural cost: a portion of the value generated by every AI-enabled process or transaction flows out of the industry and towards infrastructure and model providers.

That's why improved productivity doesn't automatically translate into higher profitability. In highly competitive markets, cost savings may ultimately be passed on to customers through lower prices. In concentrated or regulated sectors, a greater share may remain within the company and benefit its shareholders.

Figure 1. The five factors that determine who captures the value created by AI. Source: BCG Analysis.

BCG identifies five factors that help predict how this "AI dividend" will be distributed:

1. Task substitutability. The easier an activity is to automate, the greater the pressure on employment and external suppliers, and the greater the potential to transfer value to shareholders or consumers.

2. Market concentration. Companies operating in concentrated industries tend to retain more value. In fragmented markets, competition tends to pass those benefits on to customers.

3. Data defensibility. Proprietary data that is continuously refreshed can strengthen an established company's position. A static historical database is much easier to replicate or replace.

4. Compute intensity. The more expensive it is to run AI, the greater the share of the benefits absorbed by the technology layer.

5. Regulatory friction. Regulation, licensing and privacy requirements can slow the redistribution of value, although they rarely stop it entirely.

The Best-Prepared Companies Have Advantages That AI Amplifies

When the same foundation models are available to almost everyone, access to technology is no longer a sustainable competitive advantage. BCG believes many technical capabilities could reach parity within two or three years. The real competitive moat then shifts towards what lies beneath the model.

The best-positioned companies share one or more of these strengths:

-Flow data: Proprietary information generated continuously, feeding models and improving products with every interaction.

-Deep relationships and trust: Particularly important in regulated, sensitive or high-risk activities, where validation and accountability matter as much as accuracy.

-System-level integration: AI isn't deployed as an isolated tool, but embedded in end-to-end processes that are difficult to separate.

-Network scale: More users or transactions generate better data and better results, attracting even more activity.

-Scarce physical assets: Infrastructure, equipment, locations or networks that require considerable time and capital to replicate.

Figure 2. The depth and durability of different sources of competitive advantage in the age of AI. Source: BCG Analysis.

Being a Mid-Sized Company Could Become a Trap

The analysis carries an uncomfortable warning for many established businesses. Large platforms can rely on scale, capital, data and networks that are difficult to replicate. AI-native companies, meanwhile, start without legacy processes, oversized structures or outdated technology.

Caught in the middle are organisations large enough to bear the costs of legacy systems, but not large enough to benefit from network effects or a virtuous cycle of data. For them, adding an AI assistant to existing processes may improve efficiency, but it won't resolve their structural disadvantage.

In fact, some traditional strengths are becoming less effective as competitive defences:

-Having a large workforce is no longer synonymous with greater capability.

-Specialist knowledge can become a replicable feature.

-Historical data loses value if it isn't updated or used to generate continuous learning.

-Operational excellence based on standard processes may become accessible to anyone using the same tools.

-Regulatory protection remains useful, but it may weaken as regulations change and solutions mature.

A company can retain its customers and revenue while gradually becoming weaker. This is the risk of economic hollowing-out: business volume remains, while margins, pricing power and the most profitable parts of the customer relationship migrate to another player.

The New Competitor Doesn't Always Want Your Market Share

The most dangerous threats may not appear on the traditional competitive landscape. An AI-native company doesn't need to replace an established company's entire business. It only needs to isolate and capture the highest-margin layer.

This pattern requires a change in how we measure competition. Monitoring market share is no longer enough: we need to identify who is capturing the margin at each stage of the value chain.

So, What Does Being AI-Ready Actually Mean?

Being prepared isn't about having a corporate copilot licence or accumulating proofs of concept. A company is ready when it can answer yes to five questions:

1. Do we know which parts of our competitive advantage AI strengthens and which it erodes?

2. Do we have proprietary data that is continuously refreshed through business activity and improves our products or decisions?

3. Are we redesigning entire processes, or simply adding AI to the way we've always worked?

4. Do we know which high-margin part of our business an AI-native competitor would try to separate and capture?

5. Can our organisation take an idea into production quickly, govern it effectively and learn from its use?

Companies that answer "no" to several of these questions may still be able to buy technology, but they're unlikely to be building a competitive advantage.

AI Won't Reward Those Who Use It the Most

The most important conclusion is organisational. The gap between winners and losers won't depend solely on the models they use, but on business architecture, talent, incentives and speed of execution.

Leaders will need to assess their competitive advantages, protect threatened profit pools, test their strategic bets against different cost and regulatory scenarios, and redesign workflows from the ground up. This requires a mindset closer to that of a founder than someone simply managing an existing business.

AI will be accessible to almost everyone. Ultimately, the winners will be the companies capable of turning hard-to-replicate assets into systems that learn, improve and become stronger with every use. The rest risk discovering too late that they were digitising a business model whose value had already shifted elsewhere.