The Next AI Decision Is About Dependency
AI selection is becoming a dependency decision. Most executives still treat it as software procurement.
Enterprise AI adoption is beginning to diverge from consumer adoption, suggesting different buying criteria are emerging.
As AI becomes embedded in workflows, switching costs increase and provider selection becomes a strategic dependency decision.
CEOs should evaluate AI across models, platforms, and operating models rather than focusing solely on model performance.
The AI industry remains obsessed with model performance.
Every major release is evaluated through the same lens. Which model is more intelligent? Which model performs better on reasoning benchmarks? Which model generates better code? Which model achieves the highest score on the latest evaluation suite?
These are useful questions for researchers, developers, and technology teams. They are increasingly less useful for CEOs.
The executive challenge is shifting. The question is no longer simply which model performs best today. The more important question is which AI provider an organisation is prepared to become dependent upon over the next decade.
That distinction may sound semantic. It is not.
Software decisions and dependency decisions are fundamentally different. Software can be replaced. Dependencies become embedded within operating models, governance structures, workflows, and organisational capabilities. Once that occurs, changing providers becomes materially more difficult, regardless of whether a better alternative exists.
Recent developments in the AI market suggest this transition may already be underway.
Enterprise Adoption Is Following A Different Logic
One of the more interesting developments over the past year has been the emergence of different adoption patterns between consumer and enterprise markets.
OpenAI remains the dominant consumer brand. ChatGPT has become synonymous with AI in much the same way that Google became synonymous with search. Consumer awareness, mindshare, and usage remain significant competitive advantages.
Yet enterprise markets have historically followed different rules.
Recent data from Ramp’s AI Index showed Anthropic overtaking OpenAI in business adoption for the first time, with Anthropic reaching 34.4% adoption compared with OpenAI’s 32.3%. Ramp tracks actual spending behaviour across tens of thousands of businesses, making it one of the more useful signals available in the market.
This does not prove Anthropic has built a superior product. Nor does it suggest OpenAI’s position is under immediate threat.
What it does suggest is that enterprise buyers may be optimising for a different set of criteria than consumers.
This would not be unusual. Enterprise technology markets have rarely been won solely through technical superiority. Reliability, governance, integration, vendor maturity, support, security, and long-term viability often become equally important. In many categories, they become more important.
The history of enterprise software is filled with examples where the technically strongest product failed to become the dominant platform.
AI may be entering a similar phase.
Revenue Growth Tells A Different Story
Another useful signal is where revenue growth is emerging.
Anthropic’s reported growth from approximately $1 billion in annualised revenue during early 2025 to more than $30 billion by April 2026 is remarkable. More interesting, however, is the nature of the demand driving that growth.
Much of the value appears to be coming from enterprise use cases. Coding environments, research workflows, legal operations, knowledge management, and internal productivity systems are becoming increasingly important sources of adoption.
These are not casual consumer interactions. They are operational workflows.
That distinction matters because operational workflows create stickiness. Once an organisation redesigns a process around a technology platform, the economics change. The conversation shifts away from feature comparison and toward continuity, governance, and risk management.
Those are the characteristics of infrastructure markets rather than software markets.
The Industry Is Already Behaving Like Infrastructure
The strongest evidence may not come from adoption metrics at all. It may come from how providers themselves are behaving.
OpenAI’s introduction of multi-year Guaranteed Capacity agreements is particularly revealing. The offering allows organisations to reserve long-term AI compute capacity across models and cloud providers, in some cases years in advance.
Capacity reservation is not a typical software construct. It is an infrastructure construct.
Cloud providers sell capacity. Telecommunications providers sell capacity. Utilities sell capacity. Organisations reserve capacity when they expect a service to become operationally critical.
At the same time, AI providers are investing extraordinary amounts into long-term infrastructure commitments. Anthropic’s reported commitment of up to $200 billion with Google is difficult to interpret as anything other than a belief that AI is becoming foundational infrastructure.
The market is gradually moving below the application layer. Models remain important, but increasingly the competitive battle is shifting toward ecosystem control, workflow integration, enterprise governance, and infrastructure scale.
That is typically where long-term winners emerge.
Strategic Implication
The strategic implication is not that organisations should choose Anthropic over OpenAI.
That conclusion would be simplistic and likely wrong.
The more important implication is that executives may be evaluating AI investments through the wrong decision framework.
Most organisations continue to approach AI as a technology procurement exercise. They compare features, benchmark results, and subscription costs. Those considerations matter, but they are not where the largest strategic risks reside.
The bigger question is how AI dependencies are forming across the enterprise.
A Three-Layer Framework For AI Strategy
I increasingly think AI decisions need to be separated into three distinct layers.
The mistake many organisations make is treating all three layers as though they are the same decision.
Layer One: Models
This is where most executive discussions currently focus. Organisations debate GPT versus Claude, open source versus proprietary models, and performance differences across various tasks.
These decisions are important, but they are also becoming increasingly reversible. Model quality continues to improve across the market, and the gap between leading providers is narrowing.
Most organisations are allocating excessive attention to the layer where switching costs remain lowest.
Layer Two: Platforms
The second layer is significantly more strategic.
Platforms determine how AI interacts with enterprise systems, data assets, workflows, employees, and governance structures. This includes identity management, orchestration layers, agent frameworks, security controls, and integration architecture.
Unlike models, platforms become embedded.
Changing a model may take weeks. Replacing an enterprise AI platform may take years.
This is where dependency risk begins to emerge.
Layer Three: Operating Models
The third layer receives the least attention and may ultimately prove the most important.
AI is not simply changing how work is executed. It is changing how organisations are designed.
Management structures, workforce composition, decision-making processes, customer engagement models, and product development cycles are all beginning to evolve. These are not technology decisions. They are business model decisions.
Boards and executive teams should spend far more time discussing this layer than they currently do.
Three Questions Every CEO Should Ask
The next phase of AI strategy requires a different set of questions.
First, where are we becoming dependent on AI?
Leaders should map the workflows, processes, and decisions that increasingly rely on AI systems. Dependencies create both strategic advantage and strategic risk. Most organisations currently understand the former better than the latter.
Second, which dependencies are reversible?
Not every investment creates lock-in. Understanding which decisions preserve optionality and which create long-term dependence is becoming increasingly important. Switching costs are often invisible until they become operationally painful.
Third, what capabilities must we own?
Every organisation has a small number of capabilities that underpin competitive advantage. The objective is not to own everything. The objective is to ensure that strategically important knowledge, workflows, and decision-making capabilities do not become outsourced by default.
This is particularly important as AI agents become more deeply embedded within enterprise processes. The convenience of external intelligence can easily obscure the strategic value of retaining internal capability.
The Emerging Divide
Over the next five years, organisations are likely to separate into two groups.
One group will continue treating AI as another software category. Their focus will remain on features, pricing, and vendor selection. They will optimise for procurement efficiency and internal productivity.
The other group will recognise that AI is becoming a foundational dependency within the enterprise. Their focus will shift toward strategic control, organisational capability, and long-term optionality. They will treat AI decisions in much the same way previous generations treated cloud strategy, ERP standardisation, or cybersecurity architecture.
However, the most sophisticated organisations will take the thinking one step further.
They will not only ask where they are becoming dependent on AI. They will ask how AI can make them more valuable, more embedded, and more difficult to replace within their own ecosystems.
History suggests the greatest value rarely accrues to those who simply consume infrastructure. It accrues to those who build on top of it.
The organisations that recognise this shift early will not necessarily choose better models.
They are more likely to make better dependency decisions and use AI to become more difficult to displace.