A divide in the AI market is rarely discussed openly, but it shapes almost every commercial decision.
On one side are organisations that are ready. They have clean data, documented processes, technical teams that can integrate new systems, and leaders who understand what AI can and cannot do. They have allocated budgets, identified use cases, and can absorb new technology without disruption. They are a small minority, and they receive most of the attention.
On the other side are organisations that are not ready. Their data is spread across systems that do not talk to each other. Their processes are documented incompletely, if at all, with critical knowledge held by experienced people. Their technical teams are stretched maintaining what they have. Their leaders know AI matters but are unsure what it means for them. They are the large majority, and they are systematically underserved.
The adoption data shows the divide
Eurostat's figures make the pattern visible. In 2025, 20% of EU enterprises with 10 or more employees used AI, but the spread was wide: 42% in Denmark and below 10% in several member states. Year after year, Eurostat finds large enterprises several times more likely than small ones to use AI. The same divide runs through India, where millions of registered small and medium enterprises sit alongside a handful of large, digitally mature groups.
The commercial logic behind the divide is simple. Selling to ready organisations is faster and easier. They evaluate quickly, decide quickly and deploy smoothly. Selling to unready ones is slower. They need explanation before they can evaluate, foundations before they can deploy, and partnership through the work of change. Most AI providers choose the easier path.
The chasm in the middle
Geoffrey Moore's Crossing the Chasm describes why. Early adopters buy technology for its promise and tolerate rough edges. The early majority, a much larger group, buy only when technology solves a clear problem in their context, with proof from organisations like theirs. Between the two is a chasm many products never cross.
AI is at the edge of that chasm now. The ready minority has adopted. The unready majority is waiting for something that fits how they work, not how a technology company imagines they work.
Why readiness is a capability, not a starting condition
Cohen and Levinthal's research on absorptive capacity explains why the divide persists. An organisation's ability to use new knowledge depends on what it already knows. Organisations with structured data and documented processes can absorb AI. Those without struggle, not because they lack ambition, but because they lack the foundations new capability attaches to.
The usual answer is to tell organisations to get ready first: clean the data, document the processes, hire the team, then come back. It rarely works. The work is large, the benefit is distant, and daily operations always take priority.
The better answer is AI that builds readiness as it runs. A deployment that maps how work is done, structures information as it handles it, and captures the reasoning behind decisions leaves the organisation more ready each month. Readiness becomes a by-product of using AI, not a precondition for it.
What agentic AI means
The distinction matters here. Assistive AI responds to prompts: a person asks, the system answers, and the person stays the actor. Agentic AI receives objectives and carries out the steps to meet them, across systems and over time, within limits people set.
The difference is not merely technical. Asking AI to draft one customer email is assistance. Deploying AI to manage customer communication within agreed rules, escalating when needed and learning from responses, is agency. For an organisation without a large team, that difference is what makes AI useful, not just another tool to manage.
Why the unready majority has the most to gain
The ready minority already has much of what AI would bring: documented processes, structured data, specialist teams. AI improves their operations incrementally. For the unready majority, the change is larger. An organisation whose pricing logic lives with one person, whose customer enquiries arrive by phone and WhatsApp, and whose compliance evidence sits in folders gains far more from a system that brings structure, consistency and continuity.
C.K. Prahalad made a related argument about markets in The Fortune at the Bottom of the Pyramid: the largest opportunities often sit with those that conventional providers overlook, provided the offer is designed for their reality. The same is true of AI.
Designing for organisations as they are
Building for the unready majority changes the design:
Start from the inputs that exist: emails, PDFs, spreadsheets, photos and voice notes, not clean databases.
Work in the channels people use: WhatsApp, email and phone, not a new portal nobody opens.
Begin with oversight: people review every output at first, so trust builds without risk.
Structure as you go: every case handled adds to catalogues, records and decision memory.
Price and scope for patience: results compound over months, and the plan should expect that.
Signs an organisation is ready to start
An organisation does not need clean data to begin. It needs:
a workflow that is visibly strained and consumes skilled time;
someone who owns that workflow and wants it fixed;
willingness to show how the work is done, exceptions included;
patience for results that build over months rather than days.
How Mitochondria's agentic AI systems serve organisations that are not yet ready
The approach. Every deployment begins by mapping the operation as it runs, with the data and processes the organisation has today. Units are configured to work from emails, documents, spreadsheets and voice notes, through the channels people already use, with full human oversight at the start.
What it enables. Organisations get dependable AI on their most strained workflow without first rebuilding their data or hiring a technical team. Each case handled adds to the structure they lacked: catalogues, records and decision memory, held in their own environment.
The impact. AI that fits the organisation as it is, and leaves it more capable, better documented and readier for the next step every month.
Read more about Mitochondria's approach.
Sources
Eurostat, Use of artificial intelligence in enterprises (2025 data)
Moore, G. A. (1991). Crossing the Chasm.
Cohen, W. M. and Levinthal, D. A. (1990). Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35(1).
Prahalad, C. K. (2004). The Fortune at the Bottom of the Pyramid.
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