Building AI Infrastructure for the Organisations That Need It Most
There is a segmentation in the AI market that is rarely discussed openly but shapes nearly every commercial decision in the space.
On one side are the organisations that are ready. They have clean data, documented processes, technical teams capable of integration, and leadership that understands what AI can and cannot do. They have budgets allocated, use cases identified, and the organisational maturity to absorb new technology without disruption. These organisations are perhaps 5% of the market, yet they receive approximately 95% of the attention.
On the other side are the organisations that are not ready. Their data is fragmented across systems that do not communicate with each other. Their processes are documented incompletely, if at all, with critical knowledge living in the heads of experienced employees. Their technical capabilities are limited, often stretched thin, maintaining existing systems. Their leadership knows AI matters, but is uncertain what it means for their specific context. These organisations represent the vast majority of the market, and they are systematically underserved.
The commercial logic is straightforward. Selling to the ready segment is faster, easier, and more immediately profitable. These organisations can evaluate sophisticated offerings, make rapid decisions, and implement without extensive hand-holding. The sales cycle is shorter. The deployment is smoother. The case studies are cleaner.
Selling to the unready segment is slower, harder, and requires patience. These organisations need education before they can evaluate. They need infrastructure built before they can deploy. They need a partnership sustained through the messy work of organisational change. The sales cycle is longer. The deployment requires more support. The results take longer to materialise.
Most AI companies, understandably, choose the easier path. They skim the ready segment, capturing the organisations that can adopt quickly, and leave the majority to figure things out on their own or wait until they somehow become ready through means unspecified.
We have chosen differently. We build for the organisations that are not ready, because we believe that is where lasting value is created and where the most significant transformations occur.
Understanding What Agentic AI Actually Means
Before exploring why this choice matters, it is necessary to understand what we mean by agentic AI, because the term has become sufficiently fashionable to have lost precision.
The distinction that matters is between AI that assists and AI that acts.
Assistive AI responds to prompts. A user asks a question; the system provides an answer. A user requests content; the system generates it. A user seeks analysis; the system delivers findings. The human remains the actor. The AI is a sophisticated tool that enhances what humans can accomplish, but the human initiates, direct, and conclude every interaction.
Agentic AI operates differently. It receives objectives rather than prompts. It determines what actions are required to achieve those objectives. It executes those actions, often across multiple systems and over extended timeframes. It monitors results and adjusts its approach based on what it observes. The human defines goals and constraints; the AI figures out how to accomplish them.
The difference is not merely technical. It is a fundamental shift in how work gets organised.
Consider the difference between asking an AI to draft a customer email and deploying an AI to manage customer communication. The first is assistance: you prompt, it drafts, you review and send. The second is agency: you define what customer communication should accomplish, what constraints apply, what escalation paths exist, and the system handles communication across thousands of customers, adapting to each situation, learning from responses, and escalating when appropriate.
Agentic AI is designed to undertake comprehensive workflows and exercise judgement within defined boundaries. It does not merely execute predefined tasks according to rigid rules. It navigates complexity, handles exceptions, and makes contextual decisions that previously required human attention. A marketing programme, a customer support operation, a procurement workflow, a compliance monitoring function: these are the domains of agentic AI, not because they are simple enough to automate but because they are complex enough to require intelligence that can adapt.
This distinction matters because it determines what organisations need to be ready for. Assistive AI can be adopted incrementally by individuals. Agentic AI requires organisational readiness: defined workflows, clear governance, integrated systems, and leadership prepared for a different relationship between human and artificial intelligence.
Why Most Organisations Are Not Ready
The 5%who are ready share common characteristics that the 95% lack.
They have a data infrastructure. Information about customers, products, transactions, and operations exists in systems that can be accessed programmatically. The data is reasonably clean, consistently structured, and current. An AI system can query this infrastructure and receive reliable information on which to base an action.
Most organisations do not have this. Their data exists in silos: a CRM that does not talk to the ERP, a product database maintained in spreadsheets, customer information scattered across email threads and individual knowledge. The data that does exist is often inconsistent, outdated, or incomplete. Before an AI system can act on this data, significant work is required to consolidate, clean, and structure it.
They have process clarity. The workflows that AI will engage with are understood, documented, and stable. The decision logic that governs how situations should be handled has been articulated. Exceptions have been catalogued, and handling procedures have been defined.
Most organisations do not have this. Their processes evolved organically over the years, adapting to circumstances in ways that were never systematically documented. Critical knowledge about how things actually work lives in the heads of experienced employees who may not even be conscious of what they know. Asking these organisations what their process is yields the documented ideal, not the operational reality.
They have technical capability. Teams exist who can integrate new systems with existing infrastructure, maintain those integrations as systems evolve, and troubleshoot when things go wrong. APIs can be connected. Data flows can be established. Technical debt does not block every new initiative.
Most organisations do not have this. Their technical teams, if they exist, are consumed by maintaining current operations. Integration expertise is limited. Every new system creates an additional burden on already-stretched resources. The prospect of connecting an AI agent to multiple internal systems is daunting rather than routine.
They have organisational readiness. Leadership understands that AI deployment is not a technology project but an organisational change initiative. They are prepared for workflows to shift, for roles to evolve, and for new capabilities to require new ways of working. They can make decisions about governance, about human-AI boundaries, and about acceptable risk.
Most organisations do not have this. Their leadership knows AI is important, but is uncertain what it means practically. They hope for transformation but have not prepared for the change that transformation requires. Decisions about governance and boundaries have not been made because the questions have not been clearly posed.
This is the reality of the 95%. They are not ready, not because they are incapable, but because readiness requires infrastructure, clarity, capability, and preparation that takes time and effort to develop.
The Choice Most AI Companies Make
Faced with this segmentation, most AI companies make a rational commercial choice: focus on the ready segment.
The products they build assume readiness. They require clean data, clear processes, technical integration capability, and organisational clarity. They offer powerful capabilities for organisations that can use them. They do not address the gap between where most organisations are and where they would need to be.
The sales approach follows. Qualify quickly for readiness. Disengage from prospects who would require extensive preparation. Focus resources on organisations that can buy and implement now.
This approach is commercially efficient in the short term. It captures the organisations that can move quickly, generate revenue and case studies, and avoid the slow, difficult work of building readiness in organisations that lack it.
But it also means that the vast majority of organisations, the ones that might benefit most from transformation, are left behind. They are told, implicitly, to become ready on their own and come back when they have figured it out. The gap between their current state and readiness is their problem to solve.
Some will solve it. Most will not. They will continue operating as they have, watching the ready segment pull ahead, knowing that something important is happening, but unable to participate because no one will meet them where they are.
Building for the Unready
We have made a different choice. We build for the organisations that are not ready, because we believe that is where the most significant transformations occur and where the most durable value is created.
This choice has implications for everything we do.
Our engagement model begins with operational mapping, not product demonstration. Before discussing what AI can do, we invest in understanding where the organisation actually is. What does their data infrastructure look like? Where is information fragmented, and what would consolidation require? What are their processes in practice, not in documentation? Where does critical knowledge live, and how might it be made explicit? What technical capabilities exist, and what gaps would integration require? What is leadership prepared for, and what education might be needed?
This mapping is not a sales qualification step to identify whether the organisation is worth pursuing. It is the beginning of the work. It surfaces what needs to be built before AI can be deployed effectively.
Our implementation approach includes building the infrastructure that readiness requires. If data needs to be consolidated, we help consolidate it. If processes need to be documented, we help document them. If integration capabilities are limited, we build integrations that do not assume capabilities the organisation lacks. If leadership needs education about governance and boundaries, we provide that education.
This is slower than selling to the ready segment. It requires patience and investment before deployment can occur. But it creates something that quick sales to ready organisations do not: infrastructure that transforms the organisation's capability permanently.
Our systems are designed for contexts where readiness is partial. They do not assume clean data; they can operate with data that is messy and improve as data quality improves. They do not assume documented processes; they can learn processes through observation and interaction, gradually building the explicit understanding that did not exist before. They do not assume sophisticated integration; they can work with simple interfaces and expand as technical capability grows.
This is architecture for the unready. Systems that can begin operating before full readiness exists and that build readiness as a byproduct of operation.
The Forcing Function of Deployment
There is something we have learned through experience that shapes our approach: deployment itself is a forcing function for readiness.
Organisations that wait until they are ready before deploying AI often wait indefinitely. The work of building data infrastructure, documenting processes, and developing integration capability is not urgent until something demands it. Other priorities consume attention. Readiness recedes as a goal that will be addressed eventually, but never now.
Deployment changes this dynamic. When an AI system needs to access customer information, the fragmentation of that information becomes a problem that must be solved. When a process must be defined for the system to operate, the documentation that was perpetually deferred becomes immediately necessary. When governance questions must be answered for the system to know its boundaries, leadership must engage with questions they had avoided.
The forcing function is not comfortable. It surfaces gaps that organisations might have preferred to ignore. It creates urgency that disrupts the comfortable pace of perpetual preparation. But it also creates progress that would not otherwise occur.
We design our deployments to harness this forcing function constructively. We begin with use cases that are tractable, where the infrastructure gaps can be addressed without overwhelming the organisation. We build momentum through early successes that demonstrate value while building capability. We expand the scope as the organisation's readiness grows, tackling more complex workflows as the foundation strengthens.
This is not the same as deploying into chaos and hoping for the best. It is a deliberate progression that builds readiness through deployment rather than waiting for readiness before deployment begins.
Reimagining What Becomes Possible
Organisations that have never worked with agentic AI often struggle to imagine what it might mean for their operations. They can envision automation of specific tasks: faster document processing, automated email responses, streamlined data entry. These are valuable but incremental improvements on existing ways of working.
What is harder to imagine is the reorganisation of work that becomes possible when AI can manage entire workflows.
Consider an operation where multiple human roles exist primarily to coordinate information across systems, to ensure that decisions made in one part of the organisation are reflected in another, to follow up on tasks that have been assigned but not completed, and to answer questions by aggregating information from various sources. These roles exist not because the work requires human intelligence but because the organisation lacks systems capable of performing these coordination functions.
Agentic AI can perform them. Not by automating each task individually but by managing the workflow comprehensively: monitoring status across systems, ensuring information consistency, following up automatically, and providing answers by accessing whatever information is relevant wherever it resides.
When this happens, the humans previously consumed by coordination work become available for something else. Not necessarily fewer humans, but humans doing different things. Things that require judgement, creativity, relationships, and the forms of intelligence that AI does not provide.
This is what transformation looks like. Not faster execution of existing work but reorganisation of what work exists and who does it. It requires imagination to see this possibility, because it requires thinking beyond the current organisation of roles and responsibilities.
Part of our work with organisations is helping them develop this imagination. Not through abstract speculation but through concrete exploration: if this workflow were managed by an AI system, what would change? If this coordination function were handled systematically, what would the people currently doing it do instead? If these questions could be answered instantly by a system with access to all relevant information, how would that change what is possible?
This imaginative work is a necessary preparation for transformation. Organisations that deploy AI without reimagining their work will achieve efficiency gains. Organisations that reimagine their work in light of what AI makes possible will achieve transformation.
Assessing Impact Beyond Efficiency
The default framing for AI impact is efficiency: hours saved, costs reduced, tasks automated. This framing is not wrong, but it is incomplete.
Efficiency gains are real and valuable. When a process that required three hours of human effort happens in three minutes, that is a measurable impact. When errors that occurred in 5% of transactions are eliminated, that is a quantifiable improvement. These metrics justify investment and demonstrate that systems are working.
But efficiency framing misses the broader transformation that occurs when organisations build the capability they previously lacked.
Consider an organisation that, through AI deployment, has for the first time consolidated its customer information into an accessible, queryable form. The efficiency gain might be measured in hours saved on information retrieval. The capability gain is that the organisation now understands its customers in a way it never could before. It can see patterns, identify segments, and recognise opportunities that were invisible when information was fragmented.
Consider an organisation that, through AI deployment, has for the first time articulated its decision logic explicitly. The efficiency gain might be measured in faster decisions. The capability gain is that the organisation now has a foundation for analysing and improving how decisions are made. It can identify inconsistencies, question assumptions, and evolve its approach systematically rather than accidentally.
Consider an organisation that, through AI deployment, has for the first time created comprehensive audit trails of its operations. The efficiency gain might be measured in the reduced compliance effort. The capability gain is that the organisation now has visibility into how it actually works, where bottlenecks occur, where exceptions cluster, and where improvement is possible.
These capability gains are harder to quantify than efficiency metrics but often more valuable. They represent permanent improvements in how the organisation can operate, learn, and evolve. They persist even if the specific AI system were replaced. They compound over time as the organisation builds on its new capabilities.
Impact assessment, properly understood, must include these capability dimensions alongside efficiency metrics. What can the organisation do now that it could not do before? What does it understand now that it did not understand before? What possibilities exist now that did not exist before?
The Compounding Nature of Readiness
Readiness is not a threshold to be crossed but a capability that compounds.
Organisations that begin deploying AI, even from a position of limited readiness, build readiness through the process. Their data infrastructure improves because the deployment required it. Their process understanding deepens because the AI operation surfaced what was implicit. Their technical capability grows because integration creates learning. Their organisational maturity increases because governance questions had to be answered.
This improved readiness makes subsequent deployments easier, faster, and more impactful. The second use case builds on the infrastructure created for the first. The third use case leverages patterns learned from the first two. Each deployment contributes to a foundation that makes the next deployment more tractable.
Organisations that wait for readiness miss this compounding. They remain static while others build. The gap between them and organisations that are deploying and learning widens over time.
This is why building for the unready matters. It is not charity toward organisations that cannot help themselves. It is recognition that readiness develops through action, that waiting for readiness is often waiting indefinitely, and that the organisations willing to begin from where they are will ultimately develop capabilities that the perpetually preparing never will.
Lasting Transformation
There is a phrase we return to when explaining our work: lasting transformation.
The organisations we work with do not just deploy AI systems. They build infrastructure that did not exist. They develop capabilities they previously lacked. They create an understanding of their own operations that was never articulated before. They establish foundations on which continued improvement becomes possible.
This transformation outlasts any particular system. The consolidated data, the documented processes, the governance frameworks, the organisational learning: these persist as assets that improve everything the organisation does, whether or not specific AI systems continue operating.
This is what it means to build for the 95%. It is not lowering standards to serve organisations that cannot meet high bars. It is building infrastructure, developing capability, and creating understanding that enables organisations to operate at levels they could not previously reach.
The ready segment will continue to be served by vendors who optimise for quick deployment and rapid revenue. There is a market for that, and it will be filled.
But the lasting transformations, the ones that take organisations from where they are to where they could be, that build capabilities and infrastructure and understanding that compound over time: these require a different approach. They require meeting organisations where they are, building what they need, and staying engaged through the difficult work of genuine change.
That is the work we have chosen. Not because it is easier, but because it matters more.
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Mitochondria builds AI infrastructure for organisations that need transformation, not just tools. Our approach begins with operational reality, builds readiness through deployment, and creates lasting capability that compounds over time. If you are among the 95% and wondering how to begin, we would welcome the conversation.
Mitochondria builds ATP — agentic AI for operations. It learns your workflows, earns autonomy in stages, and runs with governance built in. Your data stays yours. Based in Amsterdam and Pune, working with organisations across Europe and India.