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Most companies allocate their AI budget resources to the wrong end goal. It states something like “replace SAP,” “decommission the mainframe,” or “reduce the technical debt.” The real bottleneck is, however, much more mundane – the three-week approval process, the outdated job description written back in 2016, and the gap between finance and legal functions that no software could bridge. The best model that one could find on the market will have problems with it.
This is exactly why there are many AI initiatives out there feeling busy and expensive but not really producing any tangible results. A real-world AI transformation model should start from finding the real constraint, and it’s never about the technology.
Around 90% of firms have initiated their journey using AI. Almost no executive team can deny it now. But, when the executives assess the results of their activities, there is no visible impact on either their top or bottom lines.
Where does that show up, then? In almost all cases, it manifests itself through increased personal productivity. People type emails faster, read documents instantly, and write code faster. It works and works well for the people who actually do the job. But such productivity does not translate to P&L. It evaporates before reaching it.
This is the key issue in enterprise AI adoption. The technologies are powerful. They are used effectively by the people. But the company still has its structure as it was before. The intelligence of tomorrow is incorporated into the process of yesterday.
See what your AI is actually costing before you redesign anything. AgentNuro finds every AI tool, agent, and dollar of spend and attributes it to the team, project, and model driving it. Run your AI adoption audit.
Anyone who has run a transformation program before assumes the thing to escape is a system: the ERP, the mainframe, the tangle of technical debt. That instinct is off by one layer.
The bigger constraint is your approval process. Your org hierarchy. How your meetings are run. How your job descriptions were written. The silo walls standing between finance, legal, and HR. And the way your KPIs were defined years ago, for a company that no longer exists.
None of that lives in a codebase. All of it decides whether an AI initiative lands or dies. Your legacy system is not your technology debt. It is your organisation. And organisations, unlike servers, do not get decommissioned on a project plan.
This is also the cleanest way to understand AI transformation vs digital transformation. Digital transformation largely asked how to move existing work onto better technology. AI transformation asks something harder: whether the work should exist in its current form at all. One upgrades the plumbing. The other questions the building.
Here is the line worth pinning to the wall. Technology transformation without organisational transformation gives you faster old work. And faster old work is the last thing you want AI to produce.
The history of the factory floor makes the point. When electric motors first arrived, early factories pulled out the steam engine and dropped in one big electric motor, then kept the same overhead shafts and the same shop floor. The technology was new. The layout was old. The gains were marginal.
The breakthrough came later, when motors were built directly into individual machines and the entire factory was redesigned around that new freedom. The value was never in the motor. It was in redrawing the floor.
Handing every employee a chatbot or a copilot is the "one motor, many machines" move all over again. You have upgraded the power source and left the layout untouched. The breakthrough comes when you redesign the factory, not when you install the motor.
If the constraint is organizational, then the framework has to be one too. That completely upends the conventional way of thinking about the progression of AI transformation steps.
Teams move up: first automation of the task, then automation of the workflow, then automation of the function, and, maybe eventually, the rethink of the business model. The problem occurs once they find themselves stalled at the workflow or function stage because all layers above them still follow the old processes, the old silos, and the old key performance indicators. The ceiling here is 10%.
Reverse that progression: start from the operating model and work down. First ask yourself the CEO-level question: if we were building the company now and the only constraint is a lack of intelligence, would we design our processes this way? This already changes the product you will sell, your pricing, and organization before the task gets involved.
That is why an AI operating model works so well. The AI operating model forces the pillars that have been working on their own (Data, Engineering, Legal, Finance, HR) to meet for one initiative. No serious AI effort can sustain engagement with those silos that are not willing to work together. The coordination of those pillars becomes the key effort, and that effort is organizational in nature.
Someone has to own AI spend once teams start coordinating. AgentNuro gives finance, legal, and engineering one view: real-time cost alerts, duplicate throttling, and spend capped before the invoice lands. Start a 4-week free trial.
None of this means the technology is easy or that governance and cost take care of themselves. They don't. But those become the obstacles you plan for once the operating model is right, not the reasons a good initiative quietly dies.
Skip the strategy offsite for a moment and do something smaller and more honest.
Pick one AI initiative you actually care about. Then list every non-technical thing that would have to change for it to truly land. Which approval has to be rewritten. Which job description no longer fits. Which two teams have to stop guarding their boundary. Which KPI is quietly working against the outcome you want.
That list is your real roadmap. It is almost never about the model, and almost always about the organisation around it. The companies that pull ahead with AI will not be the ones that automated the past a little faster. They will be the ones willing to redraw the floor.
Your tech stack was never the legacy system. Your org chart was. Start there.
Watch the full keynote this piece is drawn from.
Ravi laid out all ten considerations for thinking 10x instead of 10%, plus the strategy framework for mapping where your organisation sits today. It's worth the 20 minutes.
Request a no cost, no obligation free assessment —just 15 minutes to savings!
Get StartedMost companies allocate their AI budget resources to the wrong end goal. It states something like “replace SAP,” “decommission the mainframe,” or “reduce the technical debt.” The real bottleneck is, however, much more mundane – the three-week approval process, the outdated job description written back in 2016, and the gap between finance and legal functions that no software could bridge. The best model that one could find on the market will have problems with it.
This is exactly why there are many AI initiatives out there feeling busy and expensive but not really producing any tangible results. A real-world AI transformation model should start from finding the real constraint, and it’s never about the technology.
Around 90% of firms have initiated their journey using AI. Almost no executive team can deny it now. But, when the executives assess the results of their activities, there is no visible impact on either their top or bottom lines.
Where does that show up, then? In almost all cases, it manifests itself through increased personal productivity. People type emails faster, read documents instantly, and write code faster. It works and works well for the people who actually do the job. But such productivity does not translate to P&L. It evaporates before reaching it.
This is the key issue in enterprise AI adoption. The technologies are powerful. They are used effectively by the people. But the company still has its structure as it was before. The intelligence of tomorrow is incorporated into the process of yesterday.
See what your AI is actually costing before you redesign anything. AgentNuro finds every AI tool, agent, and dollar of spend and attributes it to the team, project, and model driving it. Run your AI adoption audit.
Anyone who has run a transformation program before assumes the thing to escape is a system: the ERP, the mainframe, the tangle of technical debt. That instinct is off by one layer.
The bigger constraint is your approval process. Your org hierarchy. How your meetings are run. How your job descriptions were written. The silo walls standing between finance, legal, and HR. And the way your KPIs were defined years ago, for a company that no longer exists.
None of that lives in a codebase. All of it decides whether an AI initiative lands or dies. Your legacy system is not your technology debt. It is your organisation. And organisations, unlike servers, do not get decommissioned on a project plan.
This is also the cleanest way to understand AI transformation vs digital transformation. Digital transformation largely asked how to move existing work onto better technology. AI transformation asks something harder: whether the work should exist in its current form at all. One upgrades the plumbing. The other questions the building.
Here is the line worth pinning to the wall. Technology transformation without organisational transformation gives you faster old work. And faster old work is the last thing you want AI to produce.
The history of the factory floor makes the point. When electric motors first arrived, early factories pulled out the steam engine and dropped in one big electric motor, then kept the same overhead shafts and the same shop floor. The technology was new. The layout was old. The gains were marginal.
The breakthrough came later, when motors were built directly into individual machines and the entire factory was redesigned around that new freedom. The value was never in the motor. It was in redrawing the floor.
Handing every employee a chatbot or a copilot is the "one motor, many machines" move all over again. You have upgraded the power source and left the layout untouched. The breakthrough comes when you redesign the factory, not when you install the motor.
If the constraint is organizational, then the framework has to be one too. That completely upends the conventional way of thinking about the progression of AI transformation steps.
Teams move up: first automation of the task, then automation of the workflow, then automation of the function, and, maybe eventually, the rethink of the business model. The problem occurs once they find themselves stalled at the workflow or function stage because all layers above them still follow the old processes, the old silos, and the old key performance indicators. The ceiling here is 10%.
Reverse that progression: start from the operating model and work down. First ask yourself the CEO-level question: if we were building the company now and the only constraint is a lack of intelligence, would we design our processes this way? This already changes the product you will sell, your pricing, and organization before the task gets involved.
That is why an AI operating model works so well. The AI operating model forces the pillars that have been working on their own (Data, Engineering, Legal, Finance, HR) to meet for one initiative. No serious AI effort can sustain engagement with those silos that are not willing to work together. The coordination of those pillars becomes the key effort, and that effort is organizational in nature.
Someone has to own AI spend once teams start coordinating. AgentNuro gives finance, legal, and engineering one view: real-time cost alerts, duplicate throttling, and spend capped before the invoice lands. Start a 4-week free trial.
None of this means the technology is easy or that governance and cost take care of themselves. They don't. But those become the obstacles you plan for once the operating model is right, not the reasons a good initiative quietly dies.
Skip the strategy offsite for a moment and do something smaller and more honest.
Pick one AI initiative you actually care about. Then list every non-technical thing that would have to change for it to truly land. Which approval has to be rewritten. Which job description no longer fits. Which two teams have to stop guarding their boundary. Which KPI is quietly working against the outcome you want.
That list is your real roadmap. It is almost never about the model, and almost always about the organisation around it. The companies that pull ahead with AI will not be the ones that automated the past a little faster. They will be the ones willing to redraw the floor.
Your tech stack was never the legacy system. Your org chart was. Start there.
Watch the full keynote this piece is drawn from.
Ravi laid out all ten considerations for thinking 10x instead of 10%, plus the strategy framework for mapping where your organisation sits today. It's worth the 20 minutes.
Request a no cost, no obligation free assessment - just 15 minutes to savings!
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