Is your Organization Doing it Right?
Imagine hiring a highly capable, new employee and then doing the following: giving them no introduction to the company, no clarity on what they are supposed to do, no access to the systems or data they need to do it, and no feedback on whether they are getting it right. Then, after a few weeks, declaring that they do not perform and that the hire was a mistake.
It sounds absurd. But it is almost exactly how most organizations are deploying AI.
The Magic Trick That was Never Magic
AI arrived in the business world wrapped in a kind of mythology. It was described as transformative, disruptive and intelligent in ways that implied something close to autonomous capability. A technology that would figure things out by itself, if only it was given access.
That framing has caused enormous damage to real-world AI adoption. Because AI, powerful as it is, does not figure things out by itself.
It performs within the context it is given. Feed it good context, clear goals, the right information and meaningful feedback, and it performs remarkably well. Feed it ambiguity, noise, incomplete data and no oversight, and it performs poorly.
The technology is not the variable. No, the key variable is how the technology is brought on board.
Most failed AI projects are not really AI failures. They are onboarding failures. Context failures. Operating model failures.
What Good Onboarding Actually Looks Like
Think about what a well-run organization does when it brings in a strong new hire. It gives them a thorough introduction to how the business works. It defines what good looks like, along with achievable goals. It provides access to the right people, systems and information. It sets clear expectations, defines the boundaries of the role and creates feedback mechanisms so the person can calibrate and improve over time.
None of this is complicated. But all of it is deliberate. And none of it happens automatically.
The same logic applies directly to AI. Before an AI system can add value in a business context, it needs to be given the equivalent of that onboarding, which will determine its success or failure:
Without onboarding
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With onboarding
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The difference between these two columns is not a difference in the AI. It is a difference in the organization. And in most cases, the organizations that conclude AI does not work are operating somewhere in the left column.
The Force Multiplier Problem
There is a useful analogy in physics. A force multiplier amplifies the effort you put in –but only if you apply force in the right direction. A lever is useless if you push from the wrong end. A sophisticated tool with no trained operator is just an expensive piece of equipment.
AI is one of the most powerful force multipliers available to businesses today. But it multiplies what it is given. Provide it clarity and structure, it returns speed and scale. Give it confusion and approximation, it returns output that is confidently wrong at volume.
This is why the promise and the reality of AI can feel so different. The organizations reporting the strongest results are not necessarily the ones that have spent the most on AI. They are the ones that have thought most carefully about how to introduce it, how to integrate it into existing workflows and how to design the human-AI relationship so that each side is doing what it does best.
The companies that win will not be the ones that buy the most AI. They will be the ones that introduce it properly, supervise it well, and design the organization around it.
Designing the Organization Around AI
This last point deserves particular attention, because it represents a shift that many leaders have not yet made.
Most organizations are still trying to fit AI into their existing structure – adding it as a tool on top of processes that were designed without it. That approach yields incremental gains at best.
The organizations that are pulling ahead are doing something more fundamental. They are redesigning workflows, roles and decision rights with AI as a first-class participant, not an add-on.
That means deciding which decisions AI should handle autonomously, which it should support and which should remain entirely with humans. It means creating clear escalation paths for edge cases. It means training people not just to use AI tools, but to manage them – to review their outputs critically, to give feedback that improves performance, and to know when to override.
This is, in essence, a management discipline. And like all management disciplines, it can be learned, systematized and improved over time.
Stop Treating AI Like an Experiment or Toy
The shift required is partly cultural. For many organizations, AI is still in a phase of experimentation – something the innovation team plays with, something that gets demoed at offsites and something that a handful of enthusiasts use on the side. It is treated as a toy.
Toys do not get onboarded. They do not receive context or feedback. They are picked up, played with for a while, and put back on the shelf when they stop being interesting.
A team member is different. A team member is integrated. They are invested in. They are held to a standard. And over time, with the right support, they grow into something the organization genuinely depends on.
The organizations that are going to extract the most value from AI are the ones making that transition now – from toy to team member and from experiment to operating model.
Author Bio:
Toni Nijm is Chief Product Officer at Anaqua, a global leader in intellectual property management. With over 20 years of experience in IP law, technology, and SaaS innovation, Toni is passionate about building solutions that transform how IP professionals work.
