By Eugenia Mykuliak
The biggest roadblock to AI adoption is no longer the readiness of the technology. It’s whether companies are flexible enough to redesign their operations accordingly.
Artificial intelligence has matured and expanded greatly, but most organizations are still struggling to get significant results out of its applications. Eugenia Mykuliak of B2PRIME Group believes that the core of the problem lies in how firms fail to properly restructure their workflows to include the new tools. They miss the full potential of AI by treating it as simply another software installation rather than the operational transformation it’s supposed to be.
There is a common misconception that I’ve been noticing in regards to AI adoption in recent times: namely, that companies are struggling because the technology is not ready yet. But while that may have been true even just a year ago, that’s no longer the case today. In reality, it would not be inaccurate to say that the AI tools have advanced much faster than most organizations themselves.
In finance, this is particularly visible. AI is no longer an experiment sitting somewhere on the edge of operations; it’s actively being embedded into compliance workflows, fraud monitoring, analytics, customer support and more. And yet, despite all this, many organizations still fail to create meaningful transformation from these tools. McKinsey found that by the end of 2025, only roughly 7% of companies have truly scaled AI across their infrastructure.
Why is this the case? Because far too many firms are trying to fit AI without changing how they operate around it. And it’s not working.
Most Companies Are Not Actually Transforming
There is a very simple but important point that more firms need to understand: simply buying AI tools is not enough. Yes, a lot of companies introduce AI assistants, automation, dashboards, and more — but then, more often than not, they continue operating with minimal changes to their mindset.
As a result, adoption rarely goes beyond “pilot mode.” Teams continue doing things manually, and no real improvement takes place. What happens instead is that organizations simply layer advanced technology on top of outdated structures without truly understanding what it is that they’re attempting to change, or how. Is it any surprise, then, that instead of helping, it ends up causing headaches for everyone involved?
This is one of the main reasons why AI adoption fails to generate the expected results: because businesses tend to assume that implementing the technology is in itself the transformation. But that’s only where the truly difficult part begins.
Here’s a straightforward example, a company may automate onboarding checks that previously took several days, yet still require managers to manually re-approve every stage before a client can move forward. Or it can generate AI-powered analytics in real time, while executive leadership still continues to make decisions based on weekly or monthly reporting cycles.
In these situations, the technology is not at fault; it works just like intended. The problem is that teams simply do not adapt around it.
The Human Bottleneck
What we are seeing today is a human bottleneck, rather than a technological one.
The thing is, I’ve seen how operational teams can actually be very motivated to automate repetitive tasks because they know they’ve been dealing with inefficiencies in some way or another. But on the other side of this equation, we have implementation teams that don’t necessarily know how to redesign workflows after they’ve introduces AI systems.
And what’s worse, in many cases, no one really takes accountability for the transition process, and so it rarely goes anywhere. If there isn’t anyone to keep track of things, no clearly defined goals or success parameters, then who can really say that progress is actually happening? That AI is changing things for the better?
Companies have long since grown accustomed to certain ways of doing things, and changing deeply embedded processes requires a lot of effort. Coordination across departments, employee retraining, defining new responsibilities tied to AI systems — this can be quite a disruptive process.
The problem, of course, is that firms often fail to understand this before they rush into attempts at integration. Then, later on, when they do realize the extent of changes needed, they become reluctant to commit in full.
In short, the core problem is that, organizations lack the proper mindset when they start looking in the direction of AI. And without that, the technology becomes an extra layer of complexity rather than a source of efficiency like it’s meant to be.
Operational Flexibility Will Define the Next AI Leaders
All in all, successful AI adoption today is less about automation itself and more about operational control.
With regulators still trying to catch up with the speed of AI adoption, companies around the world pretty much need to build governance frameworks themselves. The ones that integrate AI most successfully essentially set standards and boundaries around what these systems can do and what they should not.
The companies willing to redesign themselves around artificial intelligence operationally will benefit most from it, and they are also likely to establish themselves as the frontrunners and influencers of how this technology goes on to change the industry at large.
To achieve that state, however, businesses need to think: about workflows, about accountability, about how to retrain teams and change up decision-making around real-time AI assistance.
Ultimately, AI adoption is no longer a software challenge — it’s a management one.
Eugenia Mykuliak
