In this piece, Dmitry Doshaniy, CEO of 10ⁿ Tech, reflects on why AI and digital transformation investments so often fall short on the ground, and what actually moves a tool from pilot to everyday habit. Adapted from his conversation on the C-Suite Reflection with Nikita Jain, Co-founder and CEO of Eubrics AI.
Last week I was picking up my daughter from a play date. Her friend’s mother made the mistake of standing still long enough, and I spent thirty minutes explaining how AI could transform her small business. That is roughly my default state now. AI is in every conversation, at work and at home.
But there is a gap between how much we talk about AI and how much it actually changes on the ground. I joined Nikita Jain on the C-Suite Reflection to talk about that gap, and here is the longer version of what I think.
We are still at the very start
If you map AI onto the classic adoption curve, innovators, early adopters, the majority, the laggards, we are still with the early adopters. The innovators are making full use of it. Everyone else is watching.
What is unusual is the pace. AI reached its first billions of users in a handful of years. The telephone took decades. The internet took decades. But the shape of the curve itself is not set by the technology. It is set by us. Some people are natural risk-takers. Most are cautious. And if we were not cautious and risk-averse as a species, we would have gone extinct long ago. The big impact is still ahead.
Where transformation actually falls short
This is not an AI problem. It is a transformation problem, and it is an old one.
The research is blunt about it. IDC expects worldwide spending on digital transformation to pass two trillion dollars within a few years, and study after study finds that around 84% of those initiatives fail to deliver what they promised. They do not fail because the technology did not work. They fail on alignment, communication, and capability. North Carolina State’s risk management work puts resistance to change and legacy technology at the very top of the risk list for business today.
That matches what I see. The highest risk factors are resistance to change, legacy technology, and regulation, both government rules and a company’s own internal ones. And the root problem underneath all of them is that not enough planning goes into how the change will actually be delivered.
When these initiatives get scoped and budgeted, the focus is almost always technical. What are the building blocks. What software do we buy. Which engineers or contractors do we bring in. What rarely happens is communicating the impact early and engaging the actual end users early. So when the people whose work is about to change finally meet the new system, they ask the questions no one prepared them for. Is this a shiny new toy I do not need? Is it going to make me redundant? What happens to the relationships I have built around the old process?
Legacy technology is the same story wearing a different coat. Replacing a system is never just swapping hardware or software. There is a team that brought that system in years ago. They may still believe it is the right thing, and they may not want anyone running a project in their domain. Regulations work the same way. Someone wrote them, believed they were valid, and may believe they are still valid today.
Here is the honest version I give clients. In software, almost anything is possible. Name a task your organization needs to achieve and ask me if modern technology can do it, the answer is usually yes. But ask me how to achieve a specific transformation in your business, and the honest answer is “I do not know yet.” Not until we have done a proper feasibility study and mapped four environments: the existing technology, the regulatory landscape, the people, and the processes. Only then can we talk about how to make it work.
Getting everyone moving in one direction
Every person on a team sits at a different distance from AI. Some want to jump into everything. Some want nothing to do with it. So the first job is to answer one very simple question for each of them: what is in it for me?
To answer it, you have to stand in their shoes. Henry Ford put it precisely: “If there is any one secret of success, it lies in the ability to get the other person’s point of view and see things from that person’s angle as well as from your own.” When people can see how a tool helps them do their own work better, they come on board.
You also cannot discount the initial resistance. It will always be there, because we are wired against the new. So the introduction matters. Show how it is done. Pick a small pilot group, sometimes just one or two people who are naturally open and genuinely enthusiastic. Make them the champions, and make sure they sit at the same level as the people who are meant to adopt next.
This is the part most leaders get wrong. An excited manager is not enough. But when one or two peers start using the tool and their results visibly improve, and they share that with the team, something hardwired kicks in. Call it mirror motivation. We cannot stop ourselves yawning when someone near us yawns, and we copy the people around us, especially the ones we respect, especially when we can see it is working for them. Nobody wants to be left behind.
And you do not give up at the first friction. Early problems are inevitable as people learn. You hold the conviction that it will work, you point to the case studies of people who already made it work, and you give it time for the habit to form. When the new tool becomes a natural part of how things get done, that is when you can call it a success.
Change fatigue and the knowledge worker
A lot of people are tired. I hear it from learners and from L&D managers who say, fairly, “I was not trained for this, why is my manager pushing me toward agents?”
The only constant is change. For anyone in technology, this did not start yesterday. I have watched a large share of what I learned over twenty years become obsolete. You live in a constant cycle of unlearning and relearning. What used to take a year to go stale now takes a quarter. It is accelerating, and at times it is genuinely hard to keep up.
And yet I am an optimist. Statistically, this is the best time to be alive, lifespan, access, and opportunity all near their peak, and I expect new peaks ahead. This is not the first technology revolution and it will not be the last. Decades after the steam engine, after electricity, almost everyone was better off, even the workers who lit the gas lanterns and lost that job. The hard part is the adaptation in between, and that is the part we have to manage with care.
For knowledge workers specifically, there is no longer any separating yourself from AI tools, in the same way you cannot separate yourself from email or a word processor. You do not get to say you will write on paper and seal it in an envelope. Used properly, these tools help us do more, faster. The motto I give my team is simple: the goal is to work at the speed of thought. We are not far from it.
Crawl before you run
The most common mistake I hear is trying to run before you can crawl. You do not go from running an organization the traditional way to commanding hundreds of autonomous agents overnight, or even in half a year. You build the harnesses and the skills first.
From my automation engineering background, one law never changes: garbage in, garbage out. No amount of AI fixes bad data. If you cannot feed a model clean, validated information, it will not give you the right answer. It will try, it will say “sure,” and then it will hallucinate, and you will get failures you cannot explain. You start doubting the technology, and the project gets delayed or killed. So the data quality problem comes first.
The vision I am building toward is that everyone in an organization gets a team of assistants. Not all of them run around the clock. If you produce a quarterly report, you do not need an agent sitting in the cloud burning resources every day, you need a tool you run once a quarter. But if you want a daily read on what competitors are doing, then yes, while you sleep something crawls the web, processes it, connects to your CRM, and hands you a digest that is specific and actionable.
That last one is not theoretical. One of our target markets is real estate developers. We run a process that watches for new project announcements so we can reach a prospect before our competitors, with the right solution at the moment they need it, not half a year early, and not after they have already chosen an alternative.
Scaling beyond the pilot
Scaling AI past the pilot stage is a minefield, and there is no single mistake to avoid.
I still put the people factor first. Weak user adoption and weak advocacy for the change is the number one killer. But the technical traps are real too. Security gets stricter and the risk profile grows as more people get access. The context window becomes a bottleneck, push harder tasks and more data into a model and you can get less accurate results than you did at small scale.
There is an economic trap as well. I have heard of companies that mandated AI use and gamified it with token leaderboards, only for a finance chief to discover that the quarter’s entire IT budget had gone up in smoke. And it is a hard fact that the model providers are currently spending more than they earn. That cannot continue forever. At some point investors want a return, and I think it is close to inevitable that token costs rise from where they are now, absent another algorithmic breakthrough.
None of this is easy. But as I tell my team, people make money doing hard things.
Dmitry Doshaniy is the CEO of 10ⁿ Tech, a Dubai-based technology firm working across digital twins, enterprise IT, and applied AI for clients in the GCC, MENA, and beyond.
Reach out to 10ⁿ Tech
If your AI ambitions have outgrown the pilot stage, or the gap between what your teams should be doing with these tools and what they actually do every day has started to show, we are open to a conversation. You can reach out to our team.
10ⁿ Tech works with clients across digital twins, enterprise IT, and applied AI, helping teams turn complex technical needs into practical, deployable solutions.
