2025/08/11
The biggest challenge with AI is that AI adoption is continuous change. People think that change is something that you start and then you’re finished. Then you get a breather from it. But with AI and AI adoption, change is continuous. When you’re adopting AI, because of the complexity of AI, it changes a lot of things in people’s work styles, the organization. And when things are in place, the AI on its own is changing, which then causes changes to work styles and the organization. There is no breather. The change is continuous. And that’s really the main problem with AI. It’s this continuous change at a pace that people just aren’t used to, at a pace that managers aren’t used to managing.
Our traditional methods focus on there’s a start and an end for a change and then you are taking a breather to recover from it before you start the next waves of change. But that’s not how AI adoption is. That’s not how AI is. AI is constantly learning and constantly reacting. There are market pressures, AI model updates, people who want to perform. There’s a lot of continuous change to address for successful AI adoption.
There’s quite a few who share the same thoughts on this. Like here’s one from the chief innovation officer from Service Now.
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“according to KPMG research. Gartner found that only 19% of boards reported making progress toward achieving digital transformation goals.”
“The thing about AI is that it’s 100% dependent on meaningful data to help you make decisions based on past activities and outcomes.” Source: Goodbye digital transformation, hello AI-first business transformation (CIO.com) |
One of the big problems with AI adoption is people treat it as a digital thing. AI’s a lot more complicated than a digital initiative. I see AI adoption as something that you need to worry about traditional alignment. You need to worry about business transformation, cultural transformation and digital transformation and how all four of those things are interconnected to each other. And that makes AI adoption, successful AI adoption very complex.
According to KPMG research, Gartner found that only 19% of boards reported making progress towards achieving digital transformation goals. So AI is wrapped in that and only 19% progression. That’s not completing, that’s progression to. Progression should be closer to 91%. There’s a problem there. AI is being implemented in a very narrow way through digital and also we’re not used to continuous change. So we’re getting the double whammy. The thing about AI is that it’s 100% dependent on meaningful data. That’s a tall order and a high bar right there. A lot of companies can’t argue that about their data. Usually they have a lot of data but only a small fraction of it is meaningful.
Here’s some clips from Forrester.
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“Despite widespread individual adoption, we’re not seeing corresponding improvements in corporate balance sheets, GDP figures, or macroeconomic productivity metrics. The micro-level gains aren’t translating to macro-level transformation.”
“new [AI] models and capabilities emerging faster than organizations can absorb them” Source: Why AI ROI Remains Elusive Despite Widespread Adoption (Forrester) |
People can adopt AI but their productivity gains don’t translate to ROI for the company to the balance sheet or to the bottom line of the company. This is a real odd thing to hear. It gives the impression that a lot of those gains from AI are from layoffs, terminating people, and not paying their salaries. That’s where the gains are with AI, not through innovations with AI. And that’s a bitter pill to swallow. There’s also the thing with AI models. They’re changing too fast. Companies can’t absorb it. Companies can’t absorb the new capabilities. They can’t keep up with the pace of change. We need mechanisms in place to keep up with the pace of change. We need AI to help us keep up with the pace of AI and with the other automations. It’s like the AI fox guarding the AI hen house.
Harvard Business Review proposed typical change management approaches in this article.
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“In the past, organizational transformation was episodic.”
“Put human wellbeing at the center of change” Source: A Guide to Building Change Resilience in the Age of AI (HBR) |
What I liked was this clip about in the past that organizational transformation was episodic, which means it had a start and it had an end. All our methodologies, all our approaches have a start and an end. They don’t consider continuous change. So we have to start looking at change as being continuous, something that we need to monitor, something that we need to track, something we need to understand and constantly re-understand. Really the behavior of the change itself. This is getting more meta on the change. Not focusing so much on what is changing, but focusing on the nature of the change and behavior of the change itself, its personality.
And what I like here is putting human well-being at the center of change. And I love when the article said that because I do not believe in reducing headcount and having it replaced with AI. Every time you reduce headcount, you lose something unless that headcount should have been reduced already and you’re just using AI as a smoke screen to hide the fact that you should have done those cuts two or three years ago.
And lastly some quotes from Boston Consulting Group.
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“At the other extreme, fundamental changes require deeper overhauls of behaviors. However, these changes may also entail larger benefits for the individual employee.”
“it identified and promoted champions of change—a group of highly respected and well-connected engineers.” |
Here, they talk about extreme fundamental changes that require deep overhauls of behaviors. It’s hard to change people’s work styles, change their personalities, change their behaviors. But that’s what you’re expecting to do from AI. And the more people work with AI, you will see slight augmentation of how they talk. what they value, and how they prioritize. There’s a few psychological studies on this and some disturbing results are coming out.
The article also mentions that these changes may entail large benefits for the employee. So, this means that anytime there’s changes, you expect people to change. People have to have some benefit from it. You have to have some skin in the game for it. And Boston Consulting Group got all of this from pulling together a simulation. I love simulations. And this about having skin in the game is the typical type of mentality needed when you are dealing with large scale change. It’s easy to change one person. But getting a team to change, an organization to change, that’s hard to scale.
The article also talks about champions. When people change, they need to have an emotional investment in the change. They need to have a relationship with the change. And the best way to do that is to have champions because those champions are the poster people, poster boy, poster girl of that change. Have a relationship with them and you have a relationship with the change. The article talks about identifying and promoting champions of change. This is a group of highly respected and well-connected engineers. Change, effective change requires relationships. It requires people connecting with each other. And that resonates well with me too because I believe that change should succeed based on people fighting to make it happen. Because once people fight to make it happen, people will do everything they can to protect what changed and sustain it.
All of these articles are screaming continuous change. Because change is happening so fast, we need a change meta-framework. Something that will give our current change approaches the additional support they need.
We need to take a step back and look at the nature of change, how it’s behaving, what the personality of the change is. We need something to help incentivize people to look for 100% meaningful data and feel comfortable within this framework using what has worked for them in the past, their own go-to methods. And that this framework must support people up to at least 91% progression towards goals, may it be digital transformation, business transformation, or whatever. And the framework must also help connect between individual productivity and business ROI. This meta-framework would help navigate people through all those milestones of change and problems that change can uncover. It lets you know what’s happening and going to happen so you can plan for it and you can work around it. All the other approaches we currently have are good as a GPS. You know where things are going with the change, where things are headed for the change. But making sure you’re in the right direction, making sure you don’t hit a major pothole or going to the wrong destination completely, that is missing from our status quo approaches and frameworks regarding change.
One framework to consider is “Cascade to Chain: the Change Re-engineering Manifesto”. This is a manifesto that has at least 60 principles on change that help you to understand the behavior behind change, why change works, why change fails, how things are intended to be interconnected with other things at a company, and what you can do to operationalize change, continuous change within your company on a day-to-day basis. Stop treating change as a project and treating change as being operational to reduce the risk, pressures, and burn that you can get from budgets, from people, and from management and leadership. This manifesto focuses on seeing change as a cascade where we have things around that are interlinked by chains and this cascade spreads through those chains to other chains. The Manifesto emphasizes that you monitor the change. You monitor the chains themselves. And you monitor the burn on resources, the burnout of your people, and the burn of your budgets and cash flow.
This framework does what typically a lot of other frameworks argue that they should do. Build from strategy to execution. Change management approaches today focus really well on execution. They just need that navigational system which this meta-framework helps provide. Because there’s a constant monitoring of people, of the budgets, of the money, it makes progression towards sustainable change. You’re getting that 91% progression. And because people tend to work the way they tend to do, it helps people use their own approaches. And the meta-framework helps them redirect those approaches by changing their priorities and their mindsets on how they look at change and the scope for change.
You will find that AI adoption is continuous change. AI is change. But we need change re-engineering to help us keep up with change. You can’t control change. All you can do is redirect it and make sure that you mitigate the impact and the damage.
BIO
