I grew up watching my dad use a typewriter. It was the technology for writing letters, preparing documents and communicating. Then came the computer, and suddenly the way we worked began to change. At first, it looked like a better typewriter. But it quickly became much more than that.
Over the years, I have lived through several technology transitions from radio and television to desktop computers, consumer electronics and semiconductors. I have founded and built businesses around some of these technologies and, more importantly, watched how they changed markets, business models and customer behaviour.
One lesson has stayed with me:
Technology itself is rarely the disruption. The real disruption happens when technology enables people to solve a problem differently and create measurable value.
That is why I believe we need to look at technology differently.
So do not consider AI as another technology cycle, when the PC arrived, businesses had time, hardware had to become affordable and software had to be developed. People needed training. Companies had to redesign processes, IT departments had to build infrastructure, the transition was significant, but relatively gradual.
The internet was similar. Mobile computing was similar. Cloud computing was similar.
AI is different as it can increasingly sit on top of infrastructure businesses already have. It can work with documents, images, voice, data, software and existing workflows.
It does not always require an organisation to completely change the way it works before it can see value. That dramatically reduces the friction between innovation and adoption.
And this is where I believe the opportunity for businesses and for users working with them becomes very interesting. Do not sell technology. Solve a problem. When speaking with a business, I would avoid starting the conversation with:
“We have a solution.” is a wrong start, instead have a conversation with curiosity, like
“What is the problem you are trying to solve?”
Where are customers frustrated?
Where are employees spending too much time?
Where are decisions too slow?
Where are costs unnecessarily high?
Where are opportunities being missed?
Where is information difficult to access?
Where are repetitive tasks consuming valuable people?
Where could better intelligence improve an existing process?
These are the conversations that matter. Only after understanding the problem should we ask:
“Could technology help us solve this better, faster or more economically?”
That changes the entire conversation.
You are not selling technology, you are solving a business problem.
Start small. Prove lasting value.
One of my strongest recommendations is:
Do not try to sell the whole transformation on day one. Management does not necessarily need another large technology programme, another presentation full of buzzwords or another three-year transformation roadmap.
Give them something they can understand. Identify one meaningful problem.
Define the expected outcome.Build an incremental solution.
Run a Proof of Concept. Measure the result.
Then let the evidence open the next door.
A successful PoC or a Demo should answer simple questions:
• Did we solve the problem?
• Did we save time?
• Did we reduce cost?
• Did we improve quality?
• Did we increase revenue?
• Did we improve customer experience?
• Did we reduce risk?
• Can we integrate it into the existing environment?
• Can the solution be scaled?
If the answer is yes, the conversation becomes very different.
You are no longer asking them to believe in a new technology.
You are showing them what technology has already delivered. Proof creates confidence I have learned through decades of working with technology that adoption happens faster when people can see, touch and measure the outcome.
This is particularly important with AI because there is so much noise around it.
Every week there is a new model, platform, application or promise.Businesses do not need more hype.They need evidence.
Founder therefore have an important role to play: reduce the perceived risk of adoption. Do not ask a customer to commit to a massive transformation.Help them take the first practical step.
Problem, Solution, PoC, Evidence, and Business Case. That is a much more powerful path to adoption.
Integration is the real accelerator
When I moved from a typewriter to a computer, the real breakthrough was not simply the computer. It was what happened when the computer became part of the way we already worked.
The same happened with email, the internet, mobile phones, smartphones and cloud computing. The winning technologies integrated into existing behaviour.
AI takes this principle to another level. It can potentially become part of sales, marketing, customer service, finance, engineering, HR, procurement, operations and decision-making without requiring an organisation to rebuild everything from scratch.
That is why I believe integration and flexibility will be among the biggest competitive advantages in AI adoption. The businesses that can introduce AI incrementally, learn quickly and integrate what works will have an advantage over those waiting for the perfect solution.
The conversation with management has changed. For years, technology conversations often started with:
“Should we invest in this technology?”
I think the better question today is:
“Where can we create measurable business value by using this technology?”
And “How quickly can we prove it?”
That is a much more practical conversation. Management does not need to understand every technical detail or jargon. They need to understand the business outcome.
If we can demonstrate that an existing process can be made 30% faster, at no additional cost and long term cost can be reduced, that customer response can improve, or that a decision can be made with better information, we have created a reason to continue the conversation.
The economics will drive adoption, it will not happen simply because people become excited about technology. It will happen because the economics make it increasingly difficult to ignore.
If one competitor can respond to customers faster, develop products more quickly, automate routine work, analyse markets more intelligently or operate with a lower cost base, others will eventually have to respond.The competitive gap may start very quietly.
One company experiments. Another company measures. A third company scales. Suddenly, the difference is no longer about technology. It is about business performance.
That is why I believe organisations are experimenting now and not because they know exactly where AI will take them, but because they want to build the organisational ability to learn.
The Operating Reality
Start with one problem. Start with one use case. Start with one measurable outcome with a clear Call to Action.
Then prove it. If it works, expand. If it doesn’t, learn and move on. This reduces risk for the customer and gives the partner real-world intelligence about what works.
It also creates momentum. AI-readiness is about learning. Being ready for AI does not mean having every AI tool. It does not mean replacing everything with AI. And it certainly does not mean chasing every new development.
AI-readiness means being able to identify opportunities, experiment quickly, measure outcomes and scale what works. It means creating a culture where technology can be integrated into the an operating reality rather than becoming another isolated project.
We have no time to waste I have lived through enough technology transitions to know that adoption rarely happens because everyone suddenly becomes comfortable with change. It happens because the economics, the customer and the competitive environment eventually make change unavoidable.
With AI, those forces are arriving together. The technology is improving. The cost of intelligence is falling. Access is becoming easier. Integration is becoming simpler. And competitors are experimenting. So my message to the Founder is simple:
Find something that matters. Build an incremental solution.Run a Proof of Concept. Measure the value. Show the evidence.
Then discuss commercials to implement what works. That is how we turn AI from a technology conversation into a business conversation.
I learned this lesson when I moved from a typewriter to a computer. I saw it again through radio, television, PCs, semiconductors and consumer electronics.
Every generation of technology taught me that integration and operating reality accelerates adoption. AI is taking that lesson to a completely different scale. This time, the speed of change may itself be the disruption.
Our opportunity is not to predict exactly what AI will become. The opportunity is to start creating value with it today.
Start with the problem. Prove the solution. Build the confidence. That is how adoption becomes business growth.
This makes the technology proposition much clearer: you are not asking them to buy; you are helping them solve a problem with evidence and a low-risk path to scale.
