Buying an AI subscription takes minutes. Making it useful for a person, a team or a business takes more work.
Someone has to connect it to a real task, explain how to use it, check its output and help people build confidence. That work receives less attention than the latest model release, but I believe it will determine how widely AI improves our lives.
There is a term for this: technology diffusion. Put simply, it is how a new capability spreads from the people who create it to the people who can use it in everyday life.
We have been through this before.
We have learned new ways to work before
Think about the computer on an office desk. Its usefulness grew through familiar applications: writing a letter, preparing a spreadsheet or keeping accounts. The history of the IBM PC shows how spreadsheets and word-processing software helped make personal computers part of corporate life.
People also had to learn new routines. A spreadsheet changed how a budget could be prepared, checked and shared. A company needed people who understood both the software and the business behind the numbers. Research on the business value of IT has long highlighted the importance of these organisational changes alongside the technology.
The internet, broadband and mobile phones brought further changes. The internet connected people and information. Broadband made faster, more reliable online work practical. Mobile phones put that connection into our pockets, making activities such as banking, navigation and communicating with customers possible on the move.
Each step needed more than a device. People needed affordable access, useful services, skills and a reason to change a familiar habit. Those remain central ingredients of meaningful connectivity today.
Some technologies spread quickly. Others take years to reach particular communities or become embedded in daily work. Widespread awareness can arrive well before confident, useful adoption.
The lesson from the trades
This story goes back much further than computers. Research on Britain’s Industrial Revolution highlights the contribution of skilled craftspeople and mechanics who adapted, improved and put inventions to work. Apprenticeship helped pass that knowledge from one person to another.
The same principle is easy to see in a workshop today. Giving someone a better tool is a starting point. They still need to learn when to use it, how to use it well and how to recognise a mistake. Experience matters.
With AI, we need that same combination of a capable tool, practical teaching and repeated use on real tasks.
Why adoption takes patience
It is easy to call people resistant to change. Their hesitation often has a sensible explanation.
A small-business owner has limited money and time. An employee may be unsure which information can be shared with an AI tool. A manager needs to know who is responsible when an answer is wrong. Someone who has done a job successfully for twenty years deserves a clear explanation of how a new approach will help.
People also worry about their jobs. Leaders need to discuss how tasks may change, involve the people doing the work and give them time to learn. Training needs to include practice and support after the first demonstration.
Adoption becomes easier when the benefit is concrete: finding the right record, preparing a useful first draft or completing a routine report with less effort. A colleague who can see and check the result has a reason to try again.
What we are learning at UBI
Our work at UBI Business School has made this very practical for me.
In developing DeeJay as an AI colleague, we have worked on everyday tasks: finding correspondence history, preparing repeatable reports and giving staff draft communications they can review. The details matter. The information has to belong to the right person. The figures need agreed definitions. The output needs to reach the colleague in a form they can use.
For research-ethics work, our principle is that AI should prepare the first cut, while academics review it, refine it and retain final approval. That gives the technology a clear role and keeps responsibility with the people qualified to exercise judgment.
This is also a learning process for the organisation. Staff need to understand what the assistant can do, where its limits are and how to correct it. Their feedback helps shape the next improvement.
Education has an important role here. People need opportunities to work with AI in the context of real responsibilities, so they can develop the judgment to use it well.
The role I want IJK to play
At IJK, we see this as part of building stronger businesses and widening access to useful technology.
Through our work with UBI, we can connect learning with practical experience. Through our investments, we can work with management teams to understand where AI could improve service, reduce repetitive work or help a company handle growth. Different businesses will have different priorities.
Our AI implementation consulting arm helps with the work between an idea and everyday use: choosing a worthwhile problem, designing the process, building and testing a solution, helping the team adopt it and improving it through feedback.
Our cross-border perspective matters too. A process that works in one market may need a different language, a different way of serving customers or different operating arrangements elsewhere. Implementation has to fit the people and the business using it.
For me, this is an important part of the investment case. Useful adoption can strengthen a company’s ability to serve customers and get work done. That is the kind of progress we want to help create.
Progress should reach ordinary life
The wider social question is who gets to benefit. Smaller businesses, older workers and people with fewer resources need affordable tools, accessible teaching and support. The World Bank’s 2016 report on digital development made a lesson clear: the spread of digital technology can outpace the spread of its benefits. Skills and institutions matter.
I believe we should judge AI by changes people can feel: less time spent searching for information, clearer service, fewer avoidable errors and more time for teaching, customers and difficult decisions.
Getting there will take patient work. Someone has to sit beside the person trying the tool, understand what is confusing and help make the next attempt better.
Through UBI, our investments and our AI implementation work, that is the role I want IJK to help fulfil: bringing a powerful technology into the everyday work and lives of more people.