How to Make Sense of AI
- Graham
- Aug 29
- 5 min read
Updated: Sep 1
In my lifetime, few technologies have generated the volume of fear, hope and confusion as artificial intelligence.
The internet was big, but AI has the potential to be much bigger. The implications of a new technology that doesn't just make information more available, but can use that information to make decisions on our behalf, seem overwhelming. No one claimed the internet could wipe out the human race or help us live forever, yet that is exactly what is being said about artificial intelligence.
But even if you're not worried about armageddon, AI still has the power to mess with our lives in substantial ways, most noticeably through our work. How can we make sense of what's happening now so we can make smart decisions about the future?
The job of the futurist is exactly that – to decipher what you can see happening now with a view to unlocking the mysteries of the future.
And you can be a futurist too.
As a speaker on artificial intelligence, I talk about the trends that influence specific industries or functions, but I also encourage people to take control of their own destinies by being becoming DIY futurists. Here's how.
Thanks for the memories
You might be surprised to learn that the first thing a futurist does is look back. There is a saying that history doesn't repeat itself, but it rhymes. So while you won't find an artificial intelligence revolution in any history book, you will certainly find technological disruptions.
Graham Norris is a much sought-after AI speaker who is often booked up months in advance due to his unique insights. Please get in touch with Graham as early as you can in your planning, so you can secure his presence at your event and get his input into your planning process.
There have been several landmark technological revolutions in history, including the Industrial Revolution (late 18th century), the Second Industrial Revolution focused on steel, electricity and heavy engineering (late 19th century), and the Digital Revolution starting around 1970 with computing and telecommunications. Each revolution fundamentally reshaped economies, societies and culture through new technologies and infrastructures.
Some general lessons we can learn from these revolutions:
Phased Development: Revolutions typically have an installation phase (disruptive emergence) followed by a deployment phase (maturation and widespread adoption). AI is likely to follow a similar trajectory where current early disruptive applications give way to broad integration.
Economic and Productivity Impact: Past revolutions significantly boosted productivity and growth while reshaping labor markets. AI could similarly boost economic growth and transform jobs, but the pace and extent depend on societal, governmental and regulatory responses.
Infrastructure and Scale: Past revolutions involved massive physical infrastructure (railways, factories). AI’s physical infrastructure — like data centers and semiconductors — is smaller but technologically intensive, and there are still many question marks over energy requirements.
Societal Adaptation and Ethics: Previous technological revolutions induced deep societal changes in how people communicate, work and govern technologies. Early ethical discussions around AI and proactive regulation aim to smooth AI’s integration and address potential negative impacts more swiftly than in past revolutions.
You will find more specific revolutions in human resources, the energy sector and pretty much any other dimension of your work. By studying them, you will uncover trends and countertrends relevant to today.
Fast forward
Only after exploring the past do we apply that knowledge to understanding the possibilities of the future. There are two elements: the future outside our control, and the future we can influence.
Outside of our control lurk uncertainties, and to manage these uncertainties, our minds like to make predictions. Here's one such prediction from 1960: "Machines will be capable, within twenty years, of doing any work a man can do." That was written by Herbert Simon, considered one of the pioneers of artificial intelligence. As with the case of most predictions, his was hopelessly wrong.
Far better to create scenarios, stories of possible futures that together give us a holistic view of what could happen. Let's use the example of AI and jobs in 2035.
Mass Displacement - AI eliminates more jobs than it creates, leading to widespread unemployment across sectors and growing economic inequality.
Job Transformation - Most roles evolve rather than disappear, with humans working alongside AI in complementary partnerships that increase productivity.
Skills Polarization - Labor market splits between high-skilled, high-paying AI-adjacent roles and low-paying service jobs that require human touch.
New Job Renaissance - AI creates entirely new job categories at such scale that employment actually increases, similar to how the internet created millions of previously unimaginable roles.
The only thing I can be sure of is that none of these will happen. The year 2035 will have some elements of some of these scenarios plus some elements I haven't thought of. But by thinking through a broader range of possibilities, you're much less likely to be be blindsided than you would by making a simple prediction.
The future within your control is your vision, the space where you can influence the future with your decisions. It is impossible to make decisions without having a clear idea of where you want them to take you. So, for AI specifically, you need to decide what you'd like artificial intelligence to be doing for your company or function in the next 6 months, 12 months, 2 years and 5 years. If a 5-year AI vision sounds ambitious, it should do. But without direction there is drift, and you should continuously revisit your visions to test them against reality.
Back to the present
Finally, we travel to the present, where the rubber meets the road and we make the decisions that make a difference. The key is to take action, because only then can you close the feedback loop. This means embracing the spirit of experimentation.
Experimentation is important because it eliminates the need to be right and the fear of being wrong. When you view your work like a scientist, it becomes a lot more interesting. Not every decision lends itself to experimentation – the multi-billion dollar factory needs to see a return – but the more decisions you take, the better they will become.
So what AI tools can you start experimenting with today? Sure, lots of AI applications aren't ready for mainstream adoption, and you'll definitely encounter a lot of dead ends. But every time you try something new, you get a chance to adjust your scenarios and vision, and increase your chances of making better decisions in the future.
Thinking strategically about the future
By learning from the past, imagining the future and taking action in the present, you put yourself in a position to make major strategic contributions to your company or function. The world is changing quickly, and there is no one better placed than you to figure out how artificial intelligence can best serve your corner of the universe. Are you ready to lean in to the future?
Want to be more futurist? Sign up for The DIY Futurist.
