The good news: we've experienced this before. The rise of the internet, the introduction of smartphones, the shift to cloud computing... All these technological revolutions brought their own challenges, but also followed a surprisingly predictable pattern.
In this article, I share four important lessons from the past. Lessons you can use now to make smart choices about AI and other emerging technologies. Because why make the same mistakes that other entrepreneurs made twenty years ago?
Lesson 1: timing is everything
Each new technology goes through the same cycle, which is also described in the Gartner Hype Cycle, for example. The trick is to enter that cycle at exactly the right time. If you go 'all-in' too early, you will burn unnecessary money and energy. You still have to invent too much on your own and there is a high chance that your project will fail. If you wait too long, you will miss the opportunity to lead the way.
This is what the Gartner Hype Cycle looks like
Phase 1: Innovation Trigger
It starts with a technological breakthrough that gets a lot of attention. The media pops up and the first showcases appear. There is a lot of enthusiasm about the possibilities, even though there are hardly any useful applications.
Phase 2: Peak of Inflated Expectations
Expectations are growing even further. Investors are stepping in and the most innovative companies are launching the first projects, with the idea that the technology will solve all their problems.
Phase 3: Trough of Disillusionment
Then comes the inevitable disappointment. Initial implementations are disappointing, projects fail and investments do not produce the desired results. Media attention is declining and many companies are dropping out. This is the valley of disillusionment.
Phase 4: Slope of Enlightenment
But it's just after this disappointment that things get interesting. Organizations that persevere discover how they can make really valuable use of the technology. Expectations are becoming more realistic and the technology itself is maturing.
Phase 5: Plateau of Productivity
Ultimately, the technology reaches a stable level of maturity. The benefits have been clearly demonstrated and widely accepted. Implementation is becoming less risky and more and more organizations are getting in.
AI is currently at an interesting point
AI currently appears to be in the 'Trough of Disillusionment'. The first major projects have been launched and have failed to live up to sky-high expectations. But in the meantime, we do understand much better what problems AI can solve and where the limits lie. And the tools are also becoming increasingly mature. If we look at this cycle, it is likely that a period of healthy, stable growth is coming. A period in which companies are going to create real value with AI. And that is usually the time to step in.
Lesson 2: Always link new technology to your business goals
Many companies are embracing new technology because “everyone does it.” We saw that with the rise of the first websites and apps, and we are now seeing it again in AI. It often leads to expensive implementations that are difficult to justify afterwards. In the past, millions of tools and systems have been burned that did not serve a clear purpose.
Why do I want to implement this technology?
You always want to ask yourself that question. Keep asking questions until you reach concrete business goals. Do you want to make more sales? Limiting your costs? Managing risks better? Or leading the way compared to competitors? Doesn't the technology contribute to a concrete business goal? Then there is a good chance that you will invest in a solution without a problem.
Make your goals measurable
Make your goals concrete and measurable. Decide in advance how you will assess whether the implementation is successful. For example: in one year, we want to spend 30% less time on reports. Or: the time in which we answer customer questions should be halved.
In particular, evaluate the side effects
Goals are important to guide, but are you going to evaluate them? Don't focus on your pre-set goals and KPIs: innovation always leads you to unexpected places and sometimes the unexpected side effects are even more valuable than your original goals.
Lesson 3: resistance is part of it
When you innovate, you get resistance. That is a law. When the first web shops were built, it sounded: “This is never going to work, people want to be able to touch products.” When smartphones were introduced, there was disbelief: “People are really not going to type emails on such a small screen, especially on such a touchscreen without plastic keys.”
And this resistance doesn't just come from outside. You will also encounter doubts within your own organization and even in your own head. This is normal and part of the process.
How do you deal with resistance?
You have to be thick-skinned as an innovator. Accept that you won't get everyone along. Focus on early adopters, the people who are naturally open to innovation.
Use resistance as feedback too. Not to let go of your plans, but to sharpen them. What concerns are there? How can you remove it? Sometimes it is precisely the critical questions that lead to a better implementation.
The biggest mistake you can make
The worst thing you can do is wait for all resistance to disappear. Because at that moment, you are too late. And the negative effects of being late far outweigh the risks of starting on time.
Resistance is part of innovation. Think of it as a natural part of the process. And remember: if no one is critical of your plans, they may not be innovative enough.
Lesson 4: Prevent a trial and error approach
Especially within SMEs, I see that companies often innovate with a trial and error approach. They try out different tools, see what works and what doesn't. But does technology affect your primary business processes (and that happens with real innovation)? Then trial is fun, but error is absolutely undesirable. In that case, you want to use a thorough, strategic approach.
Start with a long-term vision
Start by formulating a clear long-term vision. Where do you want to be in five years? Perhaps you want AI to have taken over a large part of your financial processes. Or that your data analyses are fully automated.
Hit picket posts to your goal
Then work back to the present by hitting picket posts. Concrete intermediate goals that help you work towards your end goal step by step. For example: in one year, we will have collected all financial data in one central environment. In two years, our standard reports will be automated. And in three years, AI will perform the first analyses.
Be flexible, but stay on track
Of course, in practice, things often work out differently than you think in advance. You may not achieve some goals, while discovering unexpected opportunities along the way. That's not a bad thing. The most important thing is that you are structurally involved in innovation and that you always know what you are working towards.
Getting started
The history of technological revolutions is clear: it's not the strongest companies that survive, but the ones that adapt best. So start making your plan today. Because the future is coming faster than you think.
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