In 2006, it took Twitter two years to get to one million users. In 2009, Spotify reached that number in only five months. Just a year later, Instagram pulled it off in a mere 75 days. And in 2022, ChatGPT needed only 5 (!) days to convince a million people to use their product. Technology has always been a catalyst for transformation, but in recent years, its pace has accelerated to such a degree that it leaves many of us feeling overwhelmed. Close your eyes for a few seconds and a new tech trend probably popped up somewhere. Oh, look, robots can now see through walls (hello, privacy concerns). Wait, what do you mean when you say that two people communicated in their dreams? This acceleration only seems to continue since the launch of ChatGPT. The result is that decision-makers experience a mix of feelings that range from excitement to nervousness and even slight panic. They can grasp the immense opportunities for competitive advantage and reinvention but also struggle to keep up and fail to capture value. 

This tension is perhaps one of the greatest paradoxes of our time. According to BCG, 90% of executives ranked AI as one of the top three priorities for their business in 2024. 80% expected that AI and generative AI will radically transform their business in just three years’ time. On the other hand, 80% of the very same people are not yet uncovering new ideas with AI. And two-thirds of them are dissatisfied with any progress that they are making.  

The sum of this paradox and the accompanying statistics have led to polarised and biased viewpoints. Is AI a risk to humanity’s very existence? Will AI’s replace us or our jobs? Will we end up in a wildly dystopian society? Or, inversely: AI is just a fad that will end “not with a bang but a whimper”. Just like some people (not David Bowie, though, he saw it coming) were convinced that the internet was merely a hype 20 years ago, some people will make you believe the same about AI. Lovers and haters are locked in a fiery tango, while the moderate voices seem stuck in the corner like wallflowers. 

Hype, blind panic or…the next platform shift?

On the one side of the spectrum, we have an almost religious belief in technology and, on the other, a blind Beaker-like panic. Both aren’t productive. We need pragmatism: rather than thinking of AI like an overwhelming tsunami, let’s see it as ‘just’ a platform shift. A new technological wave does not upend everything that existed before. Rather, it finds its place, replaces some things and then co-exists in other ways. Smartphones did not replace PCs, they continue to live side by side. Similarly, AI platforms will not replace the regular web or immediately kill smartphones and PCs. But, AI will fundamentally change the technological landscape and how we interact with it.

As Westerners, we tend to look at paradoxes as an either/or situation. One is true, the other false. One good, one bad. One right, the other wrong. The Chinese, on the other hand, believe that both can be true at the same time. A crisis can also be a catalyst for opportunity. AI can be a hype and something to worry about and an enabler for business value and differentiation, all at the same time.

But how can you make sense of this age of technological acceleration and better navigate its opportunities? I have always found that in complex situations, frameworks are a great help for filtering out the noise and thinking strategically about creating value for your organisation and your customers. That’s why I listed four of my favourite ones that seem highly relevant to our current times:

1. The technological S-curve
2. The ambidextrous organisation, seasoned with a little Three Horizons
3. The Tech-Business-UX Triangle
4. Automate vs. Augment with AI

S-curvelicious

Yes, technology is accelerating at warp speed. But despite this continuous acceleration, technological advancements keep following the same predictable pattern. First, you have a longer period of slow adoption. Then, there is an inflection point (in the case of smartphones, that was the famous iPhone moment) which morphs into a fast hockey stick curve before reaching a plateau at the end, where everything slows down again and makes room for the next shifts to happen. Combine this start-middle-end in a single visual, and you will see an S-curve. Upon looking at AI’s S-curve, we can probably say that ChatGPT was the inflection point for this platform shift, though we might need some more distance to really know that.

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The reassuring part is that technological adoption follows a pretty predictable path. The challenging element is that our brains are differently wired to think about the future. They are linear “machines” who can’t compute exponentiality. Futurologist Roy Amara explained it like this: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” It happened to Nokia and Microsoft with smartphones. They were at the top of their game, generating a tremendous amount of revenue and were quick to dismiss something that was too niche, according to them. It’s a perfect example of the innovator’s dilemma, where companies focus on improving existing products for their current customers and ignore the emerging, lower-cost or simpler technologies that will eventually overtake their markets.

When you’re at the bottom, slower part of the curve, it’s really hard to imagine where you will end up and what your true potential is. People tend to extrapolate the limitations of the old paradigm at the beginning of a new one. Like when the first radio shows were merely recorded theatre plays. Only when we get used to a new technology and start to play with it do we see native patterns emerge. The first mobile apps were all things that you could do on a pc: weather apps, calendar apps, email apps. All very underwhelming.

Here's the interesting part, though: we tend to look at new technologies from an individual perspective, while we should be really asking ourselves “What does this mean if everyone has it?”. So not: “What if I am connected to the Internet”, or “What if I have a 24/7 connected device close to me”, but rather: “If everyone is 100% connected, could they stream music rather than owning it?” or “If everyone has a smartphone and we connect them through one app, can we put real-time traffic information in there?”. 

Let’s think about what that would be in the AI s-curve. Currently, most of us are thinking in terms of the individual, asking ourselves “How can ChatGPT help me, or my company?”. It’s very single-use case focused. We should ask ourselves what will happen if everyone uses GenAI and each of our personal or organisational AI’s will start to interact with each other. That will be the true platform shift.

A B2C example here could be the case of agent-to-agent communication. In case you’re not familiar with them, AI agents are systems that perceive their environment, make decisions, and take actions autonomously to achieve specific goals. You’ll find a piece about them later on in this Shift report. Take the practical example of planning a weekend trip with your friends, deciding about location, restaurants, places to visit, etc. Today, we usually do that in a WhatsApp group and there tends to be quite some delay between answers, making this a relatively slow process. 

If we all had individual agents - that knew our preferences, our agendas, etc. - talking to each other, that would go much quicker. And then, when the decision about the what, when and how is made, one super-agent could start contacting location agents, hotel agents, etc. and those could propose discounts, suggest fun and unexpected things to do, perhaps even bring you in contact with people in neighbouring holiday houses with the same profile if you’re a group of single friends, etc. You’d have one gigantic network of autonomous agents suggesting things that you, as an individual or even your individual friend group, would have never thought of alone. It would create some type of artificial serendipity that emerges from the sum of all the agent parts.

So that’s what S-curves can do for you in fast-moving times: they offer the quiet assurance that - though change keeps accelerating - there is predictability in the adoption pattern of new technologies. We just need enough imagination to understand which native applications the new platform will bring. 

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Exploring and exploiting

100% clean slates are non-existent. A new business model, technology, platform etc. will never radically and completely displace an old one from the start. There will always be a point where old and new co-exist. It’s the percentages and the balance that change. Because when the new needs to mature and adoption needs to take up, revenue still must be made on a scale, usually through exploiting current business models.

Second, old solutions can offer value in some places that the new ones cannot (yet). The most obvious example is that smartphones did not replace PCs and that both were further built on the web. Generative AI in all its forms and XR will probably not replace PCs and smartphones and will also further build on the web. But there will be native devices and applications that will surface. We have a piece about that, too, in this report, by the way, so stick around.

Finding balance is key. That’s why, in the vision of the ambidextrous organisation, companies need to find a delicate harmony between exploiting and exploring. They have to exploit their core business, because that is where the current customers and revenue are. But they also need to keep exploring new emerging technologies, products and services because that is where their future customers will be. If they don’t explore the edges, they will arrive at a point where most of their customers will switch to companies that do.

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One of the easiest approaches to finding the right balance between old and new is McKinsey's Three Horizons Framework, which suggests that companies divide their current budget and attention over three separate buckets:

1. 70% should be dedicated to the core (Horizon 1) – delivering value in the now, and 1 to 2 years out
2. 30% should be dedicated to emerging business (Horizon 2) – delivering value in about 3 to 5 years
3. 10% should be dedicated to truly disruptive business (Horizon 3) - delivering value in the long term, after more than 5 years

Lego proved to be exceptionally savvy at ambidexterity when they decided to win back their adult customers with more complex and mature models targeted at popular fandoms like Star Wars, Lord of the Rings and Batman. Or at more technical adult audiences with cranes, Mars Rovers or complex architecture like the Eiffel Tower. At the same time, they catered to the younger audiences with a different approach. And they surfed the digital wave with a wonderful and very popular Lego community. 

Microsoft is an even more intriguing example. They completely missed the mobile wave, but it seems that they are deeply committed to the AI curve. Its investments in OpenAI are the most obvious, but even since the people drama at the latter (when Altman was briefly ousted from his own company), it is clear that they are rearranging their eggs in other baskets too. Like those of Mustafa Suleyman’s (ex-Deepmind) Inflection AI (acquired for $650) million and Mistral AI ($16 million investment). If a company misses the boat of a platform shift, it can always make a comeback, if it’s big enough. But please don’t try this at home.

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Finding the sweet spot

A third framework that’s exceptionally useful in times of acceleration is the one issued from the domain of product management, which helps find the sweet spot between technology, user and business:

The framework illustrates that it’s the job of product managers to use technology, identify customer needs and capture business value to build a product that sits right in the middle of these three circles. The framework deeply underscores the importance of looking at challenges in a cross-functional way - bringing together business strategists, behavioural experts, designers, marketeers, data specialists, etc. - and conveys how crucial it is to incorporate multiple perspectives in every product strategy. 

Especially in these times of AI hype, people tend to forget that technology is only one part of the equation and frankly not even the most important. It’s only an enabler, a driver. “Why are we doing this?” “Who are we doing it for?” “How will this make our business more successful?” These are crucial questions that have more to do with CX, marketing, design, business strategy, etc., than with technology. They’re about improving customer and business value. Only if you can answer them, will you be able to create a strategy that is true to your company and will set you apart from your competitors.

One of the most common mistakes during tech hypes is lack of strategy. A beautiful example is that of media companies at the onset of the web. Many put all their articles for free on their website for the early adopters, firmly believing that printed content was their core business model and that would never change. It did, of course, and it was no longer an option to put everything up for free. But the readers were used to not paying, just like they did not pay for social media, so it was very difficult to come back from that. And they were hungry for web native services, rather than just digitisation of the paper model.

Automate, or augment?

The last framework I would love to share here, is an AI-specific quadrant. It’s partly a mix of the frameworks described above, but it’s specifically helpful in thinking about AI use cases inside your organisation.

The horizontal axis explores the whole spectrum between automation and augmentation. As I pointed out earlier, new technology waves rarely fully replace previous technologies but tend to co-exist and augment. So this is where you ask yourself the question “Will this technology automate certain tasks - taking humans completely out of the equation - or will we use AI to augment existing tasks”? The vertical axis, then, is about possibilities: will AI help you further exploit your current processes - make them more efficient, or faster - or can it help explore new opportunities and new business lines? 

The total sum of the framework offers you 4 scenarios for using AI in your company. Where you either radically reinvent your business or merely incrementally outsource it to AI. And where you supercharge your work with AI, or more boldly, deeply reimagine it. It’s a useful framework for finding out how incremental or radical AI could be used and what that could mean for your own organisation and your customers.

In a world where the pace of change feels dizzying, frameworks are great tools for turning the chaos of acceleration into opportunities for strategic innovation and growth. The s-curve teaches us to anticipate the natural rhythms of adoption and disruption, while ambidextrous thinking helps us balance the old and new to maximise value. The product management circles, then, remind us that technology alone isn't the answer—it’s about serving users, solving real problems and creating value for your company. Finally, the AI quadrant challenges us to decide whether we need an incremental or a more radical approach, both business-wise and human-wise. 

The age of acceleration may sometimes feel overwhelming, but above all, it's a time of unprecedented potential. The way you react to it will make all the difference.

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This article is a part of our Shift 25 report. Stay tuned as we release more articles in the coming weeks. For immediate access to the complete digital report, request your copy here.