In my recent discussion on the AvePoint #shifthappens podcast, one area of discussion was around the fact that most of our current thinking on what AI can be used for is (somewhat) narrow. The fast-evolving nature of AI technology and capabilities, particularly in generative AI, makes it impossible to accurately predict future use cases based on current needs alone.

If you haven’t listened to the podcast and my conversation with Dux and Mario here’s the link or listen below.

In the conversations around artificial intelligence (AI) today, most people focus on its immediate, tangible applications—better customer service, faster data processing, or helping businesses streamline their operations. These are grounded in what’s possible right now, within the boundaries of today’s world. But AI’s true potential lies in its ability to reshape entire industries and create new ones we can’t even imagine yet.

Many technologists—and those commenting on AI from a general understanding—are tied to solving today’s problems. They focus on making things faster, smarter, and more efficient. But these use cases, while important, are incremental improvements. They’re the first steps on a much longer journey.

One reason we struggle to see the full scope of AI’s future is its rapid evolution. Think about large language models like GPT. Just a couple of years ago (Nov 2022 ChatGPT moment), it would have been hard to imagine an AI system writing complex essays, creating code snippets, or even holding meaningful conversations. But here we are. AI is helping engineers write software, creatives generate digital art, and knowledge workers draft entire documents in minutes.

Take the early days of the internet as an example. No one foresaw the rise of social media, e-commerce, or the gig economy. Companies like Google, Amazon, and Facebook didn’t simply optimise what already existed—they created entirely new ways for people to interact with technology and each other.

AI holds the same potential, but we’re only scratching the surface.

Beyond Incremental Improvements

Many of today’s AI applications are about improving what we already do. Companies use AI to make customer service more efficient through chatbots, personalise Netflix recommendations, or streamline business processes. These are useful improvements, but they’re not revolutionary. They’re simply making what we know a bit better.

In healthcare, for instance, AI is already helping doctors diagnose diseases or predict patient outcomes with greater accuracy. But this is just the beginning. Imagine a future where AI isn’t just diagnosing illnesses, but continuously monitoring our health in real-time—preventing conditions before they even arise. Or consider the possibility of personalised treatments tailored to our individual genetic makeup. This kind of future could completely redefine the way we approach healthcare.

Some of the most forward-thinking companies aren’t just focused on today’s use cases—they’re building for a future we can’t fully envision yet. Take OpenAI, the company behind GPT. Their goal wasn’t just to improve chatbots or help writers. They’re building general-purpose AI systems with the potential to be applied in ways we haven’t even thought of. They’re creating the foundation for a future where AI transforms industries in ways we can’t yet see.

Similarly, Tesla isn’t just using AI to make driving safer with lane-keeping or adaptive cruise control. Their goal is full autonomy—a future where cars drive themselves and transportation becomes a service, (think Waymo) not a product. If that vision comes to pass, it could upend our entire understanding of car ownership and mobility.

Disrupting the Status Quo

Just as the internet era brought massive change, the AI revolution will too. But this time, the impact could be even more profound. AI isn’t about centralising information like the early internet—it’s about decentralising capabilities. AI can now generate human-level work products in creative fields, coding, and even complex problem-solving. The question isn’t whether AI will disrupt industries, but how.

In education, for example, AI is already helping teachers personalise learning paths for students. But what if it could go further? Imagine fully adaptive learning environments that reshape education itself. Classrooms, textbooks, and rigid curriculums could give way to immersive, interactive experiences tailored to each student’s learning style and pace. I wrote about what AI could mean for education about a year ago when there was a lot of interest around the impact of AI on education.

Andrew Ng (Founder of the Coursera platform), a leading figure in AI, recently reminded us that we’re still in the early days of understanding AI’s full potential. He noted that even 15 years into the deep learning revolution, we’re still discovering new applications for AI. The same will be true a decade from now, as we continue to build on what’s possible.

“There has been a lot of hype about generative AI’s ability to transform industries overnight. Certainly, many industries — including education — will be transformed. But we’re about 15 years into the deep learning revolution, and we’re not yet done identifying and building useful deep learning applications. Despite the exciting progress to date with generative AI, I expect that a decade from now we will still be far from finished identifying and building generative AI applications for education and numerous other sectors.”

Coursera, for instance, is experimenting with AI tools like study coaches and course builders. These tools aren’t just about making education more efficient—they’re part of a broader vision for what education could become in an AI-driven world.

Beyond what is Known

The companies and thinkers who will lead the AI revolution aren’t just solving today’s problems. They’re looking beyond the limits of current systems and imagining what the world could be like. AI’s future is full of uncertainty, but that uncertainty is what drives innovation. The real breakthroughs will come from those willing to take risks, think beyond the present, and embrace the strange.

The most transformative AI applications won’t come from making today’s systems a little faster or a little smarter. They’ll come from creating entirely new possibilities. As Bowie once sang, we need to “turn and face the strange.” It’s time to stop worrying about what AI can do right now and start thinking about what it could do tomorrow.

Thanks for reading!

Some stats on most common use of gen AI across some common current scenarios (as of 2024).

From Harvard Business Review.
https://hbr.org/2024/03/how-people-are-really-using-genai

Technical Assistance & Troubleshooting (23%)

Content Creation & Editing (22%)

Personal & Professional Support (17%)

Learning & Education (15%)

Creativity & Recreation (13%)

Research, Analysis & Decision Making (10%)

Data from the Stanford Universities, Human Centred Artificial Intelligence Lab.
 https://aiindex.stanford.edu/report/

Work Trend Index Report – https://aka.ms/wti24

Three in four knowledge workers (75%) now use AI at work.
79% of leaders agree AI adoption is critical to remain competitive

59% worry about quantifying the productivity gains of AI

Over 90% of power users say AI makes their overwhelming workload more manageable and their work more enjoyable