AI’s Pace Of Change: Six Indicators You Are Too Slow

by · Forbes

AI is setting a pace of change that should terrify corporate leaders

AI startups are setting a faster pace of change than ever before. You know you are in trouble, said the legendary CEO Jack Welch, when the pace of change outside the company is faster than that inside. If that’s true, then the rate of growth of AI startups should be striking terror into corporate board rooms around the world.

I have been skeptical of the pace at which AI will convert its potential into an economic revolution. However, I do not think that is a reason for complacency. Now is the time to ask if we are moving fast enough to ride the wave when it comes or if we will be washed aside.

AI startups are scaling at unparalleled pace

AI startups are converting ideas into revenue at 10X the speed of previous generations with a fraction of the cost and far smaller teams. The old logic was that it takes a software startup anything from 3 to 5 years to get to $50M of revenue and another 5 to 7, to go to $1B. The AI generation is making this look sluggish.

Self-coding AI Lovable has posted $40M of annual recurring revenue in 5 months of trading on the back of just $7.5M of venture funding. Bolt’s numbers are roughly the same, $30M in 4 months, with just $7.9M. Both of which look like slackers compared with image generation company Midjourney, which scaled to $200M ARR without any funding and an initial team of less than 10. [MK1]

Given the billions of dollars corporations spend to keep up in the AI race, one would think they are keeping up. However, all the evidence is that most are struggling to convert playing with AI into tangible outcomes.

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Confidence is a problem

Managers get ahead in large corporations by projecting confidence and certainty. You reassure the board and senior managers by demonstrating you have a plan, that there is “alignment” between stakeholders, and that you will deliver “unique” advantages.

The problem with this traditional approach is that nobody is certain how AI will play out. Corporations have struggled to find solid use cases to convert the hype into revenue. Consumers have started to roll their eyes at promises of embedded AI in everything from mobile phones to personal computers.

It is time to admit we don’t know. We need to make a virtue of living with the uncertainty. That means lots of disciplined, small-scale efforts to learn what works, before converting it into the next big thing. Guessing how AI will deliver benefit and spending big on a master plan is a dangerous game.

Six Indicators your pace of change on AI is too slow

My colleague Michael Kaplun has been working on this problem. How do we know we are going fast enough?

I converted his more thoughtful work into six ugly errors that we see companies committing. If any of these apply, it’s time to get the skates on and figure out how to get to the head of the puck.

  1. AI project with a clear endpoint – that’s just a recipe for investing ahead of learning. Learn from how Goldman Sachs wasted $4 billion to find out that launching a new consumer bank takes more than deep pockets (compare their expensive failure with performance of Dave.com and Chime). Success will be determined by the pace of experimentation. How much can you learn and how fast? Learning takes discipline. Write down your critical assumptions – “what needs to be true” – test them, and record which were validated, which refuted.
  2. One big project – I don’t have a source for this, but I feel confident predicting that there are hundreds of thousands of AI startups in the world today. Yet we only know about Lovable, Bolt, and a few others. The failures are hidden from view. Inside a corporation you need multiple small-scale efforts, many different options that offer potential.
  3. Technologists are running the show – technology leaders have had a great ride, all the way back to Edison. As the internet ramped up, if you didn’t know your SQLs from your ASMLs, then you had no business leading a new venture. This is changing as software code now writes itself. Commercial leadership can come to the fore, potentially putting an even greater focus on business model innovation, as they find new ways to go after discrete market opportunities.
  4. AI projects are isolated from the customer base – a big point of defensibility for the incumbent organizations versus Lovable and the rest is that they have a customer base and means for reaching them. This could be good news for some slow-moving dinosaurs that otherwise look like their days are numbered. Consider gaming giant Caesars Entertainment. It stopped making any profits several years ago and is struggling to build a franchise in online gaming with only 7% share. However, it has a 65 million customers in its loyalty scheme. Could the lowering of the technology threshold help them to leverage this as a distribution asset?
  5. Data belongs to the siloes – the ages old story of not being able to wait for “corporate” to design a new CRM or ERP system is still going on today. Business units want to move faster and make sure that they get a system in the field quickly. People, solve the root cause not the symptom. You need to figure out how to connect data, integrate it, and leverage it for insights.
  6. Organization model is out of the twentieth century – the traditional organization model with a series of functions that take products from concept to market, order to cash, are going to change. The linear business process that underpins these distinctions will not survive in a world in which we can reconfigure business processes in minutes not months. We need to think about small pods of people working with AI to develop and launch products that are far shorter cycles than before.

This is just six big issues we are seeing out there as companies wrestle to turn AI’s potential into commercial reality. Hype cycles have a predictable path, and we are headed for the moment at which we all draw breath, realizing that the change isn’t as fundamental as we thought. Or at least we were before we saw what Lovable has achieved. The message is clear. We need to move faster.