Business Unusual
AI requires behavior change—and a new mindset. Uncomfortable? Yes, but it's achievable if you're innovating work v. simply iterating the old stuff.
AI this, AI that. It dominates today’s conversations, and many organizations are getting weary, or worse—stuck. Even SXSW this year was chock full of panels on AI, but few were as provocative as a keynote by Futurist Ian Beacraft, CEO of Signal & Cipher.
AUSTIN, TEXAS—Throughout history, we’ve always tried to make rapid tech change feel less like whiplash and more like something rooted in the familiar.
The first car was, literally, a horse-and-buggy cart fitted with an engine and steering wheel. The first TV show was a radio show with a single camera and tripod set up in front of the host. The first light bulbs were physical replicas of the gas lamp, and the internet began as a remote-access, community bulletin board.
But now with artificial intelligence? Futurist Ian Beacraft says optimizing versus innovating in this way is now too risky. More than 80% of company AI transformations fail because most work harder to optimize existing work systems than to reinvent the work, itself, “to achieve better.”
“The optimizers tend to stop after optimizing. But the more tomorrow’s tech collides with yesterday’s mental models, the more it will slow us down, or even get us stuck,” Beacraft told SXSW attendees here last week. “When we look at the world through the lens of the past now, it’s kind of like we’re navigating with the stars when we have a GPS in our pocket.”
Beacraft, in a talk entitled How to Not Screw Up an AI Transformation, suggested that everyone who’s focused on the future of work should consider these 5 things, for starters— and keep them front-of-mind.
1. The shelf life for skills is collapsing.
According to a recent survey by LinkedIn, by 2030—five years from now—70% of the skills required for the average job will have changed.
Beacraft calls this “skill flux.” In the 20th century, he says, we had the ability to lean on one skill that would carry us for most of our career. Tech knowledge, he said, “used to be valued for 30 years, but today? That longevity has fallen to around 2 years and the trend is for this cycle to continue to shorten.”
For example, those who know how to code are more efficient and effective but those who also know how to prompt are far more efficient and effective—and not because they got better, but because they also know how to use the new tools which are making us better.
2. It’s no longer enough to learn something new.
Beacraft says the war for talent now will favor those who can acquire and apply new skills quickly, in a model he calls “skills surging.”
Instead of ‘I am what I know’ we’re moving on to ‘I am how well I can adapt.’ Being fast at learning and applying that learning, and then being resilient enough to quickly learn something new again and applying it again and again to achieve better results “is going to completely change the way we go to work and get our education,” Beacraft says—and continuously make our traditional front-loaded education models increasingly obsolete.
The most valuable skill of the next 10 years? The ability to adapt, he says, will be “unlearning old habits quickly” and learning new ones at high speed.
3. Less is more.
In the past, businesses focused on accumulating big data—large volumes of information. But today? The biggest value, Beacraft says, lies in curating and structuring a company’s internal knowledge as something different, focused, proprietary and premium.
AI “has commodified almost 99% of all available human knowledge,” Beacraft says, so if all you do is tap into that, your company will just be average. Only AI models trained with highly refined information specific to its business can generate real competitive advantage.
Let’s say your company starts to use AI to codify its unique data, key value proposition, unique selling points, product details, purpose, audience, voice and tone—everything for project management and process— and then layers all of that on top of the large language models it has. “This can be a way to diffuse knowledge across the organization,” Beacraft says, to enable the speed of work to accelerate significantly. Doing this, for example, can help teams test new marketing ideas on AI-generated personas rather than get tied up in extra research, meetings and focus groups. Doing this also could enable anyone to write the next social media campaign in your company’s voice, or communicate with your company’s stakeholders in a way that is already approved and aligned. “It can take a lot of the friction out of up-skilling, alignment meetings, approvals and take CYA stuff out of the picture—what we call corporate waste, the stuff that gets in the way of actually doing the work,” Beacraft says.
“We spend 13% of our time looking for the documents we need to do our jobs. “That’s about 33 days of the year just to find stuff. We then spend another 37% of our time in meetings, talking about the work, moving the work forward, so that nearly half of our time is work that is about the work—but not doing the work.
“People think we need a lot of data to eliminate that,” Beacraft says, “but actually, we do not. We just need really small amounts of data that are well structured, clean and organized, and to start with low volumes of data to get high signal.”
4. AI success needs to be measured differently.
New metrics are required to reward capability and capacity rather than optimization and scale—as “we’re not looking at just predictability and output. We’re now looking at adaptability and resilience,” Beacraft says.
Old metrics “can be an enemy,” he adds, because if not careful, “they can eliminate the opportunity to spend time thinking creatively, pausing and assessing what is needed to make change.”
New metrics might include, for example, innovation scores for novel insights, how well new knowledge is integrated into the organization and for advances in the speed of learning and frequency of human/AI collaboration, among others.
5. Creative generalists and augmented teams
A combination of specialists and generalists augmented by AI will be the differentiator for the most innovative and progressive companies, Beacraft says. “The future isn’t about machines against humans but about humans empowered by AI, capable of rapidly reconfiguring their functions and attributes as needed.”
Today, Beacraft says, “we’re stuck with rigid work structures. So what if, instead of defining fixed roles, we designed teams dynamically, based on real-time challenges? With AI, this model is not only possible, but necessary in an era of rapid change.”
Next Steps
Beacraft urged attendees, when they return to work, to consider with their teams which processes are still serving the company, which things they know to be true and which things need to be investigated further.
Companies and organizations which treat AI merely as a way to cut costs will lose ground to those which use it to expand their human potential.
“The question is not whether AI will change your company but how you want that change to happen,” Beacraft says. Developing the mental flexibility to continually reassess which human capabilities to augment, which to automate and which to use as creative individuals will be important. Making those kinds of decisions, he urged, “needs to happen now.”
What are your thoughts about AI in the workplace? Please share them here, and thanks for reading!
Always feel smarter when I've read your articles.
Karl - Thanks so much! Means a lot. Thrilled to share clarity when/where I find it!