Mind the Gap: Ulrich Störk

The expectation gap, where companies have high expectations for cost savings and productivity gains from AI but struggle to realize the full potential due to issues with data, workforce readiness and leadership mindset, is real, says Dr Ulrich Störk.

"In our discussions with clients – even discussions within PwC itself – there’s this initial excitement about how rolling out generative AI [genAI] will immediately cut between 30–50 percent of costs," Störk, also a Partner at PwC Germany and a member of the PwC Executive Team for the Europe, Middle East and Africa region, tells The CEO Magazine.

"There was a phase where companies believed: buy the tools and the value will follow."

He likens the technology to a new power tool that instantly makes everything faster and cheaper, where AI is a hammer looking for a nail without workforce readiness.

"A lot of people were investing millions, believing the sooner the savings are reflected in profit and loss, the better," he adds.

Reality check

However, if care isn’t taken to ensure the fundamentals are right, businesses are in for a costly reality check. Those fundamentals include proper workforce transformation, training and operational change.

"Someone in the C-suite is making the decisions, but it’s about reaching the people who actually need to implement it," Störk says.

For instance, as one of the largest users of Microsoft Copilot, if PwC had given employees access without training or process change, many might only be able to use it to summarize emails, missing the larger potential – and that’s where the danger lies. Access alone would mean very little without effective transformation programs and behavioral change.

According to Störk, when people don’t understand its greater value in driving innovation and productivity, they start to lose interest in using what is, in essence, just another tool.

What is also underestimated is just how fast AI is evolving.

"AI capabilities can multiply in just six months," he explains. "If your investment logic is based on today’s capabilities, it will be obsolete tomorrow."

That is, if companies plan based only on today’s use cases, they miss the bigger, near-future opportunities.

"People can be disillusioned or disappointed when they're not seeing the full impact of their investment because the world is changing so fast," he points out.

"Leaders need to have this foresight to build into their investment plans of what is coming tomorrow and the day after tomorrow."

The last piece? As much as they might want to roll out genAI, many organizations aren’t operationally ready for large-scale use. Störk uses the analogy of EVs: the push to buy an EV is there from the public, but without the supporting infrastructure, people can’t use their vehicles.

"Leaders are often surprised about what has been overlooked in the past decade or so internally that would have allowed the company to move to the next state of genAI use," he notes.

A data strategy

What makes Störk and his team particularly well-placed to guide clients along the AI journey is that PwC has experienced many of the same AI challenges its clients are facing already.

"When we advise our clients, so much of our relationship is based on trust," he says. "We are able to tell them that we have gone through the same journey, that we already know the pain points."

That first pain point is ensuring the foundations are in order. For Störk, successful AI implementation depends less on having the best genAI model and more on getting the basics right first.

"More than the models, what differentiates is the data you are using to feed these models," he says.

Especially considering that most large companies (including PwC) pull data from hundreds of disparate systems, a data strategy is crucial to ensure that the data you’re using is the right data – in other words, data that is clean and quality-checked.

The human factor

Then there’s the human challenge, Störk adds, or getting people to accept AI.

"I’m talking everyone from a personal assistant up to partners. It’s really a challenge because in every organization, people tend to look in the rearview mirror to parameters that guaranteed success in the past," he says.

"Even partners ask: will this make me irrelevant? The better question is: what new value can I create if AI takes over my admin burden?"

Störk explains that it’s something he has experienced himself inside PwC.

"If your company makes US$40 billion a year, a US$100,000 use case is irrelevant – cultural readiness and business model rethink are what matter," he reveals.

Practical initiatives that Störk has launched include empowering his team to set aside work time to explore and experiment with AI, launching a new initiative called ‘FridAI’ (pronounced ‘Friday’). Employees now devote Fridays to exploring its real-world applications.

"Everyone in my team has spent their Fridays to upskill and share knowledge with each other on leveraging AI, to drive efficiency with their work – and potentially develop client-relevant AI ideas," he says.

It’s an approach that builds excitement and motivates its people to explore and be comfortable with AI and its potential, he adds.

Leading through AI

To combat challenges around AI adoption, Störk believes leaders need to look through a variety of lenses.

"You need to think ahead of the curve and invest smartly," he advises. "If you do the investments just for today, you’ll probably have to do the same investment tomorrow – and the day after tomorrow.

"Thinking longer-term and mapping out the realistic solutions, as well as opportunities that AI can bring, with tactical strategies is a worthwhile investment."

Leaders also need to understand and address employee concerns, across all levels, from junior associates to managers and even senior partners.

"People are concerned about technology replacing their jobs and questioning what these technological shifts mean for them," he notes.

Störk cautions against short-term thinking in talent strategy.

"We need continuous, ever-evolving talent," he says.

After all, the goal is to have people work on creating value, while genAI is left to tackle the automatable tasks.

Of course, he appreciates that such an approach requires a level of optimism, confidence and trust, particularly in yourself as a leader, something that’s not always simple considering a growing fear among upper management that AI projects might make their own roles redundant.

Störk sees things differently, however.

"As AI becomes more powerful, human capital becomes more essential," he says. "AI is a must-have to keep economies competitive.

"GenAI is going to become a prerequisite to stay in the game in business."

Certain industries are more open to AI than others, particularly banking, insurance and retail. Showcasing projects with other clients in a similar industry or sector is a way Störk is able to convince leaders to move forward from theory to investing in AI.

"The next wave of differentiation won’t come from the best model, but from knowing how to use it in context – sector-specific AI agents, data ownership and culture," he says. "If you have a retail client, they want to see a solution that fits into their subsector."

Also aiding the cause is the ever-increasing speed from design to build of relevant platforms.

"You no longer need to wait. It’s become synchronized. It’s happening in real time in parallel," he explains.

It’s one thing to take those first steps, but Störk explains that leaders need to be prepared to make the commitment to the long game.

"It’s like training and going to a gym. It’s not simply going once to a gym, working out for a day and suddenly becoming a bodybuilder," he says, wryly.

"You can’t become a bodybuilder by going to the gym once. You have to show up, lift, adjust, repeat."

Small steps, in line with your capabilities, will avoid disappointment and quitting early in the game, he says.

"You need to go step by step," he cautions. "Leaving out even one step isn't a good idea. You need to go through every step of a transformation circle."