What does AI mean for your team?

Is AI coming for our jobs? The technology promises to automate routine work, make us more productive and fundamentally reshape how organizations make decisions. But while businesses race to integrate AI into their operations, a more complicated question is emerging: What happens to the people behind those operations? And how do you build a winning team when the skills, processes, and even the nature of decision-making are changing?

The thing that everyone is most afraid of, replacing employees, may not be a given. The future will more likely be about rethinking how we work. That was the consensus amongst our panelists; Todd Wilcox, CEO and deputy chairman of HSBC Egypt; Omar Khashaba, managing partner at Algebra Ventures; and Khalil El Bawab, CEO of local and regional markets at Beltone Holding.

AI is unlikely to trigger an immediate wave of job cuts. This is particularly true in highly regulated industries such as financial services but it will change the skills companies value and the way they train new hires. Regardless of the sector, companies will also need to grapple with how much responsibility they're prepared to hand over to machines.

AI can identify the problem. Humans still need to solve it. At HSBC Egypt, AI is already helping uncover operational problems that had previously proved difficult to untangle. Wilcox pointed to the bank's call center, which receives thousands of customer calls daily. For years, the bank had accumulated data from those interactions without being able to effectively identify the underlying causes of recurring complaints.

“We were able to build an in-house AI model to really look at all the data that we’ve gathered over the last five years and start to say, “What’s the root cause? Why are clients calling? What’s the impact?” and map it back to our processes, because we’ve put all the processes in that AI engine,” said Wilcox. The system was able to identify specific operational issues driving customer calls — including one process responsible for 5% of calls.

But there was an unexpected challenge: The team became so captivated by what the technology had uncovered that it risked losing sight of the actual objective: Fixing the problem. "There's the danger, I think, of getting so caught in the data and the AI mystique that you forget that we actually have a job to do," Wilcox said.

The lesson? AI can surface insights faster than ever before, but executives still need to challenge those findings, decide what to do with them, and take responsibility for the outcomes. "If you've got poor governance and no accountability, it simply leads to making bad decisions faster," Wilcox warned.

Are businesses really getting the most out of AI? For Khashaba, businesses are only scratching the surface of what AI can do. Using the technology to prepare for meetings or draft documents is useful, but its real potential lies in what he calls "systems of judgment." Traditional enterprise software — from Salesforce to SAP — has been designed to capture what happens within an organization. What it generally fails to capture is why decisions are made.

That missing context could become one of the most valuable assets a business has. Khashaba argued that organizations need to start recording not just decisions and action items, but the reasoning, discussions, and trade-offs that led to those decisions. He pointed to his own "Open Brain" system, which he developed to externalize his judgment, as an example of how AI could begin to access and build on accumulated experience.

The ultimate opportunity is to create an institutional memory that allows AI to understand how an organization exercises judgment — including the exceptions it makes, the customers it prioritizes, and the circumstances that influence its decisions. "That’s what experienced employees have that new hires don't. They don't have a different cognitive architecture. They just have a better understanding of how the organization as a whole exercises judgment,” said Khashaba.

But how much judgment should businesses actually delegate to AI? Khashaba thinks it should be a gradual process. Companies can progressively give AI more responsibility as they develop confidence in its outputs, while retaining human oversight and checks and balances.

For the two banking and financial services executives on the panel, accountability remains non-negotiable. El Bawab said Beltone views AI much like a new employee: It needs to be trained, supervised, and given clearly defined responsibilities before it can be trusted with more consequential tasks. It’s the difference between using AI to inform decisions and allowing it to make them. At Beltone, AI can help analyze stocks, funds, and other investments, but the ultimate investment decision must remain with the client. That caution is partly driven by regulation. El Bawab highlighted the sensitivity of financial client data and the restrictions governing where that data can be stored, arguing that adoption needs to advance at the same pace as regulatory frameworks.

Wilcox echoed the same concerns. While AI can uncover patterns and improve decision-making, executives remain accountable to shareholders, customers, and regulators. The two approaches aren't necessarily incompatible. Khashaba agreed that accountability must remain with humans, but argued that AI could gradually assume more decision-making responsibilities as organizations develop the systems needed to oversee it.

If AI does the work of a junior, how do companies train the next generation of leaders? One of the panel's biggest concerns was what happens to entry-level employees when AI takes over the tasks that traditionally taught them the business. Wilcox recalled learning banking by manually analyzing financial statements and conducting industry research — work that AI can now perform in a fraction of the time. According to Wilcox, those exercises weren't simply administrative tasks. They helped young bankers develop the foundational knowledge needed to eventually make complex decisions. "We're going to have to reinvent how we train new bankers," Wilcox said, pointing to the need for new internships and development programs.

And the skills employers look for are likely to change. Khashaba expects the labor market to continue rewarding generalists, as well as people with strong analytical and logical abilities who can think in systems. The challenge for employers will be ensuring that AI doesn't eliminate the learning opportunities employees need to develop the judgment that becomes more valuable as routine work disappears.

So, will AI mean smaller teams? Not necessarily — at least not by the end of 2027. Asked whether their organizations would employ more or fewer people than at the end of 2027, the panelists were broadly optimistic about headcount, even as they anticipated significant changes to job descriptions.

Wilcox expects HSBC Egypt's workforce to remain broadly stable, with employees shifting away from routine processing toward higher-value responsibilities. Khashaba similarly expects disruption to the nature of work rather than a dramatic change in overall employment levels. Beltone, meanwhile, could actually be hiring. El Bawab said digital transformation and digital onboarding are expanding the firm's ability to reach customers, creating opportunities for growth as automation improves efficiency.

And not every customer wants an entirely digital experience. “Gen Z and Gen Alpha probably don’t want to talk to a human being but high-net-worth and ultra-high-net-worth [clients] are definitely going to need the human element,” said El Bawab.

The takeaway: AI may become a permanent member of the team, but that doesn't mean the human team is going away. The competitive advantage will increasingly lie with organizations that can combine AI's speed and analytical capabilities with human judgment, accountability, and the ability to adapt.

Tap or click here to read the panel’s full transcript.