As artificial intelligence continues to redefine industries, it is also reshaping the very structure of the workforce. Automation, machine learning, and AI-powered decision-making are transforming traditional roles, creating new opportunities while rendering some job functions obsolete.
At “Startup Bazaar: InnovateAI,” hosted by The American Bazaar in Vienna, VA, on January 31, industry experts explored the characteristics that will define the next generation of change-makers. Their insights painted a picture of a future where leadership is no longer about simply managing people and processes but about navigating an intelligent, data-driven economy. Leaders who embrace AI-driven decision-making, prioritize ethical considerations, and foster a culture of innovation will be best positioned to thrive in this new era.
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Moderated by Dr. Krisztina Domjan, senior professorial lecturer at American University, the panel featured Seema Alexander, co-founder of Virgent AI; Sheena Gill, President, Americas at CognitiveCare Inc.; Muir Macpherson, Partner, Human Capital Solutions, at Aon; and tech executive Sudhir Menon.
While AI is often perceived as a disruptive force threatening jobs, the panelists emphasized its role in augmenting human capabilities rather than replacing them.
Kicking off the discussion, Menon set the stage by outlining the complementary strengths of AI and human intelligence. “When you look at AI per machine, what does it give us? The ability to look at a vast amount of data, identify patterns, and be able to do things on a large scale, whereas when you look at the human factor, the nuanced understanding, the emotional intelligence, the creative problem solving, you’ve got to mix the two, because one cannot replace the other or vice versa,” he explained.
He stressed the importance of adaptation, warning that workers who fail to embrace AI tools risk falling behind: “The earlier you understand and you enhance your skills to match them, the better.”
Alexander, co-chair of the DC Startup & Tech Week, reinforced this point with a striking statistic from the World Economic Forum’s 2025 Job Report: “92 million jobs are going to be lost, 170 million are going to be gained. So that’s a net of 78 million jobs that are going to be gained.” She highlighted AI agents as a game-changing development: “This is to me one of the biggest future virtual workforces, and that’s going to become augmentation for humans if we do it properly in certain companies.”
Comparing AI agents to the early days of the internet, Alexander described how they will streamline repetitive tasks, allowing employees to focus on higher-value work. “We’re going to have millions of AI agents doing narrow tasks and collaborative tasks,” she said. “The way we’re framing it to executives is you can use that to help your employees with productivity and efficiency and get them focused on things that are more strategic, more creative, more partnership-related, the things that they wish they could do.”
Alexander emphasized that the concept of AI agents is “the present and future of the workforce.”
Bringing an economic lens to the discussion, Macpherson pointed to historical parallels where automation expanded rather than reduced job opportunities. “Translators—we’ve had good machine translation for at least a decade now, and there are as many translators as there were before, if not actually more. So, Bureau of Labor Statistics thinks that demand for them is going to increase faster than the average job.”
He drew a similar comparison with ATMs, which were once feared as job killers for bank tellers but actually increased employment in the banking sector. “The ATM made it cheaper to open bank branches because you didn’t have to have a full set of people,” he explained. “So, what we got is more bank branches.”
Macpherson emphasized that the key to job retention in the AI era lies in demand elasticity—where industries that can lower costs with AI will likely see growth rather than layoffs. “If you lower the price of this thing, is demand going to increase a lot? So if you can make translation really cheap or free, are you going to see a big increase in demand for that? Yes, you are,” he said.
On the future of coding, Macpherson pushed back on the idea that AI will replace software engineers: “We heard earlier that coding is going to go away at some point. That may be true. I think in the short term, we’re not going to see a decrease in demand for software engineers. I think we’re going to see a stable, if not growing, demand for software engineers because with AI as a copilot, those engineers become vastly more productive.”
Bridging the AI education gap
Addressing widespread concerns over AI-driven job losses, Dr. Domjan noted a major issue: “One of the reasons why parents are scared, students are scared, and faculty are scared [is] because there is no mention of, well, don’t worry. You just cited the statistical information. Other media will say and stop at the elimination, and then there’s no follow-up.”
Gill underscored the need for adaptability in education and career paths: “Even if you choose to study two fields and one of them isn’t computer science, you’re still learning that skill of adaptability. If you can go into the workforce with that mindset that you can learn more than one thing, you will be ready for the AI-driven economy.”
Gill pointed to her own interdisciplinary background—having transitioned from computer science and mathematical modeling to law and healthcare AI—as an example of how professionals can blend expertise across domains.
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Building on this, Alexander emphasized the importance of AI literacy at an early stage, stating: “It’s not AI that’s going to take your job. It’s the people who know AI that are going to take your job.”
She referenced her conversations with education leaders, including a recent State Department presentation and discussions with American University in Bahrain, about integrating AI into high school and college curricula. “I think part of the conversation was, what are we teaching our children at college and high school levels now, and what does that policy look like so they start to get exposed to AI?” she said.
Proactive adaptation
The panel made it clear that the future of work is not about obsolescence but evolution. AI is not an existential threat to jobs—rather, it presents an opportunity for augmentation. Those who embrace AI as a collaborative tool will be well-positioned for the future.
Alexander summed it up best: “Part of this opportunity is identifying what are those new roles, right? Because there are some that we know and then some that we don’t know yet, right? So it is a very interesting moment in time, but I think the biggest thing is upskilling people on how to use the tool sets so that they can do even better and be more productive in the jobs that are there today.”
Menon highlighted the glaring gap in AI literacy, even among tech professionals. “If I ask just this room what is AI and the value of AI, I’m sure I’ll get a hundred answers. Now you go to the rest of the world and ask them, what is AI?” he posed. Menon stressed that AI education needs to be foundational, involving not just the corporate and media sectors, but also political leaders. “People have to follow ethics at every level, not just corporates or media, right? People in politics, everyone has to follow a level of ethics so that we are all looking at AI with the same lens,” he stated, emphasizing the need for a shared understanding of AI’s potential and limitations.
Cross-sector collaboration
The discussion then shifted to the significance of partnerships across industries. Gill provided a compelling example of how her company partnered with the L.V. Prasad Eye Institute in India to develop AI-driven risk prediction models for pediatric myopia and retinopathy of prematurity. “You might think, why would a company in data science partner with an ophthalmology organization? But that partnership has proven to be so strong, and we’ve learned so much about ophthalmology, and they’ve learned so much about data science,” Gill explained. She argued that such collaborations are vital for developing meaningful AI solutions, rather than operating in isolation.
Alexander reinforced this perspective, citing the example of Station DC, an initiative designed to foster government, public, and private sector partnerships. “There’s almost like an urgency, like World War II urgency right now in terms of who wins the AI race,” she said. Alexander noted that under the current administration, these collaborations will likely increase, driven by the need for both innovation and responsible AI adoption. She emphasized that AI literacy must extend to executive leadership. “The CEOs and the executive teams not only need to understand AI, but understand the foundations of how this technology works, because if they’re not educated and they’re just relying on their CIOs or CTOs, they’re not going to really understand how to innovate with this stuff or leverage it with their employees.”
Retraining vs. New career paths
A central theme of the discussion was the evolution of the workforce in response to AI. Macpherson provided a data-driven perspective, explaining that the choice between retraining employees or creating new career paths depends on job roles and skill overlaps. “In terms of thinking about that retraining versus rehiring, it’s gonna depend on that skills overlap,” he noted, referencing cases where AI augments existing roles versus those where entirely new skills are needed. He pointed to radiologists as an example of workers who can collaborate with AI rather than being replaced. “That radiologist is gonna easily be able to pick up that additional skill because it’s being used to complement their existing skills.”
Menon advocated for introducing a structured approach to AI adoption, drawing parallels with structural engineering. “When you have to construct a building, you have requirements, you have to build a footprint. From there, you need certain approvals. Software engineering has not followed those rules at all,” he stated. He argued that AI presents an opportunity to bring discipline and best practices to software engineering, ensuring that companies adopt AI for strategic reasons rather than competitive pressure. “I go to companies and ask, why do you need AI? And they say, ‘My competition is doing AI.’ That’s not an answer. You need to have a structured way of understanding why you need AI and where AI can happen.”
Alexander added that companies should focus on high-impact AI implementations rather than adopting AI indiscriminately. “Whatever it is, I’ve heard 65% to 75% of AI projects fail. For us, it’s start, focus on the functional area, then the product area, then the innovation area. Go in, test and prototype, and then part of where you get buy-in from leadership is when you show them, not tell them,” she explained.
The role of soft skills
While much of the conversation centered on technical skills, Gill cautioned against overlooking the importance of soft skills. “When we talk about upskilling, we’re not talking about only technical upskilling, hardcore coding skills. I actually believe that for many of the use cases that we’re talking about here, the soft skills are going to be really, really important,” she said. She illustrated this point using maternal health AI applications, where engagement with users can make or break a tool’s effectiveness. “For example, as part of your solution, let’s say you have an engagement solution, you’re asking somebody, do you have food security? The person may not answer truthfully, and then your algorithm gets screwed because you’re not getting the truthful information.”
Gill concluded with a powerful assertion about AI’s role in the economy. “I know we call this panel the AI-driven economy, I even said AI-driven economy, but it’s not necessarily that the economy is going to be AI-driven. It’s the human imagination that’s going to create these amazing solutions, and AI is going to help facilitate that. But we can’t lose sight on the human trust that is so key to all of this.”
Visionary leadership
Alexander emphasized that while the core role of a CEO as a visionary and innovator remains unchanged, future leaders must possess a deeper understanding of emerging technologies. “So much of what we once thought was impossible is now possible,” she noted, stressing that technological literacy will be essential for leaders to drive meaningful innovation. Rather than merely acknowledging AI’s presence, leaders must grasp its foundational capabilities and integrate them into their strategic decision-making. This understanding will not only shape company culture but also influence product development, employee engagement, and operational efficiency.
Macpherson underscored the importance of rethinking traditional business structures in response to AI’s transformative potential. Comparing AI to past technological revolutions like electricity and the automobile, he noted, “Right now, we’re still plugging AI into old systems, but the real transformative [breakthroughs] will come when we rethink everything from the ground up with AI in mind.” He suggested that industries will need to reorganize themselves to fully leverage AI’s capabilities, much like factories had to restructure when transitioning from steam power to electricity.
Ethics and AI-first business
Ethics was another key point discussed, and panelists agreed that, with AI’s rapid expansion, ethical considerations will play an increasingly crucial role in leadership. Menon cautioned that while AI opens up limitless possibilities, it also presents significant risks. “AI can break all ethics in the process,” he warned. “Leaders have a moral and ethical responsibility to the world. They must remain loyal to that responsibility, ensuring that AI is used for good.” He called for global regulations that would hold leaders accountable for how they handle data and implement AI-driven innovations.
Alexander echoed this sentiment, highlighting the importance of balancing automation with human oversight. “We need to ensure that AI remains an augmentation tool rather than a replacement,” she said, advocating for a “human-in-the-loop” approach to AI-driven decision-making. While AI will optimize efficiency and enhance personalization, human judgment will remain a critical factor in ethical and strategic choices.
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As AI continues to shape the business landscape, companies that fail to adapt may find themselves obsolete. “Eight out of ten startups today are AI-first businesses,” Alexander observed. “They may not always be strategic, but they are built with AI at the core.” She compared this shift to the rise of internet-based companies like Google and Amazon, which leveraged new technology to redefine their industries, while others, like Kodak and Yellow Pages, struggled to keep up.
For businesses to thrive in this AI-driven future, Alexander outlined three key principles: foundational intelligence, proprietary data as currency, and strong brand equity. “Every business will need some level of AI intelligence as a baseline. Data will be the key competitive differentiator, and brands that maintain trust and authenticity will be the ones that succeed.”
Gill concluded the discussion by emphasizing the importance of purpose-driven leadership. “If you’re starting with dollar signs or trends, you won’t go far,” she stated. “The leaders who succeed will be those who begin with the ‘why’—why they are solving a particular problem and how their innovation improves lives.” She stressed the need for interdisciplinary collaboration, adaptability, and a commitment to creating meaningful impact.
As AI continues to evolve, the next generation of leaders will not only need to be technologically adept but also ethically responsible and purpose-driven. Those who can harness AI’s potential while remaining committed to human-centric values will be the ones to redefine the future of business.