AI drives workforce transformation as leaders rethink talent management and restructuring strategies

AI has redefined the talent game. Here's how leaders are responding.

As AI continues to reshape how we work, organizations are rethinking what skills they need, how they hire, and how they retain talent. According to Indeed's 2025 Tech Talent report, tech job postings are still down more than 30% from pre-pandemic highs, yet demand for AI expertise has never been greater. New roles are emerging almost overnight, from prompt engineers to AI operations managers, and leaders are under growing pressure to close skill gaps while supporting their teams through change. Shibani Ahuja, SVP of enterprise IT strategy at Salesforce; Matt Candy, global managing partner of generative AI strategy and transformation at IBM; and Jessica Hardeman, global head of attraction and engagement at Indeed came together for a recent roundtable conversation about the future of tech talent strategy, from hiring and reskilling to how it's reshaping the workforce. Strategies for sourcing talent To find the right candidates, organizations need to be certain their communication is clear from the get-go, and that means beginning with a well-thought-out job description, Hardeman said. "How clearly are you outlining the skills that are actually required for the role, versus using very high-level or ambiguous language," she said. "Something that I also highly recommend is skill-cluster sourcing. We use that to identify candidates that might be adjacent to these harder-to-find niche skills. That's something we can upskill people into. For example, skills that are in distributed computing or machine learning frameworks also share other high-value capabilities. Using these clusters can help recruiters identify candidates that may not have that exact skill set you're looking for, but can quickly upskill into it." Recruiters should also be upskilled, able to spot that potential in candidates. And once they're hired, companies have to be intentional about how they're growing talent from the day they step in the door. "What that means in the near term is focusing on the mentorship, embedding that AI fluency into their onboarding experience, into their growth, into their development," she said. "That means offering upskilling that teaches not just the tools they'll need, but how to think with those tools and alongside those. The new early career sweet spot is where technical skills meet our human strengths. Curiosity. Communication. Data judgment. Workflow design. Those are the things that AI cannot replicate or replace. We have to create mentorship and sponsorship opportunities. Well-being and culture are critical components to ensuring that we're creating good places for that early-in-career talent to land." How work will evolve along AI As AI becomes embedded into daily technical work, organizations are rethinking what it means to be a developer, designer, or engineer. Instead of automating roles end to end, companies are increasingly building AI agents that act as teammates, supporting workers across the entire software development lifecycle. Candy explained that IBM is already seeing this shift in action through its Consulting Advantage platform, which serves as a unified AI experience layer for consultants and technical teams. "This is a platform that every one of our consultants works with," he said. "It's supported by every piece of AI technology and model out there. It's the place where our consultants can access thousands of agents that help them in each job role and activity they're doing." These aren't just prebuilt tools -- teams can create and publish their own agents into an internal marketplace. That has sparked a systematic effort to map every task across traditional tech roles and build agents to enhance them. "If I think about your traditional designer, DevOps engineer, AI Ops engineer -- what are all the different agents that are supporting them in those activities?" Candy said. "It's far more than just coding. Tools like Cursor, Windsurf, and GitHub Copilot accelerate coding, but that's only one part of delivering software end to end. We're building agents to support people at every stage of that journey." Candy said this shift leads toward a workplace where AI becomes a collaborative partner rather than a replacement, something that enables tech workers to spend more time on creative, strategic, and human-centered tasks. "This future where employees have agents working alongside them, taking care of some of these repetitive activities, focusing on higher-value strategic work where human skills are innately important, I think becomes right at the heart of that," he explained. "You have to unleash the organization to be able to think and rethink in that way." A lot of that depends on the mindset of company leaders, Ahuja said. "I can see the difference between leaders that look at AI as cost-cutting, reduction -- it's a bottom-line activity," she said. "And then there are organizations that are starting to shift their mindset to say, no, the goal is not about replacing people. It's about reimagining the work to make us humans more human, ironically. For some leaders that's the story their PR teams have told them to say. But for those that actually believe that AI is about helping us become more human, it's interesting how they're bringing that to life and bridging this gap between humanity and digital labor." Shifting the culture toward AI The companies that are most successful at navigating the obstacles around successful AI implementation and culture change make employees their first priority, Ahuja added. They prioritize use cases that solve the most boring problems that are burdening their teams, demonstrating how AI will help, as opposed to looking at what the maximum number of jobs automation can replace. "They're thinking of it as preserving human accountability, so in high-stakes moments, people will still make that final call," she said. "Looking at where AI is going to excel at scale and speed with pattern recognition, leaving that space for humans to bring their judgement, their ethics, and their emotional intelligence. It seems like a very subtle shift, but it's pretty big in terms of where it starts at the beginning of an organization and how it trickles down." It's also important to build a level of comfort in using AI in employees' day-to-day work. Salesforce created a Slack chat called Bite-Sized AI in which they encourage every colleague, including company leaders, to talk about where they're using AI and why, and what hacks they've found. "That's creating a safe space," Ahuja explained. "It's creating that psychological safety -- that this isn't just a buzzword. We're trying to encourage it through behavior." "This is all about how you ignite, especially in big enterprises, the kind of passion and fire inside everyone's belly," Candy added. "Storytelling, showing examples of what great looks like. The expression is 'demos, not memos'. Stop writing PowerPoint slides explaining what we're going to do and actually getting into the tools to show it in real life." AI makes that continuous learning a non-negotiable, Hardeman added, with companies training employees in understanding how to use the AI tools they're provided, and that goes a long way toward building that AI culture. "We view upskilling as a retention lever and a performance driver," she said. "It creates that confidence, it reduces the fear around AI adoption. It helps people see a future for themselves as the technology evolves. AI didn't just raise the bar on skills. It raised the bar on how we're trying to support our people. It's important that we are also rising to that occasion, and we're not just raising expectations on the folks that we work with." Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they're always clearly marked. For more information, contact [email protected].

AI Is Creating New Winners and Losers. Here's How Smart Leaders Are Restructuring to Get Ahead.

Learn how forward-thinking leaders are reimagining roles and teams as intelligent systems reshape the future of work. In 2025, the tech industry finds itself caught in a paradox. On one hand, we're witnessing an AI gold rush. Companies are investing billions, betting that artificial intelligence will unlock the next wave of innovation. Meanwhile, over 22,000 tech workers have already been laid off this year -- 16,000 in February alone. Apparently, this signals a turning point in how companies structure teams and allocate talent. Intelligent systems are redefining how teams work, which skills are gaining value and where human roles still matter. It's not simply about whether AI is causing the layoffs. What matters is how firms respond. As layoffs accelerate across the tech industry, leaders now face a choice: restructure with purpose or fall behind. Tech industry layoffs have become a defining feature of AI-driven transformation since 2022, and the trend hasn't slowed. Microsoft is cutting 9,000 jobs, following earlier rounds this year. HP is reducing its workforce by 2,000 people in October, expecting to save nearly $300 million. At first glance, these moves resemble classic downsizing during economic uncertainty. But profitability doesn't exempt companies from resetting. SAP, for example, despite strong performance, is letting go of up to 10,000 employees. The company is flattening management, consolidating teams and rebuilding platforms for AI-driven operations. Even younger firms are following suit. Scale AI, a major player in model training, recently laid off 200 employees and 500 contractors, just weeks after closing a $14.3 billion deal with Meta. Look closer, and a pattern emerges. As firms rebuild around AI, roles tied to legacy systems, siloed processes or repetitive tasks are disappearing, making room for new capabilities, but not without disruption. Related: AI Won't Wait for Your Strategy -- Why Should Your Leadership? While some jobs disappear, I've noticed new ones emerge to support AI-first operations. The focus is shifting from repetitive work to compact, cross-functional teams that build, train and integrate intelligent systems. The most in-demand skills I see today combine technical fluency and adaptive thinking -- engineers who scale AI infrastructure, product managers who understand model behavior, and analysts who can bridge data, business and strategy. Meanwhile, traditional entry-level paths like QA, support and content moderation are narrowing, putting AI upskilling at the center of workforce planning. The impact is global. The U.S. remains the epicenter, but Europe and India are also restructuring. For example, Tata Consultancy Services is cutting over 12,000 jobs -- the largest layoff in its history, citing a skills mismatch. As automation spreads, experts warn that up to half a million roles could be displaced over the next few years. For younger professionals, this creates urgency. Opportunities are still there, but the timeline to reskill is shrinking fast. Layoffs are never easy, but how they're handled matters more than how many people are affected. From my experience advising companies through transitions, three principles make the difference. When layoffs happen, people assume they're just about saving money. That perception stems from poor transparency, making cuts feel abrupt and disconnected from a bigger plan. The goal is to make every workforce change part of your strategy, not just a reaction to external circumstances. To do so effectively, here is a short checklist to guide the process: Nothing unsettles a team more than uncertainty. When layoffs come in waves, people lose focus and start wondering if they'll be next. Еven top employees may leave to escape the instability. Furthermore, repeated cuts also erode confidence among investors and customers. If layoffs are unavoidable, make them a single, well-prepared move. Align leadership on scope and timing, communicate transparently about the reasons, and support those affected right away. Then, reassure the remaining team with a clear view of what comes next. Related: Why Every Company Will Need an AI Specialist by 2026 -- and What Happens If You Don't As AI reshapes work, many roles are evolving rather than disappearing. Repetitive, rule-based tasks -- common for junior developers, testers and support agents -- are most exposed. With targeted support, these employees can move into areas like AI-assisted QA, data curation or model monitoring, where human judgment still matters. To make this shift sustainable, organizations must become skills-based, placing skills not job titles, at the core of talent management. This lets you redeploy people into new value areas as strategy changes. According to Deloitte, SBOs move from rigid job structures to dynamic, skills-oriented models that allow talent to flow where it's needed. From my perspective, transitioning toward this model starts with three practical steps: Tech industry layoffs reveal more than cost pressures. They signal a shift in how organizations define value, talent, and readiness for the AI era. From what I've observed, the companies that thrive treat this moment not as an ending, but as a chance to redesign, reskill and rebuild smarter. The question isn't whether AI will change your workforce - it's whether you'll use it to make your people and your organization stronger.