Sara Sheehan

Something remarkable is happening in the C-suite. Executives at some of the world's most profitable corporations are standing before analysts, shareholders, and employees and making a bold declaration: artificial intelligence has arrived, it is working, and thousands of jobs are no longer necessary. The numbers are staggering. The confidence is striking. And the evidence, for many of these organizations, remains dangerously thin. If you are a CEO navigating this moment, this article is for you. Not because you should ignore AI's transformative potential — you absolutely should not. But because the gap between AI's promise and its proven, enterprise-scale delivery may be wider than your board believes, and the cost of moving too fast could far exceed the cost savings on the spreadsheet. The Numbers Behind the Headlines The scale of AI-attributed workforce reductions in 2026 is genuinely unprecedented. According to programs.com, over 61,000 employees have been impacted by AI-driven layoffs in 2026 alone, with 45 or more CEOs publicly citing AI efficiencies as the rationale for cutting headcount (Programs.com, 2026). The trend crosses industries: Amazon eliminated a combined 30,000 corporate positions in late 2025 and early 2026; Block CEO Jack Dorsey cut 4,000 roles — nearly 40% of the company's workforce — stating that intelligence tools had fundamentally changed what it means to build and run a company; Oracle is reportedly reducing up to 30,000 positions; and Pinterest cut 15% of its workforce to accelerate an AI-forward strategy (Programs.com, 2026; Tech-Insider.org, 2026). The roles being eliminated paint a clear picture of where companies believe AI has already won. Customer support, quality assurance, content moderation, compliance processing, and middle management have been hit hardest (North Penn Now, 2026). Amazon's cuts have concentrated on corporate support functions and management layers (The Workers Rights, 2026). Block's reductions targeted customer support, internal operations, and mid-level management (Tech-Insider.org, 2026). Baker McKenzie, one of the world's leading law firms, is eliminating up to 10% of its global workforce as it shifts toward AI-assisted legal work (Programs.com, 2026). Entry-level roles are particularly vulnerable: a Stanford study found that Tier 1 support, manual quality assurance, and entry-level coding positions are already being automated at scale (Tom's Hardware, 2026). What makes this wave structurally different from the post-pandemic corrections of 2023 and 2024 is the stated permanence. Companies are not describing these as temporary belt-tightening measures. They are framing them as irreversible structural changes — the new operating model (North Penn Now, 2026). That framing deserves serious scrutiny. The Productivity Paradox No One Is Talking About Here is the inconvenient data point at the center of this story: the majority of enterprises cutting jobs in the name of AI efficiency cannot yet demonstrate that their AI systems are actually delivering the productivity gains they are banking on. A landmark February 2026 study by the National Bureau of Economic Research surveyed 6,000 CEOs, CFOs, and senior managers across the United States, United Kingdom, Germany, and Australia. The finding was unambiguous: over 80% of companies report zero measurable productivity gains from AI (National Bureau of Economic Research, 2026). A separate Deloitte study found that while 85% of executives have increased AI investment and 91% plan further increases, only 15% report significant, measurable ROI (Innobu, 2026). RAND Corporation data shows that 80.3% of enterprise AI projects deliver no measurable business value, and MIT research indicates that 95% of generative AI pilots never scale beyond the experimental phase (Valuebound, 2026). Cognizant Chief AI Officer Babak Hodjat has been direct about the timing problem, telling Nikkei Asia that it will take another six months to a year before companies start seeing real productivity gains from AI, and that the transition will be painful for all (Tom's Hardware, 2026). Even OpenAI CEO Sam Altman has acknowledged the dynamic, stating publicly that there is an element of AI washing where companies blame AI for layoffs they would have executed regardless, citing it as cover for financial restructuring or pandemic-era overhiring corrections (Tom's Hardware, 2026). PwC's 2026 AI Performance Study adds important nuance: nearly three-quarters of AI's economic value is captured by just 20% of organizations (PwC, 2026). The implication is stark. Most companies cutting workforces in AI's name are not in the winning cohort. They are in the 80% that remain, as PwC describes it, stuck in pilot mode. Why Moving Too Fast Is a Strategic Mistake The case for measured, deliberate AI-driven workforce transformation is not a case against AI. Read the full article here: https://bit.ly/4p4NIGM

And no one is talking about what comes next... Postings for entry-level jobs in the U.S. plunged 35% from January 2023 to June 2025. The career ladder isn't just wobbling—it's being dismantled from the bottom up. And the executives making that call may be sawing off the rung they'll need most in five years. For decades, the career ladder worked like a reliable piece of machinery. You started at the bottom—coding, drafting, analyzing, processing—and in exchange for doing the work no one else wanted, you learned. You developed judgment. You became, over time, indispensable. That machine is breaking down. AI is dismantling the bottom rungs, and most organizations are watching it happen without a plan for what comes next. The data is stark. Postings for entry-level positions fell 35% in the U.S. between January 2023 and June 2025, with AI identified as a primary driver—especially in roles categorized as highly AI-exposed: software developers, data engineers, financial analysts, compliance officers, and customer service professionals (Simon, 2025). In big tech alone, junior hiring has dropped more than 50% over three years (Rest of World, 2025). Meanwhile, 49% of Gen Z job seekers say AI has already eroded the value of their college degrees in the eyes of employers (World Economic Forum, 2025). These are not abstract projections. They are present-tense realities reshaping how talent enters the workforce—and how it will not. The Automation of the Learning Curve Here is the part that rarely makes the headlines: when AI eliminates entry-level work, it does not just eliminate jobs. It eliminates the mechanism by which people become good at their jobs. "Early-career jobs are the training wheels for a career," said Alison Lands, vice president of employer mobilization at Jobs for the Future. "Data suggests that AI is disrupting the traditional career ladder as we know it" (CNBC, 2025). The research firm Rezi captured this dynamic with precision in its 2026 analysis: the traditional deal of entry-level work—rote labor exchanged for mentorship and tacit knowledge—is dying. AI agents are automating the grunt work that junior employees once learned on, leaving early-career professionals stranded between autonomous systems and senior incumbents who hold the institutional knowledge those systems can't replicate (Rezi, 2026). Harvard Business Review has described this as the "nonlinear evolution of roles," noting that in industries where AI displaces entry-level functions, organizations risk losing the proving grounds where future leaders once developed their foundational capabilities (Harvard Business, 2025). The most urgent downstream consequence is what analysts are calling a "seniority cliff." Seniority is not simply a function of age or tenure—it is the accumulated wisdom of ten thousand problems solved, crises managed, and errors corrected. If an entire generation never grapples with those foundational challenges because AI handled them automatically, the mid-level and senior talent pools of 2030 and 2035 will be dangerously thin (Rezi, 2026). As one expert framed it: "If everyone does that, the entire pipeline of talent starts to collapse and, in a few years, employers in lots of sectors are going to find themselves in trouble" (CNBC, 2025). Who Is Rethinking This—and How A handful of enterprise organizations are taking the long view, and their approaches are instructive. IBM made headlines in February 2026 by announcing it would triple entry-level hiring across the U.S.—and the rationale is worth examining closely. Chief Human Resources Officer Nickle LaMoreaux did not simply defend the decision on humanitarian grounds. She made a business case. "Slashing early-career recruitment may save money in the short run," she said at Charter's Leading With AI Summit, "but it risks creating a scarcity of mid-level managers later on" (Bloomberg, 2026). IBM's approach was not to preserve old roles—it was to redesign them entirely. Junior software developers at IBM now spend less time on routine coding and more time on client interaction and judgment-intensive tasks. Entry-level HR staffers intervene when AI chatbots fall short, rather than fielding every employee question themselves (Fortune, 2026). The result is a model where humans are trained to do what AI cannot: handle nuance, read context, and make consequential calls. IBM is also explicit that 73% of its recruiters now rank critical thinking as their top hiring criterion for 2026—above formal AI qualifications (IT Brief, 2026). Cognizant is attacking the problem from the skilling side. In April 2026, the company launched Cognizant Skillspring, an AI-native learning platform built to map skills directly to roles, projects, and performance outcomes—with learning paths that adapt in real time as role requirements shift. Read the full article here: https://bit.ly/4y3OTKG

Every leader I have worked with wants their transformation to succeed. And yet, 70% of large-scale transformations fail to achieve their objectives — a number documented across industries, geographies, and decades (McKinsey & Company, 2021a; McKinsey & Company, 2021b). I don't cite that statistic to alarm you. I cite it because I have watched it play out firsthand — in Fortune 100 boardrooms, mid-market companies navigating ERP rollouts, mergers, digital transformations, and outsourcing decisions spanning more than 30 years of practice. The pattern is consistent. And it is almost never the technology that fails. Transformations fail because the human side of change was ignored, rushed, or assumed away. Here are six signals that your organization may not be as ready as you think. --- Signal 1: You Skipped the Readiness Assessment If your team doesn't know where employees stand before a change launches, you are flying blind. A readiness assessment tells you what people know, what they fear, and where resistance is already forming — before it costs you. Hershey Foods learned this the hard way in 1999. To beat Y2K, they compressed a 48-month ERP implementation into 30 months and skipped training and testing entirely. They launched in July — the peak of Halloween season — with a system employees weren't prepared to use. The result: more than $100 million in unfulfilled orders despite having inventory in stock, a 19% drop in quarterly profits, and a front-page story in The Wall Street Journal (Gross, 2026; Koch, 2002). A readiness assessment is not overhead. It is the foundation on which everything else rests. --- Signal 2: Resistance Is Being Managed, Not Understood Resistance is not a people problem. It is an information problem. When employees push back against change, it almost always means they don't understand what is changing, why it is changing, or what it means for them personally. Organizations that treat resistance as something to manage around — rather than a signal to engage more deeply — pay for it in slow adoption, workarounds, and, in the worst cases, collapse (Prosci, 2020). The leaders who succeed treat resistance as intelligence. They use it to refine their approach, close communication gaps, and build the understanding that drives real buy-in. --- Signal 3: Stakeholder Engagement Has Gaps Not every stakeholder needs the same message at the same time. But every stakeholder needs something. When key leaders, department heads, or frontline managers are left out of the change process, they fill that void with speculation — and speculation spreads faster than any communication plan you can write. Avon Products discovered this in 2013. They launched a $125 million SAP rollout to six million independent sales representatives with no readiness assessment, no role-specific training, and no meaningful stakeholder engagement for the direct-selling workforce at the center of the business. Representatives quit in meaningful numbers. The global rollout was halted. Between $100 and $125 million was written off. The CEO publicly acknowledged that the change to daily processes was too significant and too abrupt (Henschen, 2013; Computerworld, 2013). Stakeholder gaps are not a communication failure. They are a business risk. --- Signal 4: The Business Impact Has Been Underestimated Every transformation touches people's daily work. Job responsibilities shift. Processes change. Systems replace habits that employees have relied on for years. When organizations underestimate how much daily work will be disrupted, they underprepare their people and underfund the support required to carry them through it. They plan for the go-live date and forget about the six months after it. A thorough business impact analysis is not bureaucracy. It is the map that tells you exactly where your people will struggle — and what they will need to succeed (Prosci, 2020). --- Signal 5: There Is No Sustainment Strategy After Go-Live This is the signal I see most often — even in organizations that did everything else right. Go-live is not the finish line. It is the beginning of adoption. Without a sustainment strategy, the change that was carefully designed and thoughtfully launched begins to erode the moment the project team disbands. Old habits reassert themselves. Performance dips. The ROI you projected quietly disappears. The University of Virginia took a different approach. Facing widespread change fatigue and chronic project failure across an institution of 24,000 students and 28,000 employees, they built 54 Prosci-certified change practitioners, integrated change management into their university-wide project portfolio, and achieved $21.9 million in annual savings — $82.1 million cumulatively over four years (Prosci, n.d.-a). That kind of result doesn't happen without a plan that extends well beyond launch day. Read the full article here: https://bit.ly/4pcyyiL

Too many transformations fail not because the strategy was wrong, but because no one built the sustainment muscle to make new ways of working stick after the consultants and the momentum are gone. Sara Sheehan | Change Management Consultant | https://sarawsheehan.com