Hypothetical AI Doomsday Report Fails Reality Test
Meet the experts
Oleksii Reutov
AI & Data Science Delivery Excellence Lead
Antonina Skrypnyk
Head of FSI EMEA, Industry Success Leader
Ashley Testen Sherman
VP Client Success, Retail & CPG
Iryna Viblei
Principal Data & Analytics Solutions Architect
Summary
- AI layoffs aren’t destiny — they’re design. Companies that build AI to support people unlock more value than those trying to automate humans out of the picture.
- The real doom loop is stalled AI. With 90–95% of AI pilots failing to scale, the true threat isn’t disruption… It’s never getting past the proof‑of‑concept stage.
- The advantage goes to leaders who design AI for human uplift. Firms focused on augmentation — not elimination — are already outrunning competitors who build for efficiency first and impact later.
Will AI wipe out millions of jobs by 2028? A new Citrini Research says maybe. The coauthor even suggests taxing AI profits to offset potential job losses. But is this a doomsday prediction, or a real risk? While some companies have let go of workers in favor of AI efficiency, this pattern is not as widespread or pervasive as analysts want you to believe. But, this isn’t the first type of publicity generating fear-mongering — and it won’t be the last. This Trend Tracker combines four different (and sometimes conflicting) expert perspectives — because leaders are often feeling the same conflict themselves. If you're torn between hype, fear, and opportunity, you're not alone. That’s why we bring all the voices to the table, then boil it down to one takeaway: here’s what you can do now.
WILL AI REALLY CAUSE MASS LAYOFFS BY 2028?
Oleksii Reutov
Short-term market fears often inflate the impact of AI disruption. AI creates new opportunities, not layoffs. This “doom porn” is another reminder from short-term traders that AI is still a super-hot topic. It is here to stay and already delivers value. The challenge is to measure that value early.
Ashley Testen Sherman
I don’t see “AI doom.” I see a design flaw. If we build AI purely for efficiency and cost-cutting, then yes, rapid white-collar displacement could outpace economic adaptation. This traces straight back to the architecture. How we design systems to either amplify human potential or extract human labor. Companies can create systems that empower people and foster innovation, or they focus only on reducing costs.
Iryna Viblei
This feels like another wave of fear, amplified by social media and market cycles. We’ve seen similar narratives before. In 2008, with warnings of peak oil and energy collapse, and during industrialization, with fears of job losses. Both times, industries adapted and economies grew. AI feels more personal because it targets cognitive tasks, not just physical labor, but disruption doesn't mean collapse.
WHAT THE AI “DOOM LOOP” REPORT GETS WRONG
Oleksii Reutov
The focus on short-term fears overlooks AI’s long-term potential. AI is about intensification and multiplication. Private capital continues to invest, driven by optimism that AI’s power outweighs market volatility. We should ask: can the market deliver value before investors lose patience?
Antonina Skrypnyk
Leaders we work with aren’t building AI to eliminate workers, but to handle growth without constantly hiring. Growth is coming, churn is high, competition is brutal, and talent is already stretched thin. AI isn’t a replacement strategy, but a readiness strategy.
Ashley Testen Sherman
The issue is how we design AI, not if AI takes jobs. If companies focus only on efficiency, they leave workers behind. Instead, companies should design AI to augment human potential and support workers through transitions.
Iryna Viblei
It’s too early to predict mass layoffs with confidence. We're still in an experimentation phase. Research from MIT suggests around 90–95% of AI pilots fail to scale. That shows we’re still early, and AI's value hasn’t been consistently proven, especially at the scale needed to replace entire workforces.
HOW SHOULD COMPANIES RESPOND TO REPORTS LIKE THIS?
Oleksii Reutov
Don’t use AI to justify layoffs. They will “never pay off eventually.” Use AI to redesign processes and generate revenue. Link strategy to metrics you can measure on day one.
Antonina Skrypnyk
Companies should use AI to ensure stability, scale, and proof. Only after AI systems are trusted, compliant, and delivering measurable results will staffing models evolve. Even then, the shift will look more like slow rebalancing than sudden disruption. The smart next step? Build AI that strengthens your teams — systems that help people work faster, make better decisions, and handle more complexity.
Iryna Viblei
On the AI tax idea, I understand the intention. However, early taxation could slow innovation before its value is realized. Historically, investing in education and learning new skills has given better long-term results than trying to limit technology.
WHAT HOLDS THEM BACK?
Ashley Testen Sherman
Companies should evolve their AI systems and integrate human-state intelligence. They should design AI with emotional awareness, adaptability, and contextual decision-making — not just as layers of automation. It’s a leadership moment. Companies should proactively shape AI’s future.
Antonina Skrypnyk
If a company wants to do massive layoffs of routine jobs, they first need to build AI use cases that automate that kind of work, at scale and continuously compliant. That requires serious preparation — strong data foundations, an executable AI strategy, modern infrastructure, and a clean IT landscape. Not just a few agents in a sand box.
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FAQs
Unlikely in a uniform way. Today’s biggest blockers are operational gaps, weak governance, and change management.
Designing for efficiency only. If you ignore human amplification, you cap value creation and create organizational friction that turns initiatives into a traffic jam.
Treat AI like a new employee: train it, test it, and give it meaningful responsibilities. Upskill our workforce — starting with leaders.
AI isn’t standing at the door with a cardboard box, waiting to escort your team out. Don’t let the hype, fear, or the promise of “efficiency” steer decisions that deserve more smarts.
Ready to build AI that lifts people up instead of squeezing them out?
