• The Big Shift: AI @ Work
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  • A new model of hybrid work takes shape, trust and training gaps threaten progress, and early signals show how AI is reshaping jobs and employment

A new model of hybrid work takes shape, trust and training gaps threaten progress, and early signals show how AI is reshaping jobs and employment

Author

Henry Nothhaft, Jr.
May 09, 2025

Your go-to rundown on AI’s impact on the future of work—delivered every Friday. Each edition highlights three to five must-read stories on everything from job disruption and upskilling to cultural shifts and emerging AI tools—all in a crisp, Axios-style format.

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In the wake of the week…

With each passing week, AI’s impact on work is coming more into focus. As one pundit noted on a podcast I heard this week (apologies, the source escapes me), we are no longer in the “future of work” conversation. We are in the early phases of a full-blown transformation. It is happening now.

This week’s edition tracks three connected shifts: the rise of hybrid human–AI teams, the structural importance of training and trust, and the early signs of workforce realignment. AI is already moving beyond task augmentation. The evidence shows it is now influencing how organizations define value, structure teams, and make hiring decisions.

And, in Extra Credit: a CIO hits pause on GenAI to protect company data, ChatGPT becomes a delusional co-pilot for some, many professionals still say no to AI on principle, and sales and marketing may finally find common ground.

Let’s dive in. 👇

The Future of Knowledge Work Depends on Human-AI Collaboration

A new model of work is taking shape across industries. Rather than viewing artificial intelligence as a replacement for human labor, business leaders are beginning to design systems where AI augments human strengths. Researchers and practitioners call this emerging model hybrid creativity, a framework where human insight and machine intelligence operate together, achieving new levels of performance.

From drug development to software engineering, hybrid teams are already demonstrating how value is created when natural and artificial intelligence work in concert. Wharton’s Cornelia Walther outlines the organizational prerequisites for this shift: workflows that encourage cross-functional collaboration, cultures that treat failure as fuel for innovation, and investments in lifelong learning to demystify AI and build trust across teams.

By the Numbers

  • 67% of business leaders say human skills like critical thinking and innovation are essential for successful AI adoption

  • 30% average productivity gain expected from hybrid workflows

  • 23% of employees anticipated to be redeployed due to digital labor

  • 327% projected growth in enterprise AI agent deployment by 2027 (Salesforce)

What to Expect

Organizations that treat AI as a collaborator will outperform those that implement it purely for automation. Successful hybrid work models thrive on intentionality. They demand restructured roles, new decision-making protocols, and stronger interdependence between functions.

Leadership Insight

As AI becomes ubiquitous across industries, sustained advantage will come not from access to technology, but from the distinct human strengths that shape how it is used. Leaders must now guide hybrid teams made up of both humans and AI systems. This shift demands new skills: orchestrating workflows where machines and people collaborate, personally modeling AI use, and cultivating human strengths that AI cannot replicate, like judgment, purpose, and emotional intelligence. AI can handle complexity, but it cannot replace discernment. Start by piloting a collaborative workflow, assigning AI tasks that free up human focus, and coaching your team on how to assess AI outputs with rigor and context.

The Bottom Line: AI is becoming a core contributor to creative and strategic work. But AI alone cannot replace human judgment, context, or values. Companies must build the structures that support true collaboration between people and machines.

Trust and Training Gaps Threaten AI’s Workplace Potential

Our first story detailed the emergence of hybrid teams where humans and AI work side by side. The question is how to get there. The answer begins with two overlooked foundations: workforce training and organizational trust.

Despite a surge in AI adoption, most companies are underinvesting in both. Only 31% of employees say their organization offers AI training. Meanwhile, 73% of workers are unsure how AI will affect their job.

Several recent reports converge on the same theme: AI implementation will stall without intentional workforce development and trust-based leadership, and organizations risk cultural and financial consequences.

By the Numbers

  • $252.3B in global corporate AI investment in 2024 (Stanford AI Index)

  • 31% of employees report receiving AI training at work (JFF)

  • 73% of workers are unaware how AI will affect their job (Salesforce)

  • High-trust organizations outperform peers by 186% in total shareholder return (PwC)

Why It Matters

Efficiency gains from AI adoption will not be realized without broad employee fluency and engagement. Yet leadership is falling behind: only 10% of U.S. organizations report advanced AI literacy, and fewer than one-third offer formal training.

Trust is the other missing link. Employees fear being replaced or sidelined, and vague communication compounds that anxiety. As Stephen M. R. Covey notes, trust is not a soft skill, but a strategic lever that accelerates performance and safeguards retention. Without it, disengagement and turnover become costly risks.

Leadership Insight

AI success depends on a credible workforce strategy. Equip managers to lead change with clarity, empathy, and a bias for inclusion. Build guardrails for safe experimentation and pair training with visible leadership adoption. Employees need not only instruction but permission and purpose.

The Bottom Line: AI will not deliver transformation unless organizations invest in their people. Training builds capability; trust sustains momentum. Both are prerequisites for scaling hybrid human–AI workforces.

AI Is Reshaping the Workforce, Not Just the Work

While our first two stories offer a more optimistic view of AI maturity and the steps required to get there, our third story presents a more sobering reality. The ground is already shifting beneath workers. AI is not eliminating jobs in sweeping waves just yet, but it is stunting new job creation and redrawing the contours of the labor market.

IBM confirmed it replaced hundreds of HR roles with AI agents, reallocating headcount to software development and sales. CrowdStrike laid off 500 employees while increasing investments in AI-led productivity. Shopify and Duolingo have both formalized policies requiring teams to justify new hires based on their inability to automate.

The IT sector reflects a broader transition. While unemployment ticked down slightly in April, the size of the IT labor pool is contracting. Companies are hiring fewer people overall and focusing on candidates with AI fluency and adaptability.

By the Numbers

  • IT unemployment fell to 4.6% in April, down from 5.0% in March

  • The U.S. tech labor force declined by 214,000 jobs last month (CompTIA)

  • CrowdStrike cut 5% of its workforce, citing AI-driven efficiencies

What to Expect

Roles are not vanishing overnight, but they are evolving rapidly. Employers are reducing headcount in support and operations while increasing investment in customer-facing and strategic functions. Workers without AI skills are exiting the industry, voluntarily or otherwise. The hiring bar is rising, and talent strategies are being rewritten in real time.

Leadership Insight

Displacement is not always downsizing, but it is disruption. Employees are watching AI reshape jobs across functions, from HR to programming, and are understandably anxious about where they stand. As noted in our first two stories, leaders should proactively acknowledge this uncertainty and communicate how AI investments affect current roles, future opportunities, and hiring priorities. Silence breeds fear; transparency builds trust.

The Bottom Line: AI is compressing the traditional workforce and concentrating opportunity around those with the right skills. Leaders who prioritize communication and skill development will be better positioned to manage change and retain top talent.

Extra Credit

For the overachievers: These are the stories that didn’t crack the top group this week but are too important to ignore—quick hits on what’s happening and why it matters.

Easy, River Walker, That’s Just ChatGPT Puffing You Up

Key Takeaway: Several documented cases show how unhealthy and compulsive use of AI chatbots is contributing to delusional thinking in vulnerable adults, including beliefs in supernatural missions or fantastical technologies, highlighting the psychological risks of prolonged, isolated engagement with generative AI.

Why It Matters: These AI-induced delusions may reflect a growing mental health vulnerability in isolated individuals who compulsively use chatbots as stand-ins for real-world interaction. The dynamic resembles patterns seen in internet and VR addiction, where immersive, always-on systems blur the line between fantasy and reality. Without social grounding or guardrails, generative AI may unintentionally deepen disconnection under the guise of companionship or purpose in some people.

Opting Out: Some Workers Still Resist AI

Key Takeaway: A growing but vocal minority of professionals are rejecting AI tools over ethical concerns, environmental impact, and fear of losing critical thinking skills. Their resistance reflects deeper questions about agency, values, and what it means to do meaningful work.

Why It Matters: Even as AI becomes embedded in everyday tasks, not everyone is on board. For some, opting out is a matter of principle. For others, it is a luxury they can no longer afford. The line between conscious resistance and professional risk is quickly blurring.

Could AI Finally Align Sales and Marketing?

Key Takeaway: For decades, sales and marketing have struggled with misalignment, costing companies significant revenue. AI offers a shared, data-driven foundation that replaces finger-pointing with coordinated action.

Why It Matters: AI changes the dynamic from lead handoffs to real-time orchestration. With AI interpreting buying signals and driving next steps, both teams can act from a common playbook—turning alignment from aspiration into operational reality.

Why One CIO Blocked GenAI

Key Takeaway: Limbach CIO Christos Ruci delayed adoption of generative AI out of concern for data privacy and reputational risk, authorizing limited use only after implementing rigorous oversight and continuous employee training.

Why It Matters: Even as most enterprises race to integrate AI, Ruci’s methodical rollout highlights a critical counter-narrative—security, governance, and trust must lead adoption, especially in industries handling sensitive customer and infrastructure data.

HR Tech Gets Personal: AI, Analytics, and Automation Reshape the Employee Experience

Key Takeaway: Human Resource Information Systems are evolving from back-office tools into intelligent, adaptive platforms that personalize every stage of the employee lifecycle, from onboarding to career development.

Why It Matters: As AI transforms how companies attract, engage, and retain talent, HR tech will be a core driver of workforce resilience and productivity. Organizations that leverage predictive analytics, automation, and hyper-personalization will be better positioned to support hybrid workforces and deliver differentiated employee experiences.

This edition of The Big Shift: AI @ Work may have been edited with the assistance of ChatGPT, Claude, Copilot, Gemini, Perplexity, or none of the above.

Want to chat about AI, work, and where it’s all headed? Let’s connect. Find me on LinkedIn and drop me a message.