• The Big Shift: AI @ Work
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  • CFOs tighten the screws, MIT offers a plan, and AI blows up the career ladder as we know it

CFOs tighten the screws, MIT offers a plan, and AI blows up the career ladder as we know it

Author

Henry Nothhaft, Jr.
May 02, 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…

The numbers are in, and they don’t quite match the hype. AI adoption is surging across the enterprise, but returns to date have been limited, scale remains elusive, and CFOs are now demanding results. A new MIT playbook provides a path forward, outlining four foundational pillars for moving from pilot to performance. Meanwhile, AI is distorting the career ladder, erasing entry-level roles, reshaping hiring patterns, and leaving workers facing a future that bears little resemblance to the career paths of previous generations. As these shifts accelerate, learning and adaptability are becoming essential skills for both individual growth and organizational resilience.

And in this week’s Extra Credit: Pope Francis leaves behind a moral framework for AI leadership, IBM shows what it means to operationalize transformation from the inside out, and Anthropic convenes a top-tier economics council to shape AI’s impact on labor and growth.

Let’s dive in. 👇

AI Adoption Is Surging—Returns Are Not (Yet)

AI is spreading across enterprises at breakneck speed—78% of companies now report AI use in at least one function. Yet most efforts have yielded only modest results. According to McKinsey, typical gains from AI adoption include less than 10% in cost savings and under 5% in new revenue. Only 1% of U.S. companies investing in AI have successfully scaled its use. CFOs are responding by setting hard timelines: nearly half say they will reduce funding if AI projects do not show measurable ROI within 12 months.

By the Numbers

  • 78% of companies report AI adoption (McKinsey, 2024)

  • Most see <10% in cost savings and <5% in revenue gains

  • $33.9B in global private investment in GenAI last year

  • Nearly half of finance leaders will cut AI spend if ROI is not achieved in a year (Basware)

Why It Matters

Executives sense AI is critical to their future, but many remain stuck in pilots. This “productivity paradox”, where technical capability outpaces business transformation, is now colliding with hard financial deadlines. CFOs are shifting focus from experimentation to execution, pressing for outcomes that prove AI’s value to the business.

Leadership Insight

To move from pilot to payoff, companies must define outcomes before selecting tools, break work into measurable tasks, and focus on repeatable workflows. Establishing KPIs at the outset and building internal confidence through early wins are now prerequisites for scale.

The Bottom Line: Many companies adopted AI on belief rather than evidence, driven by a sense that it would be essential to the future of work. Now, those leading the charge are under the gun to deliver results before the CFOs take the keys away.

MIT Offers Roadmap to Operational AI at Scale

With pressure mounting to produce measurable returns and AI investments under scrutiny, a new MIT Technology Review playbook offers timely, actionable guidance for leaders navigating this inflection point. Based on a global survey of C-suite executives, the report outlines a pragmatic framework to move from pilot purgatory to enterprise-scale deployment.

By the Numbers

  • 95% of surveyed companies are already using AI

  • 76% are stuck in one to three use cases

  • 99% plan to scale AI use

  • 90% expect to increase AI readiness spending this year

The Foundation

The report identifies four pillars of scalable AI strategy:

  1. Data Liquidity – Harmonizing fragmented data systems is foundational to impact.

  2. Cost Discipline – Budget constraints are hitting mid-sized firms hardest.

  3. Execution Strategy – Most firms are flying blind without clear KPIs or ROI benchmarks.

  4. Culture Shift – Success depends on cross-functional collaboration and business-specific applications.

Leadership Insight

MIT’s research reinforces that AI wins are not achieved through technical firepower alone. Leaders must align data, teams, and priorities before chasing new models. What will set companies apart this year is their ability to move from ideas to execution with AI strategies that deliver real business value.

The Bottom Line: The gap between AI ambition and results is operational. Companies that move from pilots to production with clear metrics, strong data infrastructure, and cross-functional alignment are the ones most likely to tap into that elusive ROI.

Sources. MIT Technology Review: A playbook for crafting AI strategy

The Career Map Is Being Redrawn

AI is reshaping how people enter and advance in their careers. As traditional ladders collapse into flatter, skill-based pathways, companies are rethinking how they develop, support, and deploy talent. Job readiness now depends less on degrees or tenure, and more on fluency with tools, adaptability, and speed of execution.

At the same time, job openings are declining and confidence in the labor market is slipping. The latest JOLTS report shows 7.2 million job openings in March, down a million from the year before. Surveys suggest people are seeing fewer opportunities around them and expect conditions to worsen. While AI is not the only force driving these shifts, its role in automating routine tasks and reducing demand for entry-level roles is increasingly reflected in the numbers.

By the Numbers

  • AI may reshape 50 million U.S. jobs in coming years (World Economic Forum)

  • Entry-level tasks face automation risk exceeding 50% in fields like sales and research

  • Banking sector AI hiring rose 13% in six months; product manager roles climbed 42% (Evident)

  • 49% of Gen Z believe AI has reduced the value of a college degree in today’s job market

The Times They Are a-Changin'

The employment landscape is shifting. In tech, automation is reshaping software development, quality assurance, data entry, and customer service. At Duolingo, contractors are being phased out in favor of AI-generated content. Shopify and Klarna now ask teams to demonstrate AI usage as part of performance reviews. Meanwhile, major banks are expanding AI hiring and launching internal tools to assist traders, bankers, and engineers.

Employers are prioritizing adaptability, domain expertise, and the ability to work alongside AI tools. Routine execution and traditional credentials are giving way to learning agility and value creation. 

These changes are affecting how workers view stability, mobility, and value. AI skills are gaining weight across industries, and employers are adjusting hiring, training, and promotion strategies to reflect this new reality.

Leadership Insight

Strong organizations are rethinking workforce structure to reflect how roles and skills are shifting. This means building internal mobility, expanding AI literacy, and designing career paths that reward adaptability and impact.

The Bottom Line: Work is undergoing its most significant transformation since the dawn of the Internet. Companies must align talent strategy to the new shape of work through targeted training, mobility frameworks, and updated role design.

Learning Becomes Strategy

As roles evolve and traditional career paths dissolve, organizations face growing pressure to adapt through workforce development that is continuous, structured, and embedded in daily operations. AI is reshaping the workplace, and long-term success increasingly depends on how well employees learn and grow. New research from MIT Sloan, the World Economic Forum, and HR leaders shows that learning culture now forms the foundation for execution, retention, and innovation.

By the Numbers

  • 44% of workers’ core skills will shift by 2027 (World Economic Forum)

  • Only 20% of companies have a reskilling strategy (Deloitte)

  • 63% of HR leaders report measurable business improvement from skills-based learning (Udemy)

  • 95% of companies use AI in some capacity, yet many are early in workforce preparation (MIT)

Always Be Learning

Companies like Lego and Velux are moving beyond traditional training models. They are embedding leadership-driven learning into daily operations. Leaders act as facilitators who use structured approaches such as A3 thinking and group coaching to strengthen team problem-solving. This creates momentum for transformation and a durable shift in culture.

The World Economic Forum highlights a “new skills triad” that is becoming foundational across roles:

  1. Carbon Intelligence – understanding sustainability regulations and performance metrics

  2. Virtual Intelligence – navigating hybrid work, remote collaboration, and digital communication

  3. AI Proficiency – integrating generative tools into workflows with purpose and care

Leadership Insight

Learning does not scale on its own. Leaders must model inquiry, structure reflection, and reinforce learning as a daily habit. A question-driven leadership style, supported by peer coaching and shared accountability, builds organizational capacity over time.

The Bottom Line: A workforce that solves problems, embraces change, and continues to learn is equipped for whatever comes next. In the age of AI, resilience and adaptability will matter as much as genius. And it all begins with learning.

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.

Pope Francis Leaves Behind a Vision for Human-Centered AI

Key Takeaway: Before his passing, Pope Francis emerged as a leading voice calling for AI development that protects the dignity of work and places human value at the center of innovation.

Why It Matters: As AI reshapes the labor market, Francis challenged world leaders, technologists, and employers to measure progress not by efficiency or profit, but by how technology supports human purpose, agency, and inclusion.

Box Outlines Its AI-First Operating Model

Key Takeaway: Box is embedding AI into daily operations by requiring every team to own an AI strategy, reinvesting efficiency gains into strategic work, and fostering a culture of experimentation backed by strong governance.

Why It Matters: This approach turns AI from a tool into an operating model. By aligning productivity, learning, and execution, Box is laying the foundation for faster iteration, higher-impact work, and long-term competitiveness.

Source. Aaron Levie on Linkedin

How IBM Rewired HR Around AI

Key Takeaway: IBM rebuilt its HR operations using AI agents, streamlining millions of interactions and shifting routine work to a digital assistant that improved both productivity and employee experience. AskHR now handles more than 11 million interactions annually, with 94% resolved without human escalation.

Why It Matters: IBM’s approach highlights how AI can scale when backed by operational discipline, cultural buy-in, and a focus on employee experience.

Anthropic Assembles Elite Council to Track AI’s Economic Impact

Key Takeaway: Anthropic has launched an Economic Advisory Council made up of top economists to guide its research on how AI is reshaping labor markets, economic growth, and global socioeconomic systems. The council will inform the company’s Economic Index and help connect AI development to real-world outcomes.

Why It Matters: With AI altering how work gets done, the council’s insights will help shape policy, business strategy, and long-term planning by grounding speculation in evidence and economic rigor.

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.