Companies need to take a human-centric approach to AI, experts say, as executives try to shift from experimentation to implementation

A McKinsey study finds roughly two-thirds of firms are still experimenting with AI, and industry leaders argue successful scaling hinges less on technology than on investing in people—Accenture recommends spending about three times more on workforce transition than on tech—while Johnson & Johnson has mandated AI fundamentals for its ~80,000 employees and uses AI in drug discovery to support its innovative-medicines unit (projected to top about $57 billion in 2025). Pilots at Standard Chartered—using generative AI for supervisor reviews and an AI-driven internal talent marketplace—show how in-house sandboxes can surface use cases and employee sentiment, but J&J’s experience that 15% of use cases deliver 90% of value and academic warnings about “techno-stress” underscore the need for focused, human-centric rollouts to capture disproportionate upside without harming productivity or morale.

Analysis

A 2025 McKinsey study cited in the article shows roughly two-thirds of companies remain in experimentation with AI, and industry leaders argue that scaling requires a human-centric approach rather than technology-first spending; Accenture’s guidance to spend about three times more on people than on technology underscores the emphasis on upskilling and change management. Johnson & Johnson has operationalized this approach by mandating an AI fundamentals course for roughly 80,000 employees, offering role-specific master classes, and applying AI in drug discovery for its innovative-medicines arm, which is expected to generate over $57 billion in sales in 2025. J&J’s internal analytics—finding that 15% of AI use cases delivered 90% of value—illustrate that focused scaling of high-impact pilots is key to capturing return on AI investment. Pilot programs at Standard Chartered, including generative-AI-assisted year-end reviews and an AI-driven internal talent marketplace, demonstrate the utility of sandboxes to test adoption and employee sentiment, while academics warn indiscriminate rollout can increase "techno-stress" and erode morale; sentiment signals in the report are mildly positive with modest market impact, and per-ticker sentiment favors JNJ (0.6) over ACN (0.2).

Key Decisions for Investors

• Consider increasing exposure to companies that show concrete human-centric AI adoption and measurable ROI—for example, JNJ’s mandatory training for ~80,000 employees and AI use in drug discovery tied to a >$57bn unit indicate clearer pathways to value realization
• Monitor consulting firms such as ACN for signs that clients are moving from experimentation to scaled adoption (metrics to watch: client spending mix on tech vs. people and rate of internal pilot scale-ups) before initiating or expanding positions
• Require evidence of focused value capture before adding risk: seek proof points such as percentage of use cases scaled (J&J: 15% driving 90% of value), employee completion rates of AI training, and demonstrable productivity gains, otherwise limit exposure
• Protect positions against behavioural and execution risk by monitoring employee sentiment and HR-related controversies (e.g., use of AI in performance reviews) and consider hedges or sizing constraints until firms demonstrate disciplined, human-centric rollouts