The Chief AI Officer: Managing Machines and Minds

Artificial intelligence (AI) is no longer an experiment in the enterprise—it’s a workforce reality. PYMNTS Intelligence’s CAIO Report surveyed 60 U.S. companies across goods, services and technology sectors to understand how companies are managing the technology’s effects on jobs, skills and strategy. The findings show that AI is not a simple story of automation replacing workers. It’s reshaping work—creating new roles, redistributing tasks and testing management’s readiness to balance cost, capability and human capital.

AI in the Workplace

Industry Intent

Firms’ reasons for adopting AI reveal clear sector divides. Nearly half of goods producers (48%) view the technology as a tool to boost output and efficiency, while 30% of service companies deploy it to improve decisions and customer experiences. Tech firms (42%) adopt the technology chiefly to stay competitive in fast-moving markets. The result: Efficiency drives the goods sector, intelligence drives services and competitiveness drives technology.

Role Reversal

AI isn’t eliminating jobs wholesale; rather, it’s changing what people do and how. About half of companies expect to create new roles that require advanced skills, even as roughly one-third foresee meaningful headcount reductions. Goods and service firms are nearly balanced between adding and subtracting positions, while tech firms expect more targeted hiring. The net result is a leaner but higher-skilled labor force.

AI Strategy Split

Approaches to AI workforce management differ sharply. Goods producers are most likely to trim staff while hiring AI-skilled talent. Service companies prefer automation that boosts productivity without layoffs, integrating human and AI output. Tech firms spread across strategies—from outsourcing to upskilling—reflecting both experimentation and maturity. No single playbook fits all, but every firm is recalibrating labor economics around the technology.

The AI Readiness Gap

Confidence in managing AI-driven change varies. Three-quarters of tech firms say they are at least somewhat prepared, compared with 63% of goods producers and just 48% of service providers. Across the sample, 40% remain neutral or uncertain. The uneven preparedness underscores how digital maturity—and not just ambition—determines whether firms can turn AI promise into performance.

Human Hurdles

The biggest barriers to AI success are human, not technical. Half of firms cite operational complexity as a top risk. Service providers (71%) and goods producers (59%) focus on skill gaps demanding reskilling or hiring, while half of tech and service firms face employee resistance to these tools. These findings show that managing culture and training is as crucial as coding the algorithms.

Mixed Outcomes of AI Use

Nearly two-thirds of all firms (65%) view AI’s effect on jobs as both positive and negative. Tech companies are most upbeat—half see the technology as largely positive—while nearly eight in ten goods producers describe its impact as mixed. Services sit in between, balancing optimism over efficiency with anxiety about automation’s toll on people-centric roles. The data suggests that most organizations are still in AI’s adjustment phase.

Conclusion

Across sectors, adoption of the technology mirrors business priorities, but every company shares one concern: staying competitive without losing talent. As organizations scale AI, success will depend less on how much automation they deploy and more on how effectively they align strategy, skills and culture. The new Chief AI Officer’s mandate is clear: Connect machine intelligence to human advantage.