How CIOs can scale AI by focusing on people

CIOs must prioritize a people-centered strategy to scale AI

AI is a fundamental shift in how companies operate. The CIO’s role is evolving from a systems leader to a people leader. Scaling AI requires more than upgrading infrastructure or rolling out new software. It demands a strategy that develops people, reshapes culture, and builds trust in new technologies. When employees understand AI, they contribute ideas that improve its impact rather than fear it.

Monica Caldas, EVP and Global CIO at Liberty Mutual, said it well: modernization means modernizing how people work and grow. She referenced research recommending a balanced investment approach: for every $1 spent on AI technology, companies should allocate $3 toward developing people. This ratio is about creating a workforce that can adapt, think critically, and scale innovation with purpose.

For executives, this means redefining how success in AI adoption is measured. It’s not just about operational efficiency or automation rates, but whether teams are learning faster, collaborating better, and expanding what’s possible with AI. Technology and talent must advance together. When they do, AI doesn’t replace people, it enhances what people can achieve.

A strong AI program depends on an equally strong people strategy. Leaders should focus on three areas: building trust in AI systems, ensuring transparency in outcomes, and creating opportunities for continuous learning. When your workforce feels empowered and informed, scaling AI stops being a technology challenge, it becomes a cultural and competitive advantage.

Developing human-centric skills is essential

AI-native organizations succeed because their people can think and act beyond the limitations of technical expertise. Technology evolves quickly, but human capability determines whether that technology delivers real value. Rod Adams, Principal and Advisory People & Inclusion Leader at PwC, explained how his team now works directly with the CIO to prepare the workforce for this shift. PwC identified 15 key human-centric skills, such as empathy, judgment, agility, and coaching, that are now fundamental to how they develop and hire talent across their 70,000 U.S. employees.

This is a strategic redesign of how talent supports technological progress. Technical training still matters, but it is no longer enough. People who can assess context, make decisions under uncertainty, and collaborate across disciplines make AI implementation far more effective. These skills create the connective tissue between technology and execution, ensuring AI supports business goals rather than operating as a disconnected system.

Executives need to understand that human-centric skills serve as the foundation for adaptability. In an AI-native environment, the speed of change is constant, and the ability to navigate that change defines competitive advantage. By emphasizing judgment and empathy alongside data analytics and programming, organizations create teams capable of managing complex problems and innovating responsibly.

AI transformation is as much about redefining the workforce as it is about refining algorithms. Investments in human capability are long-term value drivers. Leaders who integrate human skills into their technology strategies will find their organizations more resilient, agile, and capable of leading the next wave of AI-driven growth.

Measuring and motivating AI adoption continues to be a complex challenge

Organizations are eager to prove that AI investments generate measurable value, but defining the right performance metrics remains complicated. Many companies are still experimenting with how to assess employee engagement and output in relation to AI use. Rod Adams, Principal and Advisory People & Inclusion Leader at PwC, highlighted this tension, noting that while some firms attempt to track AI engagement through usage metrics such as token counts, these methods are shallow and risk creating pressure without real understanding of impact.

PwC’s approach is more deliberate. The company is testing a combination of quantitative and qualitative indicators. Productivity, client satisfaction, and the depth of AI use are reviewed to determine how effectively AI enhances operations rather than simply measuring how often it is used. Importantly, Adams clarified that PwC has not yet tied these measures directly to employee performance evaluations. The organization is prioritizing transparency and experimentation before formalizing any system. This open approach allows leaders and teams to adapt their understanding as they learn what truly drives success.

For executives, the lesson is clear: rushing to quantify AI fluency can damage culture and reduce trust. Leaders must strike a balance between accountability and experimentation. Overemphasizing numerical metrics may increase compliance but discourage creativity. Measuring AI success should include meaningful outcomes, improved decision-making, customer impact, and intelligent use of automation.

Performance measurement in the AI era requires flexibility. It is not about standardizing engagement metrics but about learning which behaviors and outcomes demonstrate value. Companies that remain open to revising their measurement models as their capabilities evolve will create more authentic engagement, sustain trust, and generate stronger long-term results from their AI initiatives.

Addressing employee fear and resistance is vital to the sustainability of AI initiatives

Every major technology shift introduces uncertainty, and AI is no exception. Employees worry about losing relevance or being replaced by machines. This reaction is rooted in FOBO, the fear of becoming obsolete. Irene Oh, CIO at Network Distribution, emphasized that these emotional and psychological barriers can derail transformation if not addressed directly. She explained that lasting change only happens when employees trust the process and believe they have a role in shaping the future of their organization.

AI advancement depends on human acceptance as much as it does on technical success. Employees need access to training, transparent communication about changes, and a clear understanding of how their skills fit into the organization’s evolving ecosystem. When companies equip people with the right tools and foster open dialogue, they convert resistance into participation. Culture moves forward when leaders show commitment not only to automation efficiency but also to their people’s growth and relevance.

For executives, the message is straightforward: AI transformation cannot be imposed, it must be inclusive. CIOs and other leaders should collaborate across the C-suite to identify pain points early, provide learning opportunities, and recognize achievements in adaptation. Managing fear is about creating structural support that aligns personal growth with business growth.

Employees look to leaders for signals on how secure their future is within an AI-driven enterprise. By showing commitment to both innovation and workforce wellbeing, executives reinforce confidence and sustain momentum. AI success rests on people’s willingness to engage. When fear is acknowledged and managed proactively, organizations unlock the trust needed to scale and sustain deep technological transformation.

Key executive takeaways

• Prioritize people to scale AI effectively: CIOs should align AI transformation with workforce development. Investing three times more in people than in technology ensures employees have the skills, confidence, and culture needed to drive sustainable AI growth.
• Develop human-centric skills for long-term resilience: Organizations must balance technical expertise with soft skills like empathy, agility, and judgment. Leaders should embed these traits into hiring and training to build adaptable, AI-ready teams.
• Redefine performance metrics for AI adoption: Measuring AI success needs a balanced approach combining qualitative impact, such as client experience, with quantitative output. Executives should focus on transparency and learning before formalizing metrics.
• Address fear to enable lasting transformation: Leaders must openly tackle the fear of obsolescence by providing training, clear communication, and visible support. Proactive engagement turns employee anxiety into participation, keeping transformation sustainable.