You Can't Spell AI without HR: The Surprising Secret to Scale

Generative AI’s intuitive interfaces and instant utility have left companies in an unfamiliar spot; individual employees are racing ahead, rapidly integrating the technology into their daily work, while their employers move more cautiously, often getting stuck in experimentation. In a recent Bain & Company global survey of nearly 800 IT executives, 97% reported some level of generative AI testing. Slightly less than 40% said that they have scaled its use across their organization.

Legacy systems, cultural inertia, and regulatory uncertainty are some of the reasons for this slow scaling of a technology that continues to evolve at breakneck speed. Consider, for example, how DeepSeek’s R1 model refocused executives overnight on the rapid decline in AI costs and its impact on long-term investment and strategy.

Early adopters that are able to break the pattern, moving past pilot projects to broader adoption, are seeing profound impacts on their workflows, team dynamics, and cost structures. They are doing this by integrating HR into their generative AI strategies and by balancing big strategic bets with practical, near-term gains.

A two-speed strategy: Big bets and small wins

The leaders are scaling generative AI in a controlled way, pursuing a dual-track approach that balances large-scale, transformative initiatives with incremental improvements. They empower their teams to experiment with incremental improvements while also reserving resources for carefully selected big bets that align with long-term strategic goals.

Big bets: transformational investments. Some companies are making significant, strategic investments that embed generative AI into core business functions. This can be agentic AI chatbots in customer service or automation of entire supply chains. These are bold, high-risk/high-reward moves that can only work with cross-functional alignment, significant investment, and strong leadership commitment. Critically, they should have a clear focus on return on investment; we remain early in the evolution of this technology, and big bets will need to have some payback, even when, inevitably, they are quickly replaced.

In our survey, 54% of companies identified as generative AI leaders reported redesigning their systems and processes to fully integrate AI—not just layering it onto existing workflows.

AI investments are revolutionizing how financial software giant Intuit’s people work. Integrating AI self-help into its customer service has reduced contact with TurboTax product support by 20%, and coders using AI assistance are up to 40% more productive.

Small wins: everyday efficiency gains. But this isn’t enough. While big bets grab headlines, smaller-scale applications of generative AI play a critical role, posting efficiency gains and showing a near-term return on the AI investment. In the most effective cases, teams are automating repetitive tasks, generating content at scale, and enhancing decision making—incremental gains led by employees without the need for complex and high stakes top-down directives. Benefits accumulate over time, building cultural buy-in momentum for larger initiatives.

In our survey, 69% of companies reported improved collaboration through AI-driven processes. And 77% saw meaningful reductions in time required to complete day-to-day tasks, with 30% saving more than 20% of their time in the first year alone.

Last September, Intuit reported a 15% average productivity gain from its generative AI trials, with certain tasks reporting even higher gains. The time required to create the software maker’s marketing content, for example, had decreased by 50%.

HR’s critical (often overlooked) role in getting generative AI to scale

One thing is clear: Companies that empower their employees to experiment with generative AI are pulling ahead. They aren’t making the common mistake of treating generative AI as a technology challenge. They recognize it as a workforce deep transformation.

Today, half of the companies we surveyed deeply involve HR in their AI.

When HR is fully engaged, AI adoption accelerates. And when it’s sidelined, progress stalls. Among survey respondents, 62% of companies with high rates of generative AI adoption, integration, and value creation are investing in employee training to scale the benefits of the technology.

These forward-thinking companies are integrating HR early into three key areas:

• Redesigning roles and workflows to maximize AI’s impact.
• Encouraging employees to experiment with generative AI to develop skills and learn organically. This is important for all companies, but especially so for organizations reliant on legacy technologies that need key skills and may struggle to attract scarce new AI talent.
• Creating a culture in which AI is seen as an enabler, not a threat. This goes hand in hand with a focus on organization-wide adoption over isolated experimentation.

The path forward: Three necessary steps for successful generative AI adoption

Even companies committed to mobilizing HR early in their integration (at scale) of generative AI face structural roadblocks.

Nearly half of the organizations we surveyed reported that they are struggling to modernize their platforms. Without foundational upgrades, AI’s transformative potential will remain limited. Companies operating in industries with stringent regulatory requirements have the particular challenge of balancing innovation with compliance—constraints that require new frameworks and expertise.

Cost of implementation is an issue, too. From upskilling employees to acquiring cutting-edge tools, leaders worry about spending money when there is not always a clear return on investment.

Overcoming these obstacles starts with taking three steps.

Step No. 1: Foster a culture of flexibility and experimentation. Generative AI is evolving too fast for rigid, multiyear roadmaps. Instead of betting everything on a single tool or strategy, leaders build adaptive AI governance models—often through an AI “center of excellence”—to ensure consistency without stifling agility. Companies must cultivate a culture that embraces iteration and agility and that can experiment, learn from failures, and refine quickly.

Step No. 2: Put people at the center. It’s critical to invest in workforce readiness, not just in technology. Giving HR a strong role in the deployment of your generative AI strategy will ensure that the things critical to generative AI’s long-term success—including upskilling, role redesign, and change management—are prioritized. HR can identify skill gaps, craft targeted training programs, and work to ensure that employees embrace AI-driven shifts in job tasks and processes.

Step No. 3: Rethink, don’t retrofit. Simply bolting AI onto existing workflows limits its impact. Rather than trying to make incremental changes to the existing ways of doing things, companies should get comfortable with redesigning work and processes from scratch. Combining big bets such as reshaping back-office operations, with small wins, such as encouraging writing and sharing GPTs, works here, too. And this type of reimagining offers fresh perspectives on the ways AI can transform operations and reach its full potential.

Act now

Generative AI is reshaping industries at an unprecedented pace. Companies that hesitate risk being left behind not just by competitors but by their own employees, who are already embracing AI in their daily work. By focusing on both small wins and big bets, companies can learn and scale at the same time. Elevating HR accelerates learning and helps ensure that systems are built to get the most from generative AI and to evolve as the technology continues to improve.