Good Friction: The Leadership Blueprint for a Human-Agentic Workforce

Dan Gray, Vice President of Global Technical Customer Operations at DXC Technology, offers a blueprint for what a human-agentic workforce can look like and how “friction” can benefit your company. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

AI is reshaping industries and reimagining work at a remarkable speed and scale. While many organizations continue to frame adoption around how much they can automate, enterprises that are making real progress recognize that humans play a central role in harnessing AI to drive real results.

Rather than blanket automation, organizations are intentionally designing moments where human insight, judgment, and collaboration create value that AI alone cannot. This is what we call “good friction.” Leaders are effectively deciding what kind of AI relationship they want to build: one in which AI assists professionals in doing their jobs better, or one in which AI delivers outcomes more directly, with humans supervising, governing, and shaping decisions from a higher vantage point.

This comes at a pivotal moment. AI, particularly agentic AI, is growing more sophisticated and self-sufficient by the day, and the workforce feels it. A global ADP survey of 35,000 workers shows a clear divide: 43 percent see AI as helpful, while 42 percent fear job loss. The youngest workers, aged 18 to 24, understand AI best but are also the most anxious about its impact on their careers.

Despite these concerns, organizations that view AI as a tool to supercharge, not supersede, their workforce will pull ahead. Underpinned by “good friction,” leaders can prepare people and transform processes alongside this technology to elevate human capabilities, reinforce AI-ready cultures, and amplify small wins into scalable results.

Retraining employees for judgment-driven, higher-value work

AI transformation begins with automating repetitive tasks, but the work doesn’t stop there. The real impact comes when organizations enhance what humans can do. Increasingly, this means building an agentic workforce where humans and AI agents work together as digital teammates to multiply the value of their output. This represents a fundamental shift from task-specific automation, where AI functions as a tool, to persona-based AI, where AI takes on the role of a digital colleague with specific capabilities and responsibilities.

It’s worth being precise about what makes agentic AI different from traditional automation. It’s not simply about doing tasks faster or at a greater scale. Agentic systems understand context rather than just follow rules, make decisions based on evolving signals rather than static playbooks, and know when to pause and escalate to human oversight rather than push forward. That distinction matters enormously for how organizations prepare their people.

Take security analysts at DXC’s security operations center (SOC). Rather than simply deploying AI tools to automate isolated tasks, DXC introduced agentic AI that functions as a junior-level analyst or digital teammate, handling entry-level work such as alert classification and documentation of findings. This persona-based approach cut investigation times by 77 percent. The results freed analysts to redirect their expertise to higher-value work, such as complex investigations and fine-tuning systems to catch emerging cyber-attacks. It also empowered them to implement agentic SOC solutions for global customers just starting their AI journeys.

This pattern of transformation is proliferating across industries, including DXC’s Global Infrastructure Services business. The question is no longer whether AI will reshape work, but whether organizations can move fast enough to capture the advantage that comes from preparing their people for it.

We recently surveyed nearly 2,500 global business leaders and found that among those with high visibility into workforce planning, the vast majority are taking action: 86 percent will use AI to shift job responsibilities, 89 percent will create new roles to manage and optimize AI systems, and 87 percent are actively retraining staff to work alongside AI.

Ensuring AI systems remain ethical and explainable

High performers will excel where AI can’t. Take highly regulated industries like insurance, where human behaviors like empathy and nuanced understanding are often required. When regulators ask, “How did you make that decision?” insurance organizations relying on agentic AI will need people who can explain the reasoning and the underlying algorithms, and who can take accountability.

This need for human judgment extends beyond compliance. While agentic AI can speed up claims and sharpen risk assessment, insurance agents and brokers remain essential. They help customers navigate complex products, personalize coverage, and feel supported during life’s milestones.

The human imperative goes beyond just frontline workers. Our survey found business leaders ranked the lack of AI leadership and strategy alignment as the second-biggest challenge to implementing AI solutions, just behind integration difficulties with legacy systems. This is not surprising, as many managers are no longer just leading people; they’re orchestrating collaboration between humans and AI. As these systems take on more responsibility, effective leaders must guide both to ensure AI delivers value without losing the human touch.

It’s also worth remembering that autonomous systems are only as useful as the context in which they operate. Leaders who invest in giving AI the right environmental understanding, including the nuances of their organization, customers, and industry, will see meaningfully better outcomes than those who treat intelligence alone as sufficient.

Fostering trust, aligning teams around shared outcomes

Change is hard. Anyone who has led a team through periods of uncertainty or disruption knows resistance is inevitable. What differentiates effective leaders is their ability to build trust, inspire confidence, and demonstrate a clear, collective path to results.

With agentic AI, that path starts small, with targeted implementations that tackle specific business processes. Quick wins are often the blueprint to faster (and larger) investment. When colleagues and leadership see AI making their jobs easier, teams more productive, and organizations more profitable, they become evangelists. This unlocks bigger opportunities. Human talent shifts from tedious to tactical, with AI handling the routine while people embrace more rewarding work that creates a competitive advantage.

Organizations with a clear path forward don’t just accelerate automation; they innovate differently. And their true measure of success ranges from the sophistication of the tools they deploy to the outcomes they deliver. Leaders who keep that distinction front and center are the ones who pull ahead.

The bottom line

Our next era will be defined by AI. Across industries, impactful AI transformation isn’t merely a technological endeavor but a workforce-and-workflow one that honors both the value of AI and people. Enterprises that thrive will invest not just in their tech stack but in their human potential, guided by “good friction.”

Large, established organizations have more to bring to this moment than they may realize. Deep institutional knowledge, mature governance practices, and years of hard-won operational experience are not liabilities in an AI-driven world; they are advantages. The organizations that choose to lead their own transformation, rather than protect legacy ways of working for too long, will find that those foundations are exactly what agentic AI needs to perform at its best.

Looking ahead, the most competent organizations won’t be the ones with the latest AI models or cutting-edge tools, but those whose people understand how to add value where this technology simply cannot. This will demand leaders committed to relentless skills development, transparent oversight, a clear and communicated vision, and an understanding of the value-gap that only people can bridge. The technology is here. The real work of deciding what kind of workforce and company you’re building is just beginning.