leading AI transformation with culture, value and human purpose
key findings
Discover how companies are successfully redesigning work for the AI era. Explore three essential strategies for human-AI success: building collaborative teams with shared accountability, bridging the return-on-investment gap by focusing on measurable outcomes, and prioritizing human purpose over basic task efficiency. Read insights from industry leaders on shaping the future of work. Learn how leading organizations are driving meaningful AI transformation, based on what they shared during the Radical Innovators Collaborative Summer Summit.
As AI continues to evolve, the value is no longer just about saving time, but truly transforming work itself. This means businesses must ensure they help talent to learn, thrive and gain the skills and best practices for working alongside AI. Much of that effort falls on the shoulders of CHROs, who are tasked with the challenge of literally redesigning the idea of work and what roles should look like in the AI age.
Indeed, this shift from AI-as-efficiency to AI-as-business-transformation is now a critical leadership imperative, but one that is easier said than done. So, what’s the best path forward?
3 strategies for human-AI success
Fortunately, there are many organizations that have unlocked the secret to AI transformation, as demonstrated during the Radical Innovators Collaborative Summer Summit, held this past June in New York City. This event, which brought together senior leaders focused on redesigning work, leadership and enterprise performance for an AI-first world, explored the biggest challenges facing HR leaders as they guide their teams through these uncharted waters.
From piloting new ways to work alongside AI to maximizing its value and ensuring people remain an essential part of the process, here are three ways companies are redesigning roles for today’s changing landscape and setting their people up for long-term success.
1. co-elevation: replacing competition with shared accountability
One thing is clear from our summit, as well as from my conversations with the CHROs I interview and meet with: the need for co-elevation. Pioneered by Keith Ferrazzi, founder, chairman and CEO of the Radical Innovators Collaborative, the idea of co-elevation is to shift mindsets away from individual performance and competitive silos, and instead toward group commitment, shared success and mutual accountability. And despite AI driving much of the need for increased co-elevation, this collaborative spirit should extend to digital workers as well.
Achieving true co-elevation requires treating AI agents not merely as simple automation tools, but as active teammates whose contributions and development are managed with the same care and intentionality as human colleagues. In this new human-agent paradigm, governance is shifting from traditional execution to orchestration and high-level judgment. Because AI agents can be convincingly confident yet wrong, success is no longer about speed or passive alignment, but about managing cognitive overhead and verifying output quality.
How can companies best navigate this shift? At the Summit, Cisco shared insights on its pioneering research on how human-agent teams can work effectively. Utilizing the UCLA Simulator, the company allows teams to practice critical human-agent governance decisions in real time before deployment in production environments. Among the learnings so far, the research finds that organizations must actively design workflows with built-in friction, challenge and constraints to account for AI agents being convincingly wrong at times. This intentional resistance prevents agents from defaulting to the path of least resistance, ensuring rigorous, high-quality outcomes through shared accountability between humans and AI.
2. the AI ROI gap: bridging value and consumption
The rapid adoption of AI has exposed a fundamental mismatch in how organizations measure and pay for digital labor. Many companies continue to use outdated seat-based pricing models that fail to capture the actual value generated by AI agents. To bridge this ROI gap, leaders should focus on real-world outcomes rather than simply tracking usage or headcount. This requires a shift in perspective where the goal is no longer just access, but measurable impact on business processes, ensuring that investment is directly tied to tangible performance improvements.
At the Summit, Google shared insights on its finance operations, which presents a powerful example of this transformation. The team deployed a "Resolve AI" agent to automate invoice reviews against contract terms. By moving away from manual sampling to reviewing 100% of invoices, the team significantly increased throughput. Crucially, this did not eliminate human roles but redefined them, as employees shifted from the repetitive task of manual auditing to acting as high-level decision-makers, judging exceptions identified by the AI. More than just automation, this approach emphasizes the rationale behind decisions, effectively turning every interaction into training data that continuously refines the system.
3. AI jobs disruption: preserving human purpose
The rapid evolution of AI necessitates a deliberate recalibration of the workforce, prioritizing human purpose over mere task efficiency. Organizations must proactively define the capabilities that keep work meaningful, ensuring that automation supports — rather than replaces — the unique value humans bring. Dell exemplifies this disciplined approach by prioritizing people and processes, with technology as the ultimate enabler. At the Summit, they shared the company’s strategy, which includes identifying value pillars in which AI promises to move the needle the most, rebuilding broken processes before applying AI and ensuring proper governance to avoid rogue AI islands. This approach has enabled Dell to implement its enterprise-wide AI transformation while maintaining its focus on its people.
At RedHat, the AI shift is manifesting in how roles are fundamentally redesigned for an agentic future, resulting in significant savings of both time and money. Rather than eliminating human input, RedHat is transitioning roles from manual execution to strategic oversight, empowering humans to manage the complex networks of AI agents that now power their operations. To bridge the gap for non-technical talent, RedHat has systematically converted legacy human processes into machine-readable logic. This move democratizes access to automation, allowing a broader spectrum of employees to leverage AI tools and engage in more sophisticated, high-value work.
Ultimately, navigating a future with AI requires leaders to move beyond viewing employees as costs to be minimized. Success depends on a "human-first" transformation strategy where technology is viewed as an enabler for broader organizational evolution. By clearly defining how AI augments capability, organizations can maintain institutional wisdom and purpose. Not only does this help shield their teams from the risks of mass displacement, but it also prepares them for a future in which humans and AI agents work side by side.
building a human-centric AI strategy
The Radical Innovators Collaborative Summit emphasized that companies must redesign work in a way that makes themselves and their teams better, not just operate faster. Just as important is the idea that future performance relies on the successful integration of human-centric leadership and AI-native operations.
But rather than adopting AI tools and strategies and adding them on top of existing processes, any AI integration must be intentional and be built with proper guardrails and governance. As these examples from Cisco, Google, Dell and RedHat from the Summit showed us, AI can indeed be implemented in a way that fosters human-technology collaboration, delivers clear value and ensures that human thought remains the most essential part of the equation.
As AI continues to transform the very idea of work, and companies transform their teams in response, we’ll continue to highlight the best practices of companies integrating AI successfully.