The Big Rethink: An agenda for thriving in the agentic age

AI is poised to recalibrate knowledge work in much the same way robots revolutionized manufacturing—only much faster. With once “human” tasks within AI’s grasp—judgment, multistep reasoning, orchestration, problem-solving, and even creativity—executives will need to rethink roles for people and structures that ensure ethical, effective human–AI collaboration. The true limiting factor will no longer be technology’s capabilities but humans’ ability to oversee and manage agents.

Key questions:

• How do you reshape roles and responsibilities in a world where machines can think, orchestrate, decide, and create?
• How do you retain, manage, develop, and motivate human talent when AI becomes a central player in the workforce?
• How will you create and manage skills dynamically, providing continuous learning opportunities to counteract the shortening shelf life of skills?

Talent management will need to quickly evolve to focus on skills rather than roles and to be clear on what the most effective human–agent collaboration models look like.

As costs go down and capabilities go up, AI will lower entry barriers and enable new players to disrupt incumbents with unprecedented speed. Intellectual property and institutional expertise could increasingly become commodities. Recalibrating the business to home in on true sources of competitive advantage—think data, technology, culture, and enterprise-wide capabilities—is critical.

Key questions:

• What are the potential business benefits or material risks when my customers become empowered by their own agents to optimize customer outcomes instantly, continuously, and at close to no cost to them?
• In this environment, how can you protect and expand your sources of competitive advantage?
• How can leaders be transparent and fair, while avoiding short-term optimization at the cost of long-term benefits?
• How can culture and fostering a sense of belonging—dynamics that AI cannot replicate—be enhanced as critical differentiators to the business?

The journey requires thoughtful investment and calibrated action to continually protect and enhance competitive advantage.

Agentic AI is not just about short-term productivity; it’s about reimagining how work gets done to spark innovation, transform customer experience, and elevate employee engagement while delivering sustained value. Think inside the box and companies will find themselves boxed out of the big gains that will shape businesses for the next ten years. The opportunities lie in finding answers to important questions, such as:

• How will customer behaviors change when they are equipped with their own AI agents?
• Where can AI’s capabilities solve challenges that once felt impossible, or open avenues to entirely new business models?
• What are the sources of competitive advantage that can be enhanced with—or undermined by—AI?

Successful leaders will approach the future with a bold mindset of opportunity, rethinking their value proposition and redefining how to create value.

Our research shows that while almost 80 percent of organizations use gen AI in some way, the same percentage see no impact from it on the bottom line. The reason is often a focus on deploying tools and disjointed pilots: Many organizations start broadly and aim to raise individual competencies through copilots and chatbots rather than shifting collective performance. While these efforts are not wasted—they build fluency and readiness—true impact comes from thinking “AI inside,” where AI is embedded into a few high-value domains (verticals) and workflows are rewired end to end.

Key questions:

• Where could vertical reinvention create a step change in your business?
• What is the operating model for an AI-first workflow, and how should it be supported?
• How can you architect AI models, pipelines, and systems for reuse and scalability across verticals?

The leaders who act now will move beyond pilots and make AI central to how work is done and value is created.

Current organization structures are functional, reflecting how to best manage knowledge workers. As humans and agents work side by side, organizations will need to pivot away from traditional functions and toward outcome-oriented models that are flatter, faster, and more fluid. Cross-functional squads will fuse product vision with software delivery, harnessing AI to accelerate the journey from idea to impact. Shared ownership and real-time experimentation will become the norm.

Key questions:

• What governance must be implemented to ensure accountability without slowing progress?
• How should you track progress and growth when productivity is no longer defined by time but by how many agents the business can effectively orchestrate?
• What does an optimally designed organization that harnesses the best of AI and human collaboration look like?

Leaders should consider designing systems where human accountability and agent speed reinforce—not clash with—each other.

The rapid pace of AI innovation presents a double-edged sword. While the opportunities are vast, so is uncertainty. In a world with near-zero marginal knowledge costs, success will depend on how well—and how quickly—organizations can learn and adapt. Learning and adapting are primarily cultural questions, but they will require putting in place capabilities, such as scalable, flexible technology infrastructures (for example, as AI meshes) that encourage and can respond to continual change.

Key questions:

• How can organizations build a continuous improvement, “test-learn-adapt” mindset and culture?
• What is the right balance between open sourcing and building internally?
• How can you architect an organization that learns faster than your competitors?

Having the necessary level of flexibility on the technology front means rethinking the build-versus-buy equation to prioritize custom infrastructure, multicloud deployments, and scalability.