AI Success Requires Redesigning the Future of Work

Cisco’s Systematic Approach to Redesigning Processes for the Future of Work Offers a Blueprint for Enterprise Leaders Struggling with AI ROI

Originally posted in the March 6, 2026 “AI Decoded With Maribel Lopez” LinkedIN newsletter that discusses AI and the future of work for enterprise leaders. 

Most enterprises are layering AI tools on top of broken processes and wondering why the ROI never materializes. Cisco took a different approach. Instead of adding more tools to existing workflows, Cisco built a methodology for redesigning work from the ground up. The results from its initial pilot suggest that approximately 60% of workflow activities can be AI-augmented. But the more important finding is what had to happen before any of that augmentation delivered value: the work itself had to be re-architected.

This mirrors a pattern I’ve seen repeat across mobile, cloud, and now AI adoption. Organizations that treat new technology as a layer on top of existing operations get incremental gains at best. Organizations that redesign how work gets done around the technology’s capabilities capture the real value. Cisco’s approach is worth studying because it offers a replicable model for making that shift.

See the Work Before You Redesign It

Cisco created Atlas, an AI agent system that analyzes jobs and workflows to build an enterprise-wide map of how work actually gets done. Atlas cataloged approximately 4,000 entities and 25,000 relationships across job roles, activities, and tools. For each activity, the system identifies which AI tools can augment the work and suggests how roles may evolve.

The most useful insight wasn’t about AI at all. Atlas revealed that roughly 75% of project management tasks are identical across IT, operations, and software development. Without that visibility, each department would redesign independently, duplicating effort and missing opportunities to reuse what works.

This is the step most organizations skip. They deploy AI tools without understanding how work is actually performed across the enterprise. Pattern recognition across functions prevents waste and ensures that successful redesign approaches can be applied broadly. You can’t redesign what you can’t see.

Give Leaders a Redesign Tool, Not Just an AI Tool

Cisco paired Atlas with a digital workflow canvas that translates analytical insights into an actionable design interface. Leaders can see their current workflows, drag in available AI tools, reconfigure roles, and deploy new agents or assistants. When a leader saves a redesigned scenario, the system calculates the percentage of work now AI-augmented, estimates efficiency or growth gains, and produces an implementation plan.

This matters because workflow redesign requires structured methodology, not ad hoc experimentation. A formal design interface forces leaders to make explicit tradeoffs between efficiency, growth, role changes, and tooling investments. Most organizations lack this discipline. They experiment with AI tools in isolation, resulting in tool sprawl rather than transformation.

Gianpaolo Barozzi, VP and Chief Innovation and Technology Officer for Cisco’s People, Policy & Purpose Organization, put it directly: “Let’s pause and let’s not just keep layering on more AI agents or tools. Let’s actually reengineer the workflow and understand what the use cases that are in flight.”

That’s a message every enterprise leader needs to hear. Proliferation of AI tools without process redesign creates complexity, not value.

What Cisco’s Pilot Actually Proved

Cisco tested the methodology with its People, Policy, and Purpose (3P) organization. The pilot identified 28 transformational use cases and found that approximately 60% of workflow activities could be AI-augmented. Cisco is now expanding the approach to Product, Engineering, and Strategy organizations.

The deliberate balance between efficiency gains and growth opportunities is worth noting. Cisco explicitly avoided framing AI as a headcount reduction play. Instead, it positioned augmentation as enabling existing teams to expand scope or improve quality. This distinction matters for adoption. Teams that believe AI exists to eliminate their jobs will resist it. Teams that see AI as a way to do more meaningful work will embrace it.

This is consistent with what I see across enterprise AI deployments. The organizations getting measurable ROI from AI are not the ones deploying the most tools. They are the ones redesigning processes around specific, scoped use cases and measuring outcomes against defined business KPIs.

The Leadership and Talent Shift Most Companies Miss

Cisco is repositioning workflow redesign as a core leadership competency, not a delegated HR or IT responsibility. Fran Katsoudas, EVP and Chief People, Policy & Purpose Officer, described the shift: “It’s a leadership and a cultural change that has to happen, not just a talent change. Leaders need to lead change, embrace AI and reinvent their work.”

On the talent side, Cisco is revising its systems to identify AI explorers, employees demonstrating high AI adoption. AI tool usage data now informs engagement assessments, promotion prioritization, and career acceleration. AI proficiency is being treated not as a secondary skill but as a primary indicator of adaptability and future leadership potential.

Both moves are significant. Without visible leadership commitment, workflow redesign initiatives get treated as HR programs rather than strategic transformation. And if AI proficiency is strategically important but not formally recognized in performance management, you’re asking employees to change behavior without aligning incentives. That rarely works.

What Enterprise Leaders Should Do

Cisco’s approach reinforces a principle that applies to every enterprise AI deployment: tools alone don’t transform work. Redesigned workflows, combined with leadership commitment and updated talent practices, do.

Three actions to take now:

• Map your workflows before deploying more AI tools. You can’t redesign what you don’t understand. Build visibility into how work is actually performed across functions and identify where the overlaps and redundancies live.
• Make workflow redesign a leadership responsibility. If AI transformation is delegated to middle management or treated as an IT project, it will be seen as tactical rather than strategic. Leaders must own the redesign.
• Align talent systems with AI priorities. If you want employees to adopt AI, recognize and reward the ones who do. Update performance management, promotion criteria, and retention strategies to reflect AI proficiency as a core competency.

The question for most enterprises isn’t whether to adopt AI. It’s whether they’re willing to do the harder work of redesigning how their organizations operate. The technology exists. The use cases are proven. What’s missing is the organizational discipline to redesign workflows before deploying more tools. Start there.

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