By collapsing 75 tools onto one cloud platform and championing a ‘transform, don’t migrate’ mindset, Cisco cut software spending by over half and gained millions in savings, but the true challenge lay in empowering deeply rooted teams to embrace new processes.
- Engineers empowered to redesign decades-old processes
- AI adoption delayed by legal and compliance reviews
- Productivity boosts of 3-5% across 20,000 engineers
What happened
Cisco undertook a major transformation by consolidating 75 disparate tools into a single cloud platform, resulting in a 54% reduction in software spend and $5.3 million in annual savings through improved efficiency. The effort impacted 10,000 users directly and spanned multiple product lines, supporting a 20,000-strong engineering workforce.
However, the process was complex and candidly revealed resistance from senior engineers accustomed to legacy workflows. Jason Andrews, VP of Engineering Operations, emphasized that success hinged on more than just migrating technology; it required fundamentally rethinking operational processes and granting teams the authority to redesign how they worked.
Why it matters
The story underscores a vital lesson for enterprises embarking on cloud adoption or digital transformation initiatives: lifting and shifting systems without changing underlying processes rarely unlocks true value. Cisco's experience shows that leaders must confront organizational inertia and support empowered technologists to innovate internally.
Moreover, the integration of AI tools like Rovo highlights ongoing challenges. Legal and compliance obstacles delayed adoption for nearly a year, illustrating the importance of early cross-department dialogues to navigate new data governance requirements. This careful approach allowed Cisco to safely pilot AI-powered workflows, starting with straightforward queries and evolving to more sophisticated cross-platform reporting.
What to watch next
As Cisco continues to leverage AI across its thousands of engineers, the next phase involves broadening adoption beyond initial pilots to drive deeper operational insights and efficiency. Monitoring how well productivity gains scale will provide insight into the real-world impact of embedding AI within enterprise processes.