Aligning Business Leadership, Governance, and Workforce Transformation
In Part One of this series, we explored how AI and generational shifts in the workforce are redefining the technical skill requirements for network engineers. But the impact of AI extends well beyond technical domains.
In this second installment, the conversation with Par Merat and Ryan Rose turns toward business leadership, governance, and how AI itself is being used to accelerate learning and certification development. The key insight is that AI transformation is not a technical project; it is an organizational one. See full interview below
Cisco’s expansion into business practitioner tracks, combined with its use of AI to modernize content creation and personalization, reflects a broader industry shift: workforce development must now span executives, technologists, and practitioners alike.
The challenge is no longer just building AI-ready networks; it is building AI-ready organizations.
Why a Business Practitioner Track Matters
A key announcement in this segment is Cisco’s push to formalize AI learning pathways for non-technical leaders, not just network engineers. Par framed this as a direct response to the reality that AI will touch every function and every sector, with a meaningful portion of job roles expected to change as AI becomes embedded into daily work.
This is an important move for enterprises because AI adoption often stalls not due to a lack of technology, but due to misalignment amongbusiness leadership, governance, and technical execution. A business practitioner track aims to close that gap by giving leaders a shared baseline in:
- AI fundamentals and applied use cases
- Workflow transformation vs. point productivity gains
- Responsible adoption practices, including governance
- A clearer understanding of how AI changes workloads, risk profiles, and operational dependencies
The enterprise outcome is straightforward: organizations that build shared literacy across leadership and technical teams are better positioned to make faster, more coherent decisions about where AI should be deployed, how it should be governed, and what “success” should look like.
Who’s Engaging: From C-Suite to Front-Line Managers
Par described traction as “holistic,” spanning senior leaders through operational management. That breadth matters. AI programs fail when they’re either:
- Purely top-down mandates without operational buy-in, or
- Grassroots experimentation without executive sponsorship and governance.
Cisco’s emphasis on hands-on learning, starting from fundamentals and moving to applications, helps address both risks. Executives can grasp the strategic implications and governance requirements, while managers and practitioners learn how AI reshapes workflows in practice.
The more nuanced benefit here is organizational: a shared vocabulary reduces friction between teams that often approach AI differently, IT emphasizing reliability and security, business emphasizing speed and differentiation. In AI-era operations, that alignment is becoming a prerequisite for scale.
Using AI to Build AI Skills, Without Compromising Trust
The discussion then shifted to how Cisco is using AI internally to accelerate the creation of learning content. Par pointed to Cisco U. and Networking Academy investments in personalization and predictive learning experiences, and described using AI to speed translation, blueprinting, and question/item development, while maintaining tight oversight.
Ryan added a critical perspective that will resonate with enterprise leaders: speed is valuable, but credibility is non-negotiable in education and certification. His framing, that every AI response is effectively a generated output that must be validated, underscored the need for robust review processes, especially when content will be used to validate professional capability.
From an enterprise outcomes standpoint, this “measured acceleration” approach maps well to how organizations should implement AI in production:
- Use AI to compress cycle times and remove repetitive work
- Apply human expertise to validate, refine, and contextualize
- Protect trust by ensuring outputs are accurate, explainable, and fit-for-purpose
The takeaway is not that AI replaces subject matter experts; it amplifies them when governance and review disciplines are in place. For companies relying on Cisco certifications as signals of real capability, that trust foundation is central to the program’s value.
Managing Complexity: Teaching Modern AI Networking Without Overwhelming Learners
One of the most practical segments addressed a growing reality in enterprises: the network domains that teams must understand are expanding. Traditional tracks already spanned campus/branch, WAN, and data center. Now, AI infrastructure introduces new backend requirements and architectures that many organizations didn’t operate at scale five years ago.
Ryan’s answer focused on “scaffolding”, a learning design approach that sequences complexity in digestible steps. The key idea: teach breadth early as awareness, then deepen skills through structured pathways, rather than forcing learners to absorb everything at once.
He described several mechanisms that align with how modern enterprises build workforce capability:
- Bucketing topics into foundational concepts before deep specialization
- “Unlocking” progression through early milestones (e.g., CCNA as an entry key)
- Modular content: tutorials, short use cases, labs, and role-based pathways
- Clear job-role alignment: design, troubleshooting, builder tracks, AI infrastructure paths
- Gradual progression to expert-level architecture, tying technical decisions to outcomes
A notable proof point was the popularity of the Data Center Networking Fundamentals course, which was offered widely for a limited time. The strategic insight: when content is structured to reduce intimidation and provide early wins, adoption rises, especially in fast-changing domains like AI infrastructure.
For enterprises, this scaffolding model reduces two common risks:
- Skill fatigue (learners disengage when everything feels urgent and complex)
- Misallocation (people train broadly but don’t gain role-relevant competence)
A role-based map also supports workforce planning: leaders can align training investments to operational needs, building depth where the business requires it most.
What This Signals About the Networking Profession
Collectively, part two reinforces an industry trend: networking careers are shifting from configuration work toward systems-level design, policy, automation, security integration, and AI-assisted operations. As AI infrastructure expands, the “network engineer” role increasingly intersects with compute, storage, orchestration, and application performance expectations.
Cisco’s approach, spanning business literacy, technical depth, community-driven learning, and practice-based certification, suggests a strategy to shorten time-to-expertise while preserving its thoroughness. That matters as enterprises confront both retirement-driven knowledge loss and accelerating infrastructure complexity.
Practical Takeaways for Enterprise Leaders
1) Treat AI literacy as a leadership requirement, not optional training.
A business practitioner track can reduce decision latency and governance confusion.
2) Invest in applied learning, not just awareness.
Scenario-based labs and role-aligned pathways accelerate operational readiness.
3) Demand trust mechanisms when adopting AI, internally or externally.
Human-in-the-loop review and transparency aren’t “nice to have”; they protect outcomes.
4) Use scaffolding to scale reskilling without overwhelming teams.
Structure learning journeys so people progress with confidence, not anxiety.
OurANGLE
Taken together, these two conversations underscore a fundamental industry reality: AI readiness is as much about people as it is about platforms.
Technical professionals must evolve toward applied, systems-level expertise. Business leaders must develop literacy in AI governance, workflow transformation, and responsible deployment. And learning organizations must balance speed with credibility as they integrate AI into their own development processes.
The networking profession is entering another period of renewed relevance, comparable in scale to the internet boom but broader in scope. This time, success will not be defined solely by technological innovation, but by how effectively organizations align skills, leadership, and trust with the demands of AI-driven infrastructure.
For enterprises evaluating their next steps, the message is clear:
AI-ready networks require AI-ready people at every level of the organization.
For viewers deciding where to start, the advice from Par and Ryan was simple and actionable: stay curious, start with what’s accessible, and engage the learning community. In a market defined by rapid change, the most durable advantage may be the ability to learn continuously, at both the individual and organizational level.
Link to part one
Link to Learn with Cisco
Link to Cisco Network Academy
