From Talent Pressure to Applied AI Skills
Enterprise networking is entering a period of structural transition. Artificial intelligence is reshaping how networks are designed, operated, and secured, at the very moment when a significant portion of the industry’s most experienced engineers is approaching retirement.
This convergence creates a dual inflection point: a generational workforce shift and a fundamental change in the skills required to build and operate modern infrastructure. Cisco’s response, bringing together its learning, certifications, and Networking Academy initiatives under the Learn with Cisco umbrella, reflects a broader industry recognition that AI readiness is as much about people and applied skills as it is about platforms and silicon.
In this first installment of our NetworkANGLE series, I sat down with Par Merat, VP of Learn with Cisco, and Ryan Rose, Director of Skills and Certifications Product Management of Learn with Cisco, to examine how Cisco is addressing this challenge at scale. The discussion focused on the evolving role of the network engineer, the shift toward applied and scenario-based learning, and why AI-era infrastructure demands broader systems-level thinking. See full interview below
Over the course of the conversation with Par and Ryan, it became clear that organizations that treat workforce transformation as a strategic priority, rather than a training afterthought, will be better positioned to navigate the next era of AI-enabled infrastructure.
A Dual Challenge: Talent Shortage and Skills Shift
The workforce issue is not simply a matter of headcount. As Par Merat noted, it is a “both/and” scenario, simultaneously a talent shortage and a skills shift.
A large cohort of early-generation network engineers, those who built the backbone of the internet, mobile, and cloud eras, is nearing retirement. These professionals accumulated decades of operational knowledge through hands-on configuration, troubleshooting, and architecture design. Their departure creates not only a staffing gap, but a risk of institutional knowledge loss.
At the same time, AI is increasing demands on networks at an unprecedented pace. Every digital interaction, data ingestion, model training, inference, and automation workflow rides the network. The infrastructure must now support:
- Higher performance requirements
- Greater automation
- Distributed AI workloads
- Increased operational complexity
Ryan Rose described this as the intersection of “skill readiness and network readiness.” Organizations are modernizing with technologies such as AIOps, advanced NetOps models, and next-generation connectivity like Wi-Fi 7. However, the expectations placed on engineers are evolving just as rapidly.
The result is not a replacement of foundational skills, but an expansion. Engineers must now think more holistically, moving beyond device-level configuration to systems-level architecture, automation strategy, and AI-assisted decision-making.
Bridging the AI Comfort Gap
Network engineers are often pragmatic and cautious by design. Their responsibility is uptime, performance, and security. Introducing AI into operational workflows raises understandable concerns about trust, control, and accountability.
Rose emphasized an important nuance: while engineers are cautious, they are also inherently curious. Many experiment in home labs, test new tools, and explore emerging capabilities. The key to accelerating “time to comfort” with AI is not abstract theory, it is practical application.
Cisco’s approach focuses on:
- Demonstration-based learning
- Real-world use cases
- Hands-on labs and experimentation
- Transparent AI workflows that allow verification
This emphasis on making AI “real” addresses a critical barrier to adoption. Engineers are more willing to integrate AI into workflows when they can observe, test, and validate its outputs. Transparency, particularly the ability to trace how AI arrives at conclusions, builds confidence and mitigates fears of surrendering control.
From a business standpoint, this matters because AI adoption in networking will not scale without operator trust. Cultural resistance can slow transformation initiatives as much as technical immaturity. Structured, experiential learning reduces that friction.
Learn with Cisco: A Unified Learning Continuum
Cisco’s decision to unify Networking Academy, learning programs, and certifications under the Learn with Cisco umbrella reflects a shift from siloed training programs to a continuous learning model.
This move was not about fixing a single problem. Instead, it acknowledges the accelerating pace of innovation and the reality that AI literacy is now required across experience levels.
Key strategic elements include:
- Multiple entry points, from beginner AI literacy to expert-level certifications
- Blended learning models (instructor-led, digital, and community-based)
- Holistic learning journeys that connect foundational knowledge to advanced practice
- Community-driven mentorship and peer collaboration
The scale of impact is notable. Cisco Networking Academy has trained 28 million individuals globally, with a high percentage of them attributing their career advancement to the program. Additionally, industry demand reinforces the relevance of certifications, with a substantial share of job postings specifying Cisco credentials.
For enterprises, this has two implications:
- Certifications remain a trusted proxy for validated capability.
- Workforce development is increasingly tied to ecosystem credibility and hiring confidence.
In an AI-driven market where applied skill matters more than static knowledge, structured validation becomes a competitive differentiator.
From Memorization to Applied Competency
Perhaps the most important shift discussed is the evolution of curriculum design and certification methodology.
Historically, technical certifications often emphasized knowledge recall. While foundational understanding remains essential, AI is increasingly capable of retrieving and synthesizing information on demand. The differentiator becomes applied problem-solving and architectural thinking.
Cisco is accelerating a move toward:
- Scenario-based training
- Project-based curriculum
- Lab-centric instruction
- Practical skill validation within exams
This approach aligns with how enterprises now evaluate candidates, through hands-on assessments and real-world simulations. Certifications that validate application, not just comprehension, reduce hiring risk and accelerate onboarding productivity.
For entry-level professionals, this shift is particularly significant. As automation absorbs many routine tasks once used to build early-career experience, new engineers must demonstrate practical competence earlier in their careers. Project-based learning helps bridge that gap.
From an enterprise perspective, this reduces time-to-productivity and supports more agile workforce planning. In AI-intensive environments, the cost of slow ramp-up or skill mismatch can be material.
The Broader Industry Context
Networking is entering a renewed period of strategic relevance. During the internet boom, infrastructure innovation defined the era. Today, AI is driving a similar inflection point.
Distributed AI workloads, sovereign requirements, and performance-sensitive applications are repositioning the network as a foundational enabler of business outcomes. This transformation elevates the role of the network engineer, from device operator to infrastructure strategist.
Reskilling is not optional. It is a structural requirement for AI-era competitiveness.
Organizations that invest in:
- Applied AI literacy
- Automation fluency
- Cross-domain systems thinking
- Continuous, community-supported learning
will be better equipped to translate AI investments into measurable operational gains.
OurANGLE
Part one of the discussion with Par Merat and Ryan Rose underscores a central truth: AI transformation is fundamentally a workforce transformation.
The industry faces simultaneous generational transition and technological acceleration. Addressing one without the other is insufficient.
By unifying its learning ecosystem, emphasizing applied skills, and aligning certification rigor with real-world demands, Cisco is attempting to close the gap between innovation velocity and operator readiness.
For enterprises, the takeaway is clear. Organizations that treat reskilling as a strategic priority, not a reactive training expense, will be better positioned to translate AI investments into operational advantage. Those who underestimate the human dimension of AI transformation risk slowing innovation at the point of execution.
In Part Two of this series, we expand the lens beyond technical practitioners to examine how business leaders, governance models, and AI-driven learning platforms are reshaping workforce development across the enterprise.
Link to part two
Link to Learn with Cisco
Link to Cisco Network Academy
