Cisco is reshaping its certification portfolio to reflect an AI-first world, with major updates to CCNA and CCIE that explicitly bake AI, automation, and “human skills” into the learning path. These changes matter not only as exam tweaks but also as a signal of how the role of the network, and the people who run it, is changing in the AI era.

What Cisco is changing

Cisco is rolling out AI-driven updates across its certification portfolio, starting with a modernized CCNA blueprint and a new AI-focused module in the CCIE practical exam.

• The CCNA is getting its first major change since 2019, built around four pillars: network infrastructure, troubleshooting and problem-solving, a security-first mindset, and understanding the role of AI in network management and operations.
• The updated CCNA will emphasize hands-on, experiential learning, with more labs and practical skills assessed on the exam to validate day-one job readiness in hybrid, AI-driven environments.
• At the expert level, Cisco is introducing a CCIE AI Deploy, Operate, and Optimize module that embeds an AI assistant into the practical exam to help with configuration, troubleshooting, and code creation.
• Cisco is also releasing no-cost “human skills” tutorials on Cisco U. focused on critical thinking and business communication to help engineers become better strategic advisors, not just technical operators.

The timelines are deliberately staged: New CCNA exam topics became available on May 20, 2026, and the refreshed exam is going live on February 3, 2027, while the AI module for CCIE Data Center is slated to go live in 2027, following phased blueprint and lab releases at Cisco Live events.

Why AI makes the network more critical than ever

Every serious AI initiative, whether it is LLM-based applications, computer vision, or real-time analytics, ultimately runs on top of a network. Training clusters, GPU fabrics, data pipelines, and inference endpoints are all connected through the underlying infrastructure, and any weakness in that infrastructure manifests as latency, jitter, or outright failure in AI-driven services.

• AI workloads are bandwidth and latency sensitive, and often distributed across data centers, campuses, branches, and clouds.
• The telemetry needed to monitor, troubleshoot, and govern AI services is itself a massive data stream that the network must carry and make observable.
• Security for AI systems, such as protecting data, models, and APIs, relies heavily on network visibility, segmentation, and policy enforcement.

Cisco’s messaging around “operator to orchestrator” captures the shift: Instead of logging into boxes and typing commands, network engineers are now expected to design and run platforms that use automation and AI to keep increasingly complex environments stable, secure, and high performing. If AI is the new engine of the business, the network is the digital supply chain that keeps that engine fueled and safe.

The CCNA: AI-aware foundation for modern networking

The modernized CCNA blueprint is Cisco’s attempt to hardwire AI-era expectations into the first rung of the career ladder. While the certification still anchors on core networking, it does so with a recognition that AI and automation are now part of foundational skills.

Key aspects of the new CCNA direction include:

• A structured focus on network infrastructure plus deeper troubleshooting and problem-solving capabilities, mirroring the realities of operating hybrid networks at scale.
• A security-first mindset, reflecting that every network engineer is now also a security stakeholder.
• An explicit understanding of the role of AI in network management and operations—how AI-driven tools surface insights, support AIOps, and change day-to-day workflows.
• More experiential learning and hands-on labs, so candidates aren’t just memorizing commands but proving they can configure, secure, and troubleshoot live environments.

The long runway to the February 2027 go-live, along with free foundational content and tutorials through Cisco U., is designed to let both individuals and employers plan their transition without derailing current study paths. Importantly, Cisco makes it clear that the current CCNA remains valid and respected through the switchover, reinforcing its status as a global benchmark.

The CCIE: gold standard, now with AI in the loop

Cisco has long positioned the CCIE as the expert-level benchmark for deep, hands-on networking expertise, and that doesn’t change in the AI era. What changes is the toolkit a CCIE is expected to wield. The new AI Deploy, Operate, and Optimize module in the CCIE practical exam is a strong signal that expert engineers will be judged not just on what they can configure by hand, but on how well they can leverage AI tools to deliver outcomes.

• The module is a one-hour component alongside a two-hour Design module, complementing the existing five-hour Design, Operate, and Optimize assessment, with all three tied to a single blueprint.
• Candidates will interact with an AI assistant for configuration, troubleshooting, and code creation, offloading repetitive tasks and allowing them to focus their efforts on higher-level decision-making.
• The exam will assess both soft engineering (using general-purpose LLMs for scoping, diagnostics, validation, and assisted coding) and augmented engineering (using AI tools for AIOps and network operations).

Rather than diminishing the CCIE, this evolution arguably strengthens its status as the gold standard by recognizing that the best engineers are those who can combine deep knowledge of protocols and architectures with mastery of AI-augmented workflows. In other words, it is no longer enough to know the network; you must know how to orchestrate AI and automation around that network.

Human skills: from technician to strategic advisor

One of the more interesting aspects of Cisco’s announcement is the explicit emphasis on human skills. As AI takes on more rote tasks, organizations need network leaders who can connect technical capabilities to business outcomes.

Cisco is rolling out free tutorials on Cisco U. focused on critical thinking and business communication, framed as essential for translating complex technical environments into decisions that matter to the business. Employers consistently highlight these uniquely human capabilities as the differentiator for professionals who can use AI effectively and responsibly.

For network engineers, this is a cue to broaden beyond CLI and APIs: the value lies in articulating how a network change supports a customer experience target, a security policy, or an AI-driven product roadmap.

What this means for network professionals

Taken together, these changes sketch a clear profile of the skills required for network professionals in the AI era:

• Fluent in core networking and security, but also comfortable with automation, APIs, and AI-driven tools
• Able to read and reason about AI-generated recommendations, validate outputs, and adjust policies based on context
• Increasingly responsible for tying infrastructure design and operations to measurable business and user outcomes

For individuals, the implication is that standing still is not an option. Certifications are no longer static milestones; they are evolving proof points that you can thrive as AI reshapes the networking stack. As the expression goes, AI won’t take your job but an engineer that’s fluent in AI will.

Recommendations: How to keep your skills current

For network practitioners, many of whom are mid-career or senior professionals, the question is how to navigate this transition. Here are practical steps aligned with Cisco’s direction.

Treat the network as an AI platform, not just plumbing. Start mapping how AI initiatives in your organization depend on the network: data flows, GPU clusters, edge inference points, and observability pipelines. This will naturally pull you toward skills in telemetry, automation, and policy that directly impact AI outcomes.

Lean into automation and AIOps, even if you are a CLI purist. Experiment with AI-assisted tools for configuration generation, hypothesis generation for troubleshooting, and log analysis, while rigorously validating their outputs. The CCIE exam’s emphasis on soft engineering with LLMs is a preview of what your production workflows will look like.

Use the CCNA and CCI updates as a roadmap, even if you are not actively certifying. The new CCNA pillars (infrastructure, troubleshooting, security-first, AI in operations) are a solid checklist for foundational competence in modern networks. The CCIE AI module topics and timelines give you a sense of when AI-assisted operations will move from nice-to-have to table stakes in expert roles.

Invest in human skills as much as you do technical ones. Take advantage of resources like Cisco U.’s tutorials on critical thinking and business communication, or similar content from other providers. Practice explaining AI-driven network changes in business language, risk reduction, revenue protection, and customer experience rather than feature lists.

Plan your learning around Cisco’s timelines. If you are already studying for the current CCNA, stay the course because the exam remains live and respected until the new one launches in February 2027. If you are CCIE-bound, monitor the rollout of the AI Deploy, Operate, and Optimize module in your track (starting with Data Center), and start experimenting with AI tools now so the exam format reflects what you are already doing in practice.

Anchor on the CCIE as the long-term benchmark. For those seeking to position themselves as top-tier experts, the CCIE remains the gold standard. What is changing is that “expert” now implicitly means “expert at using AI and automation, not just configuring devices,” and the updated exam will verify exactly that.

As AI continues to reshape networking, Cisco’s certification updates offer both validation and direction: validation that the network is central to AI success, and direction on the skills that will matter most. For network professionals, the opportunity is to embrace this shift early and intentionally—and, in doing so, ensure the network team sits at the center of the AI conversation, not on the periphery.