AI Is Changing Enterprise Networking, but Not All Disruption Looks the Same

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AI has become a nearly universal component in enterprise networking product road maps

TBR’s latest research on AI disruption among enterprise network vendors addresses the gap in measuring how deeply AI is changing how these vendors operate by providing a quantitative assessment of Cisco, Hewlett Packard Enterprise (HPE) and Extreme Networks using TBR’s ©Human Intensity Reduction Index (HIRI). HIRI is a proprietary metric that provides a direct measure of a company’s AI, human capital, utilization, offshore leverage and delivery model evolution on its operating margin. The metric helps distinguish genuine business-model disruption from window-dressing AI announcements.
 
Our initial results show an industry in transition. HIRI and operating margins are improving across the peer group, but AI is only one part of the story. Restructuring efforts, revenue growth, subscription adoption and portfolio consolidation continue to strongly influence workforce productivity. Additionally, customer demand for AI-based solutions to support their own AI transformations boosts financial performance independent of the vendors’ internal AI investments.

HIRI helps separate AI-related activity from routine business operations changes

The three vendors illustrate different approaches to achieving the same fundamental goal. Cisco demonstrates the clearest evidence of AI directly handling work, including 145,000 customer support cases resolved without human intervention. Cisco has also publicly touted that AI agents are now available to all remaining employees following its latest round of layoffs. HPE is leveraging AI enhancements along with the Juniper integration, creating opportunities to reuse development resources across a much larger networking portfolio. Extreme Networks is taking a product-led approach, using Agent ONE and Platform ONE to advance autonomous networking and accelerate its transition toward a more SaaS-oriented business model.
 
HIRI is only part of the story, however. Through our research, we also examine how AI is disrupting vendors’ operating, commercial, delivery and partner models, layering a qualitative lens over quantitative metrics. This analysis makes it clear that AI is only part of the broader efficiency story for these vendors. One of the most important emerging AI-centric themes is the progression from AI assistants toward agentic and autonomous systems that can increasingly investigate, troubleshoot and execute network operations with less human intervention. These vendors are both producers and consumers of this technology, placing them all squarely at the center of this transformation. There are also very real, nontechnological roadblocks to this progress, namely FOBO (fear of becoming obsolete) and the underlying hesitation among end users to adopt and embrace AI, a challenge companies continue to seek to overcome.
 
TBR’s AI disruption research will continue to examine whether enterprise network vendors’ investments in AI are materially changing how they operate, generate revenue, deliver technology and engage their ecosystems. Access the current enterprise network vendor AI disruption data and analysis with a TBR Insight Center™ free trial.

Exploring KPMG’s Role in AI

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In July KPMG hosted its 9th annual Tech and Innovation Symposium in Deer Valley, Utah, attended by more than 100 technology and business professionals, politicians, academics, KPMG technology alliance partners, KPMG professionals and a handful of analysts, including TBR. Three days of discussions, breakout sessions and informal chats covered AI, the space economy, quantum, cybersecurity, workforce transitions, the NBA, geopolitics and more. This is the first in a series of blogs focusing on specific elements, discussions and/or insights TBR noted during the event.
 
In 2026 KPMG’s message around business outcomes is landing differently than it did just a year ago, when AI hype possibly reached its peak. Now the firm and its clients talk less about productivity, cost cutting, or even AI’s potential to increase revenues and growth. Technology has taken a backseat to change as a business outcome. It is a subtle shift, perhaps, but in contrast to hype about AI’s unrivaled promise, the joys of vibe coding, and AI as the answer to every question, a grounded, restrained, business-first approach — one could even say “vibe” — provided a convincing foundation for the wary optimism that permeated the KPMG event.
 
Two specific recurring themes capture the difference between 2025 AI talk and 2026 AI reality. First, speakers, including but not exclusively KPMG professionals, stressed that enterprises cannot overspend on governance, risk and compliance. Understanding the inputs, the outputs, and what led from one to the other will be critical to leveraging AI at scale. Absent trusted and auditable AI, there will be chaos (apres moi, le deluge, perhaps). Second, using the right type of AI matters more than using AI. To paraphrase Ted Lasso, don’t be a gravedigger using a spoon.
 
Additional insights into KPMG’s AI initiatives based on discussions and presentations at the Utah event:

  • TBR noted a disconnect between AI as it is reported in major news outlets and AI-centric news and analysis feeds and AI as discussed by the speakers, KPMG professionals and attendees. IPOs, circular financing among chipmakers and frontier model companies, acquisitions, and AI personalities barely registered. Regulatory changes effecting enterprises’ business decisions around AI, practical applications of AI-enabled solutions, cost considerations when choosing different AI models, and the twin challenges of change management and trust featured heavily. In short, the Symposium participants seemingly ignored AI drama to focus on AI practicalities.
  • During a question-and-answer session, KPMG US CEO Tim Walsh provided a fairly compact set of steps for enterprises embarking on an AI initiative: know yourself, know your data and operations, know your resilience, and bring in an outsider to help. In TBR’s view, perhaps KPMG would prefer the last step to come first, but as a way for KPMG’s clients to think about scaled AI adoption within an enterprise, those steps are necessary for success, starting and ending with trust. At last year’s Symposium, a KPMG speaker said success depends on leaders, labs and the masses, a formula TBR brought up with clients during this event, testing its continued relevance. Next year, TBR will ask if outside help still rings true.
  • In a session with TBR, KPMG leaders noted that the firm has encouraged every professional across the firm to use AI agents, even if only at a rudimentary level. They also stressed KPMG’s AI role lies between the AI model layer and the data layer, providing intelligence and orchestration based on KPMG’s understanding of how businesses actually run. In TBR’s view, this intentional positioning in the middle of the AI value chain (in addition, of course, to AI advisory services at the front end of consulting) reflects the firm’s broader strategy of becoming an even more critical ecosystem partner to technology providers. When a KPMG leader talked about the firm’s OpenAI + Oracle + KPMG customer-zero story, TBR heard how OpenAI and Oracle can now better explain, to their clients, KPMG’s value and KPMG’s orchestration and business operations layer.

At the start of 2026, “simplicity” seemed to be the word of the year. Clients clamored for simplicity in AI deployments and their technology spend. Consultancies and IT services companies promised simplicity in pricing, implementation and operations. By July, trust replaced simplicity: trust at scale, trust through auditable AI, and trust enabled by Responsible AI.
 
What struck TBR most about KPMG’s frequent evocations of trust was the humility that went with it. Knowing trust has to be earned, maintained and renewed seemed to inform how KPMG leaders spoke about the firm’s capabilities and relationships. And in casual discussions with KPMG clients at the Utah event, trust came up repeatedly as a key reason they were there.