In a recent blog we asked the question:

“How can enterprises quantify the value of their investments in network visibility?”

That exercise led to this one which dives more deeply into the role visibility plays in optimizing network and security monitoring operations to meet three trends being driven by the mass adoption of AI:

• Threat actors’ ability to generate and scale modern cyberattacks in real time
• AI’s ability to drive up data center costs
• The use of AI to help defenders accelerate detection and response  

Where enterprise adoption of AI stands

A few years ago, Gartner predicted that more than 80% of enterprises would be using generative AI or gen-AI-enabled applications by 2026[1]. Keysight’s experience working with customers and more recent research bear this out:

• In one EY survey of IT leaders, 50% said their companies plan to double their AI budgets in the next year
• The same survey said AI could represent up to 25% of the total IT budget before long[2]
• 62% of respondents in a McKinsey survey said their organizations are using or experimenting with AI agents[3]

This last bullet may prove most impactful as agentic AI has the potential to transform an endless array of business operations from qualifying leads and updating CRMs to resolving support tickets and performing compliance checks. The more organizations find new ways to transform processes using generative and agentic AI, the more they’ll need to invest in AI-scale data center infrastructures — and the visibility needed to sustain and scale initiatives.

AI drives cost and efficiencies within modern data centers

Much like we can’t suddenly decide to build a skyscraper on a foundation designed to be the site of a track home, we can’t expect traditional legacy network, cloud, and security infrastructures to accommodate the explosion of AI without upgrading their foundations. Here’s what that means from a cost perspective:

AI-powered data centers could increase data center costs by more than 5X

Recent projections from AllAboutAI predict that in 2026, “33% of the world’s 11,800 data centers will be optimized for AI workloads, totaling nearly 4,000 AI-capable facilities globally.”  By 2030 they expect:

• AI data center investments will exceed $200 billion annually
• AI training workloads will consume up to 70% of global data center capacity
• The AI data center market will continue to grow at a rate of 28.3% (outpacing the 11.24% growth of traditional data centers)

The most commonly cited reasons supporting AI costs so much more center around the basics:

Specialized hardware/ high rack space density

High-end AI GPUs can run up to $30K, meaning a single AI rack could exceed $500K on hardware alone. Hardware that powers AI also takes up more than its fair share of rack space with~60% of the hardware investment going to GPUs and AI-specific components.  

All told, AllAboutAI suggets:

The average cost of an AI rack is projected to hit $3.9 million in 2026, compared to just $500,000 for traditional server racks. This represents an almost 8x increase in infrastructure investment for every unit of compute capacity.

Increased power and cooling costs

The high cost of AI stems in part from the fact that GPUs and AI servers require more, and more expensive power. AI racks can consume up to 10X more power (40–100+ kW per rack compared with ~5–15 kW) than traditional CPU-based servers depending on configuration and density.  This directly drives electricity bills, cooling, UPS capacity, and other rising costs.

Because AI servers generate so much heat, cooling systems may need to work harder and may require liquid cooling or advanced designs that add to both upfront and ongoing costs. The AllAboutAi article speculates that, “Large AI data centers consume up to 5 million gallons of water daily, equivalent to the daily water usage of a town with 50,000 residents.”

Exploding cloud costs

Multi-cloud and hybrid connectivity costs rise as modern architectures generate more traffic, redundancy, and inspection overhead. AI traffic growth, particularly the growth of east-west traffic may warrant the use of higher-speed links (40/100/400G) to create lower-latency infrastructures. GPU usage gets billed on a per second basis and idle GPUs can drive up costs as well.

Operational efficiencies will help offset investments

While it’s too early to cite definitive statistics for the positive impact that AI will have on data center ROI long-term, one recent recap makes several bold claims and predictions[4]:

• 35% of data center outages are prevented using AI-driven predictive maintenance tools
• Using AI for data center management could reduce operational costs by up to 25%
• 50% of data centers will use AI for automated troubleshooting by the end of 2026

Along with its impact on improving and managing performance, AI’s net impact on security operations may prove even more profound. Look for details in our next blog, “Visibility Helps SOC Analysts Win the Race to Leverage AI.

At the end of the day . . .

As far as we know, no single ROI calculator exists to help enterprises build reliable cost and payback analyses — but that won’t stop investments from happening. One thing we do know is that, like every major sea-change in IT and security, the mass adoption of AI will require complete (more and better) visibility across the entire enterprise environment and digital estate.

While change remains constant in the data center, the core value proposition for hybrid network visibility remains constant:

The more you invest in a data center, the more you benefit from building it on a foundation of fast, accurate, complete, and resilient visibility.

AI represents not only the next frontier but the ultimate use case or “poster child” for this fundamental truth.

Visibility Keeps ROI Goals in Sight

Building a scalable, modern network visibility infrastructure helps enterprises meet goals for performance, security, and ROI. AI can’t detect anomalies or initiate the right response if the data being used to train and inform decisions is incomplete or inaccurate or overwhelms your monitoring tools.

A modern network visibility platform helps businesses:

• Avoid downtime
• Prevent breaches
• Optimize operations and ROI  

Visibility provides:

• A foundation for scale
• A reliable source of truth for data used in monitoring, training, and day-to-day operation
• Actionable insight and oversight to maintain compliance, optimize workflows, and exceed goals for ROI 

Highlights include:

Deduplication optimizes tool utilization and ROI

Duplicate traffic typically represents up to 30% of traffic sent to security and performance monitoring tools due to a variety of factors like redundant sessions as multiple teams SPAN the same links. Mirroring the same traffic to multiple monitoring tools generates duplicate data that drives up traffic ingestion, bandwidth or cloud utilization costs.

Worse yet, sending unnecessary traffic overwhelms and overloads tools more quickly which can degrade performance. That means data used in detection and response becomes less reliable and leads to premature investment in more tool capacity.

Building operations on a strategic foundation like Keysight’s Network Visibility platform eliminates unnecessary duplicate packets and data flows helps minimize costs associated with ingestion, bandwidth/connectivity and storage – any element priced according to data volumes or solution capacity. As an example, if you typically mirror 15TB of data per day and use a visibility platform to remove 30% of duplicate traffic, you’d eliminate roughly 5TB per day and improve ingestion and storage costs by roughly the same percentage. Better utilization and performance would mean analysts could spend less time reacting to false positives.

Advanced filtering and traffic shaping accelerate analysis

Your network visibility platform also adds metadata and granular, intelligent filtering by application, user, device, location, and other variables your team can use to refine data even more for efficient analysis. In certain use cases, timestamping, header-stripping and other advanced visibility techniques help improve tool performance and make compliance workflows more efficient.

Running AI agents on packet brokers could cut deployment costs in half

 To offset these rising expenses, Keysight’s Application Fusion Program enables enterprises to run agents from their preferred monitoring solutions directly onboard our Vision network packet brokers (NPBs). Companies can deploy AI-powered monitoring agents from multiple Keysight alliance partners and avoid up to 50% of overall incremental CapEx and OpEx costs associated with:

• Infrastructure: High-density servers and related power/cooling requirements
• Networking: More east-west traffic, faster links, redundant connectivity  
• Security and compliance: Encrypted traffic inspection and audit requirements

Potential savings comes in many forms:

• CPU: At ~$10K–$30K per server, Integrating onto an existing platform can save tens of thousands to hundreds of thousands upfront if it avoids dedicated AI hosts.

• GPU: Companies using a GPU server approach could save ~$50k–$250k+ per node depending on their GPU model.

• Savings on licensing fees incurred when pricing scales in step with the number of nodes: AI stacks may charge according to the number of hosts and ingestion endpoints. Running the agent on the same packet broker that’s aggregating data from across your environment avoids excessive or escalating fees associated with adding clusters and ingestion tiers.

Consolidating around fewer, more reliable platforms saves time and effort as well as sheer operating costs making better visibility a clear requirement for scaling AI.

[1] Gartner Says More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026

[2] Enterprise AI Adoption: State of Generative AI in 2026

[3] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[4] Ai In The Data Center Industry: Data Reports 2026