By Rekha Shenoy, CEO, BackBox
The pressure to use AI to cut costs is rising — to the point that AI is now reviving enterprise software discussions about build vs. buy. AI makes it easier to tinker, but AI inaccuracies and the inability to explain AI outputs top the list of concerns about AI risk. Is there a way to responsibly leverage AI to consistently deliver on mission-critical enterprise needs? That’s the dilemma network infrastructure leaders face right now.
More than two-thirds of enterprise networking activities are still performed manually, yet NetOps teams are being pushed straight to agentic AI — and told to do it themselves. Networks are the backbone that keep trading floors operational, energy flowing, patients receiving care, and food in grocery stores. An outage can quickly turn into a crisis. How can DIY possibly fit into mission-critical scenarios?
First Things First
Before jumping into Agentic AI, we must consider two points.
- Context as a bedrock for mission-critical applications. Your network infrastructure matters to your organization, customers, and partners. However, AI doesn’t come out of the box aware of your infrastructure. As the adage goes, “garbage in, garbage out.” Nearly 70% of enterprise data leaders say their data isn’t clear or trustworthy enough for AI, and 65% say it lacks the clarity and business context AI needs to be useful.
For NetOps teams, context is information about the network devices that ensure availability, compliance, and uptime. This includes information about the device, its configuration, compliance requirements, vulnerability scores, and business purpose. To leverage AI effectively, think of it as an intern that needs to learn your business before it can truly add value. Having a system in place that understands your infrastructure and serves as a unified source of truth, collecting and organizing this information in one place and keeping it up to date, ensures that AI consults the right resources before making recommendations.
- Compliance-ready automation. AI isn’t just providing answers to questions; it’s making changes. But it doesn’t intuitively know how to make changes that align with your internal policies and automated lifecycle management best practices. For example, mature compliance-ready automation includes the following steps:
- Make a change on a single device.
- Test the change to make sure it works.
- If the test is successful, apply the change to a larger group of devices.
- Create a backup so you can recover to a known and trusted state in the event of a failure.
- If the test is unsuccessful and the change doesn’t work, restore it to a prior trusted state and immediately send a ticket to the IT service management (ITSM) platform for investigation.
According to IBM’s 2025 Cost of a Data Breach Report, 97% of AI-related breaches occurred due to inadequate guardrails. This type of multi-step workflow isn’t available out of the box with AI. You need a system that provides these guardrails to prevent your network from being put at risk.
Without a unified source of truth and compliance-ready automation, every mistake can be catastrophic.
AI’s Sweet Spot
A network automation platform with the information and workflows tailored to your infrastructure and business saves valuable time building that essential foundation and gives you the confidence to take the next step with AI.
AI is now the smart intern, making recommendations based on a trusted source of information. For example, it can quickly answer questions such as: How is this system performing relative to its baseline? Is this device compliant with our internal policies or external standards, such as CIS benchmarks? Which devices are vulnerable to a new CVE, and which are a priority to remediate?
Additionally, rather than scaling automation unchecked, agentic AI follows current approval processes with guardrails and alerts that allow humans to confirm flagged issues before changes are made. Tasks such as configuration updates and backups, as well as vulnerability checks and prioritization, can be executed automatically following predetermined workflows that the NetOps team has created and approved.
The Impact on NetOps
With automation providing a trusted foundation, AI is less about coding and more accessible to network experts. How many routine tasks are your network experts currently handling manually? Consider using an alternative AI strategy before addressing more complex issues, such as service deployments or outage management.
For instance, the hundreds of hours spent each quarter analyzing vulnerability data on vendor websites to identify critical upgrades could be a task AI can perform more efficiently, reducing the risk of outages. From there, seek a reliable automation solution that understands your business and can enhance its AI capabilities. This approach will give you confidence in business continuity while delivering the efficiency you expect from AI.
The Road Ahead
There is a way to responsibly leverage AI to consistently meet mission-critical enterprise needs and cut costs, but it must be done in the right order. Use automation to create a unified source of truth and compliance-ready workflows. Then use agentic AI to generate significant gains in efficiency, productivity, and agility on top of that foundation.
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