CIO Influence Interview with Josh Kindiger, President and COO at Grokstream

Josh Kindiger, President and COO at Grokstream chats about the evolving ITOps ecosystem and how AI is impacting end to end IT management cycles in this CIO Influence Interview:

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We’d love to hear about the top highlights of Grokstream’s predictive IT ops solution. How does it benefit IT management protocols and IT teams?

Grokstream is focused on helping IT teams get ahead of incidents before they happen. Our platform delivers Predictive IT Operations using a unique Cognitive AI architecture that combines predictive, causal, and generative intelligence. It’s a fundamentally different approach from traditional AIOps platforms that rely heavily on static rules and topology maps.

With Grok, we don’t require teams to maintain a perfect (Configuration Management Database (CMDB) or predefined logic trees. Instead, the system starts learning in the first week, automatically identifying root causes, surfacing predictions up to 48 hours in advance, and compressing noise dramatically—often by 3x compared to other platforms.

Grok isn’t designed to perform automation just for the sake of automation. It’s built to explain its reasoning, demonstrate business impact, and enable human-in-the-loop collaboration. Teams get real insight and orientation, not just alerts, and they can remediate faster with confidence.

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How is predictive IT changing the scope of how modern IT functions?

Predictive IT is flipping the traditional model on its head. For decades, ITOps has been reactive — waiting for alerts, figuring out what went wrong, and trying to contain the impact. Predictive IT is about giving teams foresight.

What’s really exciting is how this reshapes the function of IT. You’re not just responding to issues—you’re preventing them. You’re not just monitoring—you’re prioritizing and acting intelligently. With a predictive approach, teams can reduce escalations, improve SLA compliance, and spend significantly less time triaging incidents. It frees IT to focus on delivering value instead of managing chaos.

Can you share a few top benefits of self-learning AIOps tools and how IT teams can capitalize on them?

Self-learning AIOps platforms are designed to deliver real impact to IT teams. One standout benefit customers will appreciate is receiving real-time, root-cause predictions up to 48 hours ahead of incidents, with no rules or tagging required.

Another major benefit is Agentic AI that actively learns from telemetry and human interaction to guide, recommend, and even act when needed—always with operators in the loop.

Other benefits include significantly reduced alert noise through associative clustering and a combination of supervised and unsupervised learning and genAI technology that provides clear summaries, impact assessments, and intelligent recommendations for actions and remediations. Last but not least, another critical component of self-learning AIOps is person-driven analytics that explain how AI is helping both team and the business.

The key is that there is no need to rely on topology to connect the dots. Self-learning AIOps platforms can find patterns and causal relationships through behavior—not static blueprints. That removes a huge maintenance burden for IT teams and speeds up onboarding. Once these platforms start learning from your environment, the value starts compounding. From our experience, many customers can see measurable ROI in just a few months.

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What about the current state of IT Ops is broken, in your view?

The biggest issue we’ve observed is that the model most IT Ops teams use today just doesn’t scale anymore. IT environments are more dynamic than ever. They are cloud-native, hybrid, and constantly changing. Yet many AIOps tools are still built around static rules and topology-based correlations. Those methods are fragile. They break every time your environment changes and require constant upkeep.

The solution is to adopt an AI approach that not only doesn’t depend on perfect data models or manual tuning, but also learns in real time, adapts automatically, and evolves based on operator (human) feedback. This approach leads to fewer false positives, fewer repeat incidents, and far more confidence in the system. We have seen how it also can lead to an 80+% reduction in incident volumes, which frees up engineers to work on strategic projects instead of sifting through tickets.

In what ways will AI enhancements change the scope of IT ops and management in the next few years?

We’re moving toward a future where AI doesn’t just assist, it collaborates using AI agents that understand operational context, explain their reasoning, and can act autonomously within trusted guardrails.

The AI world is already heading in that direction. With human-in-the-loop agentic AI, systems learn continuously, allowing IT teams to gradually shift more responsibility to the platform without losing control. Over time, AI will become not just a tool, but a trusted partner in operations that proactively identifies issues, resolves them, and even helps guide strategic decision-making.

Any final thoughts on the future of IT management?

The future of IT Operations is intelligent, autonomous, and human-aware. IT leaders won’t be measured just by how quickly they react, but by how confidently they prevent.

We believe the most successful organizations will be the ones that embrace this shift by letting AI handle the complexity so that humans can focus on impact. From our perspective, this requires a platform that’s not only predictive, but explainable, adaptive, and deeply integrated across the stack.

The best part is that it’s not five years away. Organizations can realize this value today.