By Enterprise AI adoption dashboards look healthy. Production reality does not. Most organizations are still operating at the human-to-AI interaction layer, forwarding AI output with minimal review, while the agentic workloads on their roadmaps require a level of process discipline and operational maturity they have not yet built. The gap between wearing an AI-enabled label and running AI as a reliable digital worker is significant, measurable, and growing.
In this interview on TFiR, Rob Hirschfeld, CEO at RackN, breaks down where enterprise AI adoption actually stands, why agentic workloads demand more supervision than most teams expect, and what process debt means for organizations trying to move faster with AI.
Guest: Rob Hirschfeld, CEO at RackN
Show: TFiR
Here is what every platform engineer and enterprise architect needs to know.
Technical Deep Dive
Q: How much enterprise AI adoption is real versus AI washing?
Rob Hirschfeld, CEO at RackN, says enterprises are overshooting the mark broadly, with adoption numbers that look strong on the surface but mask a shallow implementation pattern. The dominant mode is still human-to-AI interaction, where people use AI tools to produce output and pass it along with little additional judgment or filter. Hirschfeld notes a correction is beginning, as audiences and colleagues start pushing back against unfiltered AI output, particularly AI-generated graphics in presentations and the recognizable cadence of AI-written prose.
“We’re finally starting to see some rebound from that perspective. AI graphics aren’t as welcomed in slide presentations as they used to be.”
Rob Hirschfeld, CEO, RackN
Q: What is the reverse centaur problem in enterprise AI?
Hirschfeld uses the term “reverse centaur” to describe an anti-pattern where humans serve primarily as a relay between AI output and end recipients, rather than using AI to augment or extend human thinking. In a healthy centaur model, human judgment directs and filters AI capability. In the reverse centaur, the human adds little value beyond transmission. He argues enterprises are training their organizations into this pattern by rewarding volume of AI-assisted output without evaluating the quality of human oversight applied.
“People are using AI instead of thinking. We have to separate AI thinking from human thinking and understand what AI does really well.”
Rob Hirschfeld, CEO, RackN
Q: Are agentic AI workloads ready for enterprise production?
Hirschfeld is direct: true agentic workloads, where AI agents process tasks as digital workers in the background with minimal human intervention, are not yet at the reliability level enterprises need. Organizations that have moved quickly to enable agentic systems have learned that AI as a digital worker requires substantial ongoing supervision, which changes the economic and operational calculus significantly. The value proposition of autonomous AI agents is real, but the supervision cost is underestimated in most business cases.
“The AI as a digital worker has a tendency to need a fair bit of supervision. And so you have to factor that in.”
Rob Hirschfeld, CEO, RackN
Q: Where is AI showing up reliably in enterprise systems today?
Hirschfeld identifies point-of-workflow AI integrations as the current strong suit: AI embedded into individual applications or workflows to perform analysis, classification, or summarization tasks. These bounded use cases are showing meaningful adoption. However, he also notes overshoot here, specifically the proliferation of AI-generated text in communications and documentation that has trained readers to recognize and distrust the AI dialect, creating a credibility problem for organizations that over-relied on unfiltered AI output.
“Putting AIs into individual apps or workflows to do analysis or things like that has been showing up quite strongly in systems.”
Rob Hirschfeld, CEO, RackN
Q: Why does process debt block enterprise AI progress?
Hirschfeld argues the deeper blocker for most enterprises is not AI capability but accumulated process debt: deeply embedded workflows, legacy decision-making patterns, and infrastructure commitments like VMware that teams are reluctant to unwind. Enterprises expecting AI to automatically accelerate them through this debt are misreading the situation. AI amplifies the speed of decision-making but does not dissolve the underlying process problems. Organizations need to rethink how they make decisions before AI can help them move faster.
“They need to be rethinking how they’re making decisions because they’re getting stuck on a whole bunch of ‘we’ve always done it this way.'”
Rob Hirschfeld, CEO, RackN
Q: What should enterprises do about legacy infrastructure like VMware while investing in AI?
Hirschfeld acknowledges the VMware replacement question is real and expensive, but warns against letting it consume organizational bandwidth at the expense of operational progress. Some organizations may be better served keeping VMware in place temporarily, accepting the cost, rather than diverting the engineering and leadership attention needed for forward-looking AI and infrastructure work. The key calculus is not what is ideal but what the organization can actually absorb given current capacity.
“You might keep VMware in place even though you don’t like them as a vendor. It’s expensive because you don’t want to take the time to do that replacement.”
Rob Hirschfeld, CEO, RackN
Q: What will genuinely surprise enterprise leaders about AI in the next wave?
Hirschfeld’s prediction is not about AI capability but about operational shock: the next generation of AI-integrated infrastructure will not behave like the current generation, even for teams running stable environments today. Organizations that assume current working systems will continue to work with new AI tooling without adaptation will be caught off-guard. He advises finding partners already doing that forward integration work rather than trying to absorb those transitions internally, because most organizations do not have the capacity to manage those shocks alone.
“If it’s working today, the next generation stuff showing up is not going to work the same way. You’re going to have to find partners who were already doing that work.”
Rob Hirschfeld, CEO, RackN
Resources & Documentation
- RackN, infrastructure automation platform focused on enterprise operational readiness and digital operations
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👇 Click to Read Full Raw Transcript
Swapnil Bhartiya: Every product today wears an AI enabled label. How much of that is real enterprise adoption versus just AI washing? What percentage of enterprises are actually using AI in meaningful production? Btw?
Rob Hirschfeld: Oh my goodness. I mean, AI adoption in the enterprise, as far as we can tell, is across the board. Probably people are overshooting the mark a little bit and turning themselves into reverse centaurs, meaning they have a lot of people who are using AI instead of thinking. And we’re in a correction curve where companies are learning how to use AI to take over and assist people. But there’s starting to be a lot more sensitivity over people who are just acting as a go between from an AI output and they just send you an AI output without much filter and thought. So we’re finally starting to see some rebound from that perspective. AI graphics aren’t as welcomed in slide presentations as they used to be. So we’re seeing a tremendous amount of adoption. And now we’re starting to see some people separate AI thinking from human thinking and understand what AI does really well. Along those lines, though, we’re still at an early point where a lot of those are human interactions, human to AI interactions. The real agentic workloads, where agents are processing and doing work in the background as digital workers is still a little bit off, where the people who start to enable that quickly learn that the AI as a digital worker has a tendency to need a fair bit of supervision. And so you have to factor that in putting AIs into individual apps or workflows to do analysis or things like that, that has been showing up quite strongly in systems. And we’re seeing a bit of overshoot there too, because we’ve done a really good job of everybody learning how to read mannered speech, that very specific AI dialect, and people are starting to push back against it. So as powerful as this is, using it correctly and keeping humans in the loop is absolutely essential.
Swapnil Bhartiya: Looking ahead beyond the hype, beyond AGI talk, what’s the one thing about enterprise AI that you think will genuinely surprise people?
Rob Hirschfeld: Yeah, I think there’s a real challenge here because a lot of the enterprises we’re talking to are still trying to deal with how do I replace VMware? And it’s a real question for them because they’re spending money on that. They have to fix their estate, but they’re grinding through it. We talk to companies that act like they have a lot of time to invent their own operations, processes and figure things out on their own, and where they think AI is going to suddenly show up and help them iterate faster through things where they’re already struggling with process and process controls. I think that when enterprises look at the current AI landscape, they need to not just be thinking about how do I use AI, but they need to be thinking about how they make decisions, how they, what they’re spending time operating on where they’ve been getting stuck before and rethinking. This is a theme in our conversation Swapnil. They need to be rethinking how they’re making a decision because they’re getting stuck on a whole bunch of we’ve always done it this way. We love this process. It’s really deeply embedded. Right. We have to do it this way. And they’re missing the idea that they don’t have the time to reset and fix all their processes. They need to be moving, bringing their general operational knowledge up much more aggressively than they’re doing. And so I think that this isn’t just how do I use AI more effectively here, it’s how do I recognize that AI is going to be driving me to have to change my processes or improve my processes, move on, move faster on proven things. And you might spend more money on other systems. Right? You might keep VMware in place even though you don’t like them as a vendor. It’s expensive because you don’t want to take the time to do that. Replacement people need to make those decisions and move on. And where we started to bring this full circle is you can’t just assume because something worked in the past, it’s going to keep working in the future. Right? We are seeing across the board, from a operations perspective, on people who manage their own infrastructure. And I think you’re going to see this is true in cloud also, that if it’s working today, the next generation stuff showing up is not going to work the same way. And you’re going to have to be prepared, you’re going to have to find partners who were already doing that work so that you can smooth over those bumps because you don’t have time in your organization to absorb all those shocks.
