When a phishing email hits your inbox and you click “report,” what happens next?
Until recently, that click triggered a cascade: a ticket gets created, a security analyst picks it up, spends 30 minutes to 2.5 hours investigating, checking links, detonating attachments in sandboxes, querying tools, hunting for leads across systems. Then they write it up and close the ticket.
Seventy percent of the time? It’s nothing. False positive. Wasted effort.
That analyst you just hired, the one who got into cybersecurity to hunt down bad guys and protect the organization? They’re spending most of their day doing repetitive investigation work that leads nowhere.
Nathan Burke, CMO at 7AI, just showed me what happens when you solve this differently.
The Four-Minute Investigation
On this week’s episode of Enterprise AI Unlocked, Nathan walked me through something that should make every CISO pause: 7AI’s autonomous security agents are completing investigations that used to take 30 minutes to 2.5 hours in four minutes. Same quality. Same thoroughness. But without burning analyst time on non-human work.
Here’s what’s different: instead of one generalist AI trying to do everything, 7AI built specialized agents, small, purpose-built pieces of LLM with specific missions. One agent checks links. Another detonates file attachments. Another queries endpoint data. They work in parallel, handling the entire investigation chain end-to-end, then hand results to humans only when something actually needs attention.
The result? Investigations that complete in four minutes. Security teams getting 25-50% of their time back. And analysts finally doing the work they signed up for: threat hunting, proactive defense, strategic security initiatives.
But here’s what caught my attention: this isn’t about technology. It’s about trust.
The Trust Curve
Nathan said something that reframed how I think about enterprise AI adoption:
“What I’m selling you is not a new piece of software to do things better or faster. I’m saying I’ve got this thing that’s gonna do the work you don’t want your people to do.”
That’s a fundamentally different value proposition than traditional enterprise software.
When you buy an expense management platform, you’re buying a process. You’re implementing a tool and teaching users how to follow workflows. The work shifts, maybe gets marginally faster, but humans are still doing it.
When you deploy AI agents, you’re replacing the work itself. The agents look at receipts, OCR them, categorize them, check your calendar for context, submit them on your behalf. They don’t shift work, they eliminate it.
That’s a much bigger trust hurdle to cross.
Nathan’s customers go through a predictable journey:
- Skepticism: “This can’t be real. AI can’t handle this.”
- Validation: “Wait, it actually works in our production environment.”
- Trust: “I can see every decision it made, and I can give feedback.”
- Strategic thinking: “Where else can we apply this?”
The moment that shift happens – from “Can your technology solve my problem?” to “Where else can we use this?” That’s when AI moves from experimentation to transformation.
The One Metric That Actually Matters
I asked Nathan the question I ask every guest on Enterprise AI Unlocked: If you had to pick one metric to prove AI’s value to your board, what would it be?
His answer was immediate: Time saved.
Not “alerts processed faster.” Not “reduction in false positives.” Time saved on people.
Here’s why that matters in cybersecurity specifically: there are between 1-3 million unfilled cybersecurity jobs in the US alone. It’s almost impossible to find good security analysts. You’ve got increasing attacks and alerts on one side, and a massive shortage of skilled people on the other.
So you’ve got these precious resources spending 70% of their time on manual, repetitive work that turns out to be nothing most of the time. Meanwhile, they want to be threat hunting, upskilling, doing strategic work, but they’re drowning in tickets.
Time saved isn’t just an efficiency metric. It’s unlocking capacity for work that actually matters. It’s retention in a talent pool where people can leave for another $20K across the street tomorrow. It’s the difference between responding and being strategic.
Nathan gave me an example that stuck: One of their customers challenged a security analyst to spend a week automating one thing that was wasting her time. She picked security questionnaires, those vendor compliance forms that are repetitive and time-consuming. A week later, she had it automated with AI. Done. In production. She got her bonus.
The CISO used that story to show the entire team what was possible. Now they’re all asking: “What else can we automate that we shouldn’t be doing manually?”
The Real Problem: Non-Human Work
This is where the conversation gets uncomfortable for a lot of organizations.
Nathan introduced me to a framework that reframes everything: human work vs. non-human work.
Non-human work is essential. It has to be done. But it doesn’t require human creativity, judgment, or strategic thinking. It’s checking links against threat databases. It’s detonating files in sandboxes. It’s writing queries and jumping between tools to gather data.
Human work is threat hunting. It’s investigating complex, nuanced security incidents that require context only humans have. It’s building relationships with the business so security isn’t “the team that sits in a dark corner saying no to stuff.”
The problem? Most organizations haven’t asked the foundational question:
What work do you want your people to do?
Not “what work needs to be done.” Not “what can AI handle.” What do you actually want your security team spending time on?
Once you answer that question, the prioritization becomes obvious. You look at everything else and ask: Why are humans doing this?
The 90% Failure Rate
Here’s the harsh reality: MIT research says that more than 90% of AI initiatives fail.
Nathan’s seen it happen. Organizations see the promise of AI, try to do everything at once, spiral out of control, burn budget, and have nothing to show for it.
The ones that succeed follow a simple pattern:
- Start with one painful area that has clear impact
- Scope it tightly so you can measure success
- Know your qualification criteria before you begin
- Prove it works, then expand
Don’t start with “What can AI do?” Start with “What are we doing that humans shouldn’t have to?”
Find that one pain point. The one that’s critical, high-value, clearly measurable, and achievable quickly. Solve it. Prove the ROI. Then expand.
J.P. Morgan didn’t deploy AI to 250,000 employees by trying to do everything at once. They started with clear use cases, measured obsessively, governed tightly, and scaled what worked.
7AI’s customers follow the same playbook. They start with one or two security use cases – phishing emails, endpoint investigations. They prove it works. They build trust. Then CISOs start asking: “What about cloud identity? What about this other workflow?”
The trust curve accelerates. But only if you build it right from the start.
Governance as an Enabler, Not a Barrier
I pushed Nathan on governance because in cybersecurity, mistakes can be catastrophic. How do you balance innovation with control when AI is literally on the front lines making security decisions?
His answer flipped the narrative I usually hear about governance:
“AI agents can actually be more governable than human analysts because every step is visible and auditable.”
Here’s what that looks like in practice:
When a 7AI agent investigates a phishing email, you can see:
- The agent that handled each step
- The input it received
- The query it sent to each tool
- The response it got back
- The determination it made
- Why it made that determination
Everything is auditable. Everything is immutable. You can copy-paste queries and validate them yourself.
Compare that to human analysts. If every person documented every step and every reasoning path for every investigation, they’d complete maybe one investigation per day. It’s not scalable. It’s not realistic.
With AI agents, you get more transparency, more consistency, and a complete audit trail that was never possible with humans alone.
Governance isn’t slowing down deployment. When you architect it right, governance enables scale because it builds the trust required to deploy autonomously.
The Uncomfortable Truth About Jobs
I asked Nathan to paint the picture: What does work look like when we’ve eliminated all the non-human work?
His answer was honest and thought-provoking.
At his last company, he managed a team of 43 people globally. At 7AI, his team is two people. As they scale, he’s asking: How many do I actually need?
It’s not that AI eliminates jobs. It’s that AI fundamentally changes what jobs look like.
In the past, you’d build specialist teams: product marketing here, demand gen here, brand here. Everyone stays in their lane. But that creates bottlenecks. Specialists end up waiting or saying “I only do this one thing.”
AI is enabling the rise of what Nathan calls the super generalist: someone who’s curious, capable of doing more than one thing, and empowered by AI to move across disciplines without needing to master every technical detail first.
You don’t need to learn all the little things to do something pretty well anymore. AI handles the repetitive, technical, rote work. You focus on creativity, judgment, strategy, and connecting dots across domains.
That’s the shift happening in the next 12 months. Fewer individual specialist roles. More ultra-generalists who can tackle broad challenges because AI removed the barriers.
Is that threatening? For some, yes. For others, it’s liberating. It depends on whether you’re someone who wants to stay narrowly specialized or someone who’s been dying to do more but couldn’t because of time constraints.
What J.P. Morgan and 7AI Have in Common
Here’s the pattern I’m seeing across enterprises that are actually making AI work:
1. They treat AI as an ecosystem, not a tool J.P. Morgan built LLM Suite as a centralized platform. 7AI architected specialized agents with governance built in. Both understood that scattered tools without infrastructure fail.
2. They measure from day one J.P. Morgan knows which use cases deliver ROI. 7AI’s customers can see time saved, false positive reduction, and analyst capacity unlocked. You can’t scale what you can’t measure.
3. They build trust through transparency J.P. Morgan updates LLM Suite every eight weeks and feeds it proprietary data with clear guardrails. 7AI shows every agent decision and makes everything auditable. Trust isn’t assumed—it’s engineered.
4. They start small and scale what works J.P. Morgan didn’t go from zero to 250,000 employees overnight. 7AI starts with one or two use cases. Both prove value, build trust, then expand.
The Real Playbook
If you’re deploying AI, especially agentic AI – in your organization, here’s the framework:
Start with the work you don’t want people doing Not “what can AI do?” but “what are humans doing that wastes their potential?”
Pick one painful area and prove it Don’t boil the ocean. Find the highest-value, most measurable pain point. Solve it. Show ROI.
Build visibility and governance from day one You can’t scale what you can’t see. You can’t govern what you don’t understand. Architect transparency into the system.
Measure time saved, not tasks completed The metric that matters is human capacity unlocked for higher-value work. That’s what executives care about. That’s what drives retention.
Scale based on data, not intuition J.P. Morgan knows which agents work. 7AI’s customers know which workflows deliver ROI. Build the intelligence layer so you can make decisions based on evidence.
What Comes Next
Nathan’s answer to where enterprise AI is headed in the next 12 months was refreshingly honest:
“It’s early innings. Twelve months from now is a lifetime away.”
What he’s seeing: adoption happening faster than anything he’s experienced in 20+ years in tech. 7AI went from launch to large enterprise customers in production in under a year. One Fortune 350 customer went from first conversation to production deployment in eight weeks, and now they’re bundling 7AI as a service offering to their global customers.
That acceleration is unprecedented.
But the companies winning aren’t the ones with the best AI. They’re the ones who solved the trust problem, the measurement problem, and the governance problem first.
Your Move
Here’s the uncomfortable question every enterprise leader needs to answer:
How many autonomous agents are running in your organization right now?
Can you name them? Can you measure their ROI? Can you prove to your CFO which ones justify continued investment?
If the answer is no, you’re building in the dark. And while you’re figuring it out, your competitors are measuring, optimizing, and scaling based on intelligence.
Nathan said it clearly: “Don’t start with ‘what can AI do?’ Start with ‘what are we doing that humans shouldn’t have to?’”
That question changes everything.
This article is based on Episode 2 of Enterprise AI Unlocked, where I sat down with Nathan Burke, CMO at 7AI, to discuss deploying autonomous AI agents in cybersecurity operations. Watch the full 47-minute episode on YouTube, or catch the first 15 minutes here on LinkedIn.