Before He Opens His Laptop, AI Has Already Run His Day

A conversation with John Head, Chief Evangelist and Strategic Account Manager at Netrix Global.

See John’s Newsletter here: Head's Up - Enterprise AI: A read for leaders deploying AI at scale. What shipped, what broke, what to do about it. Less hype, more execution.

John Head has been in enterprise technology for thirty years. He started writing code in the Lotus Notes era, shifted into consulting and advisory work, spent two decades building a boutique firm that was eventually acquired by Netrix — a global IT consultancy with around a thousand people. He has advised CIOs, run digital transformation programs, and watched every technology cycle from workflow automation to cloud to RPA to AI.

When someone with that kind of history tells you he was an AI skeptic as recently as late 2024, it's worth understanding why.

We had John on the latest episode of Building for Others, the podcast co-hosted by elvex CEO Sachin Kamdar and Head of Marketing Doyle Irvin. The show has one rule: to get on, you have to have made an actual thing that real people use. No hype, no demos that don't exist in production. John qualified.

From Manual Grind to a Morning Routine

Every Friday morning for years, John spent thirty to forty-five minutes doing the same thing: combing through Spotify, YouTube, and a handful of music sites to find new metal and hard rock releases, then putting together a list to post in a Discord server he'd been part of for twenty years. The group had a standing Friday thread called New Music. It was personal, something he cared about, and it ate his morning breakfast time. At work, he was doing the same kind of thing at larger scale — research, prospecting, weekly reports, all of it requiring the same careful, repetitive information gathering.

He had a ChatGPT subscription and used it occasionally. But he was skeptical of the "AI" label. His view, formed over three decades of watching Gartner rename automation every few years, was that this was the next iteration of a cycle he'd seen before.

Then someone showed him what agentic AI actually looked like — tools that go beyond simple chat to plan, delegate, and run on a schedule. Platforms like elvex, Claude Cowork, and others in that category were starting to make this real. He built a scheduled agent that would go out every Friday morning, find new releases across the sources he trusted, and surface a curated list automatically. What had taken thirty to forty-five minutes of manual research now happened in the background in under a minute — and over time, the agent learned his preferences and got sharper.

"Now imagine if I could do that with the weekly reports I do at work."

One tool doing one real thing, and then the immediate, intuitive translation to where it actually matters.

Daily Industry News Briefing

Once that door opened, John didn't stop at the playlist.

He built a morning intelligence brief that surfaces enterprise AI news every weekday — case studies, market trends, governance and security stories — filtered and prioritized for his audience. He still writes his own LinkedIn posts; the discovery is what's automated. "Data discovery can be automated as long as there's a way to fact-check it," he said.

Pre-Meeting Research

He built a pre-meeting research system. Every person he's meeting that isn't an employee gets a full dossier automatically generated before the meeting — LinkedIn activity, board and advisory roles, philanthropy and community involvement, mutual connections, recent news, and a set of conversation hooks. The whole thing runs on a calendar trigger, takes five to ten minutes in the background, and lands on his phone before he walks in the door.

Midway through the episode, John demonstrated this live — pulling up a profile his agent had built on Sachin during the recording. It was accurate, it was deep, and it had done in minutes what would previously have taken an hour of manual research.

"Every meeting I go into, it generates one of those and comes through," he said. "That's my process."

Networking Automation

He built a networking automation on top of that: when introductions come in through a group he's part of, an agent handles the initial back-and-forth, responds to scheduling emails, books a Calendly slot, and coordinates logistics. He was at lunch with one of those connections recently when the person mentioned how much they'd enjoyed the scheduling conversation. They hadn't realized they weren't talking to John.

LinkedIn Performance Dashboard

There's also a LinkedIn performance dashboard — a real-time tracker of which posts are getting engagement, what's resonating, what's falling flat. He described it with visible excitement mid-episode: "I just click on it and it shows me the last week or last month, here are the stories getting the most clicks, the most reshares."

What This Actually Means for Enterprise

John advises mid-market companies in manufacturing, construction, logistics, and transportation — industries that don't appear in the AI hype cycle, but where the ROI case is concrete and real. His personal AI stack is a proof-of-concept he uses constantly in client conversations.

The translation he makes is explicit: if a scheduled agent can curate metal releases, it can curate inventory variance reports. If it can build a dossier on a networking contact, it can build a pre-meeting brief that pulls from your CRM, call transcripts, AR data, and every note anyone at your thousand-person company has ever taken on that account.

"It is literally the dream of every salesperson to walk in with on their phone a true profile and history update," he said. "I think we're there. You just have to build."

The broader point John keeps returning to cuts against the way most enterprise AI is sold. The tools themselves have changed, but the fundamental behavior — making a computer handle the repetitive cognitive work so a human can focus on the parts that require judgment — is the same thing he was doing in the 1990s. What changed is that the cloud got big enough, the data was already there, and the processing got cheap enough.

"What I kind of feel like is it's like a puzzle — all the puzzle pieces came together," he told us. "And it's not just that what OpenAI did with ChatGPT changed everything, but the fact that everybody else could respond so quickly tells you that everybody was on the verge."

2024 Was Design. 2025 Was Pilots. 2026 Is Proof.

A thread running through the whole conversation is accountability. John framed it simply: "You had an ROI conversation when you implemented your CRM five years ago. Why aren't you having that conversation with AI today?"

His read: 2024 was design, 2025 was pilots, 2026 is proof. Boards and PE sponsors are asking what they actually got for the spend. The answer "we couldn't get ROI" is no longer credible when the tools are this capable. The question is whether enterprises are willing to apply the same discipline to AI they'd apply to any other technology investment.

The companies that figure this out, in John's view, aren't the ones with the most licenses or the most Copilot seats. They're the ones where someone — one person, often — figured out how to make the tools run in the background and translated that from personal productivity to team leverage.

That's where he's focused. And from the conversation, he's further along than most.

About John Head

John Head is Chief Evangelist and Strategic Account Manager at Netrix Global (https://www.netrixglobal.com), a global IT consultancy focused on managed services, security, cloud, data, and AI. He has thirty years of experience in enterprise technology and specializes in AI advisory for mid-market companies. You can follow him on LinkedIn (https://linkedin.com/in/johndhead).

Building for Others is a podcast produced by elvex and co-hosted by CEO Sachin Kamdar and Head of Marketing Doyle Irvin. Each episode features practitioners who have built AI tools and workflows that real people actually use.