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Vixul Inc We help tech services founders stop trading time and start building equity.

Vixul installs the positioning, pipeline, and AI-first systems that turn your firm into a stronger, more valuable business.

May was about foundations. The month's theme, "Building the AI-First Foundation", pushed at a question most tech service...
05/27/2026

May was about foundations.

The month's theme, "Building the AI-First Foundation", pushed at a question most tech services firms think they've already answered: what does an AI-native agency actually look like?

Everything we learned kept coming back to the same point: operating decisions, not technology choices.

Sam Melehy, Vixul Advisor, puts it plainly:

"An AI-native foundation isn't a tool stack, an org chart, or a model choice. It's the operating decisions a firm makes about how work gets done. Who decides what to build. How adoption gets measured. What compounds across engagements and what evaporates. The people getting this right aren't necessarily using AI more than everyone else. They're using AI inside a system designed to make the AI matter."

June picks up where May left off: Customization without chaos — what it actually takes to deliver bespoke work at scale without blowing up margin or quality.

What operating decision do you think most AI-native firms get wrong first?

Eid al-Adha Mubarak to all celebrating today. 🌙A day rooted in reflection, generosity, and gratitude that resonates acro...
05/27/2026

Eid al-Adha Mubarak to all celebrating today. 🌙

A day rooted in reflection, generosity, and gratitude that resonates across cultures and communities.

Wishing you peace and joy, from Vixul.

95% of enterprise AI pilots. Zero measurable P&L impact.MIT tracked 300 implementations. The number held.The models work...
05/22/2026

95% of enterprise AI pilots. Zero measurable P&L impact.

MIT tracked 300 implementations. The number held.

The models worked. The organizations didn't.

AI doesn't transform a broken org. It accelerates it — in whatever direction it was already going.

People, data flows, tooling, connected by an operating model the founder owns: that's the fix. It's not a technology decision.

What's the hardest layer to fix first — people, data, or tooling?

We're live. 🔴The roundtable on Hiring & Team Model for an AI-Native Agency is happening right now. Usman Ghani, Aater Su...
05/21/2026

We're live. 🔴

The roundtable on Hiring & Team Model for an AI-Native Agency is happening right now. Usman Ghani, Aater Suleman, PhD., and Sam Melehy are in the room and the conversation has started.

If you RSVPed, you know where to be. If you missed out? DM us for a link.

If AI is doing the work, what exactly are you hiring for?That's the question at Vixul's next roundtable.Guest speaker Us...
05/20/2026

If AI is doing the work, what exactly are you hiring for?

That's the question at Vixul's next roundtable.

Guest speaker Usman Ghani, Co-Founder & CEO of The Working Town, brings 20+ years in ERP and SAP, 40+ integrations, 80+ clients globally, and firsthand experience restructuring a delivery team for an AI-native world. He has a front-row seat to what it looks like when AI meets enterprise delivery at scale. He's done the work, and he'll share what it's taught him.

Then the floor opens. Same questions, your answers, your company.

Not a webinar. Not a panel you watch. A working conversation with founders and builders navigating the same inflection point.

📣 Hiring & Team Model for an AI-Native Agency

📅 May 21 · 🕙 10 AM Central · 📍 Online

👉 RSVP: https://lnkd.in/dydAe54c

No one has ten years of experience in AI-native work. The field is younger than most hiring managers' last performance r...
05/19/2026

No one has ten years of experience in AI-native work. The field is younger than most hiring managers' last performance review cycle.

That makes the traditional interview process almost useless. It was built to prove track record. There is no track record yet.

So what does the interview actually look like in AI-native tech services?

1️⃣ Replace the case study with a live build. Give them a half-defined problem and thirty minutes with their tools of choice. What do they reach for first? Where do they pause? What do they discard and why?

2️⃣ Ask about tool limits, not tool lists. Everyone can name the same five tools. Ask them where they hit the ceiling of a tool you rely on, and what did they do next?

3️⃣ Test output judgment before output delivery. Show them deliberately flawed AI-generated output and ask them to evaluate it before doing anything with it. Can they figure out what's wrong before they run it?

4️⃣ Ask how their workflow has changed, not what their workflow is. Anyone serious about this space has rebuilt how they work at least twice in the last six months.

The interview isn't about verification anymore. It's a window into how someone thinks when the ground is moving.

This is the conversation we're continuing at our roundtable this week.

📣 Hiring & Team Model for an AI-Native Agency
📅 May 21 · 🕙 10 AM Central · 📍 Online

RSVP: https://www.linkedin.com/events/7458577898019590144

Credentials measure past performance. In an AI-native agency, you're hiring for a future that's paradigmatically differe...
05/18/2026

Credentials measure past performance. In an AI-native agency, you're hiring for a future that's paradigmatically different from the past.

The work is ambiguous by default: undefined problems, imperfect options, no clear stopping point. Can your next hire frame a problem that hasn't been fully defined yet? Can they choose between three different options that are all partially wrong? Do they know when to stop building? Can they articulate why before they jump to what?

Everyone agrees judgment matters more than credentials in the abstract. The hard part is acting on it: passing on the pedigreed candidate who freezes in ambiguity, and hiring the less-credentialed candidate who sits with a half-formed problem and reasons their way forward.

Most agencies say they hire for judgment. Very few actually let credentials lose.

This is the conversation happening at our roundtable this week.

📣 Hiring & Team Model for an AI-Native Agency
📅 May 21 · 🕙 10 AM Central · 📍 Online

Register here → https://www.linkedin.com/events/7458577898019590144

05/15/2026

You can buy the tooling. You can license the models.
But you cannot buy your way out of the wrong team model.

After 40+ SAP integrations, Usman Ghani, CEO & Co-Founder of The Working Town, has seen this pattern more than any other: the team model is what determines the ceiling.

Building an AI-native agency means rebuilding how you hire, how you structure, and how you think about who does what when the AI is doing more. That conversation is harder than picking a model. And most agencies are still avoiding it.

We're bringing that conversation to a live meetup next week. Don't miss out!

📣 Hiring & Team Model for an AI-Native Agency
📅 May 21 · 🕙 10 AM Central · 📍 Online

Register on LinkedIn for the invite.

Every founder says "AI-first" now. It costs nothing to say. But the question customers are actually asking: what does th...
05/15/2026

Every founder says "AI-first" now. It costs nothing to say. But the question customers are actually asking: what does this change about how you work?

For most agencies, the honest answer is: not much.

We broke down 5 structural signs that your AI is bolted on, and what an AI-native structure actually looks like instead.

👉 https://www.vixul.com/blog/signs-your-ai-is-bolted-on-and-what-native-unlocks

SAP's Joule agents are generating real excitement. That's exactly when founders need to slow down and ask the right ques...
05/14/2026

SAP's Joule agents are generating real excitement. That's exactly when founders need to slow down and ask the right questions.

The promise is compelling: agents trained on business logic, embedded inside S/4HANA, automating workflows that used to require a room full of specialists. But compelling promises inside mission-critical systems deserve hard questions before rollout, not after.

Usman Ghani, CEO & Co-founder of The Working Town, has spent more than two decades inside SAP environments across 26+ countries. His questions cut straight to what most Joule conversations skip:

"Joule agents trained on business logic sound compelling until you ask three questions: How does the agent behave when it hits a Z-table it wasn't trained on? Who owns the audit trail when an agent executes a financial transaction across a multi-system landscape? And what happens to your data residency posture when the inference layer sits outside your S/4HANA boundary? These aren't edge cases — they're the first three things a serious operator should stress-test before any Joule rollout."

Z-tables. Audit trails. Data residency. None of these are exotic concerns. They are the operational reality of every enterprise SAP environment in production. The firms that treat them as afterthoughts will find out why they shouldn't.

Which of the three do you think gets overlooked most often, and what would you add to the list?

Usman Ghani started The Working Town in a single-room flat. First job: $80.Today, TWT has delivered 80+ SAP projects acr...
05/13/2026

Usman Ghani started The Working Town in a single-room flat. First job: $80.

Today, TWT has delivered 80+ SAP projects across 26+ countries, generated $12M+ in client value, and grown 100%+ year over year. What didn't change along the way: the specialization.

More than two decades inside SAP and enterprise ERP environments taught Usman something most AI vendors learn the hard way: putting AI inside a mission-critical system isn't a technology problem. It's a trust and governance problem.

Most AI implementations treat the ERP as a data source. TWT treats it as the operating system of the business — because that's what it is. 35,000 tables. 3,000 custom objects. Legacy configuration nobody ever wrote down. Business rules baked in so long ago that the people who built them have retired. Getting AI to work reliably inside that environment requires something no model can provide out of the box: earned trust.

Part of sharpening that thinking came from being inside a community of founders who pushed each other to build beyond delivery — to ask harder questions about what a defensible, scalable AI services business actually looks like. That's what Vixul gave TWT: not just a network, but a different way of thinking about the work.

The firms getting this right aren't the ones moving fastest. They're the ones who earned the trust to move at all.

What's the hardest barrier you've encountered when deploying AI inside enterprise systems?

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