Learn from MISTAKES with AI | Tech Transformation | Innovation | Cloud | DevSecOps

Are you struggling to demonstrate the business value of technology?

With over 25 years of experience driving innovation and architecting complex systems, I've seen fantastic technology fail because it did not adequately address the user needs of the people it was built to serve.

I specialize in creating clarity amidst complexity - bridging the gap between technology capabilities and business priorities. My superpower is quickly grasping the heart of ambiguous problems and rallying teams to deliver solutions that create measurable value.

If you are a technology or business leader tired of:

❌ Chasing the latest hype while struggling to justify budgets

❌ Translating technobabble for skeptical executives

❌ Watching projects derailed by misaligned priorities

I can help you cut through the noise to build innovation strategies and technology roadmaps that move the needle for your organization and fully leverage AI and automation.

My approach combines systems thinking, design practices, and technical fluency to:

🎯 Uncover the root causes of business problems

🎯 Rapidly validate solution ideas

🎯 Deliver technology that users love

🎯 Continually optimize to drive outcomes

I've applied this formula to industry leaders like PepsiCo and helped clients build and integrate critical IoT, cloud, data, DevOps, and AI capabilities. 

Let's connect for a discovery call to explore if my expertise can help unlock value in your technology investments.

Daniel Lemire

You want a real AI mistake story?

I set up Claude Code on my new NVIDIA Jetson AGX Thor. Hit an issue immediately. In retrospect it probably not smart to run a coding agent on the same machine that's also running a local model. 

Ok, let’s try again and keep ‘em separated.

Got everything running. OpenAI Codex + GPT-5.2 as the main agent, Anthropic sub-agents for the heavy lifting. I tell Claude explicitly: "Use these agents for this project. Always refer to it by this name."

Claude says: great.

Time passes. I check in.

"How's that project going?"

"Oh, I haven't been working on that."

No alarm. No flag. No warning.

"Alright, let's get moving on it."

"Will do." 

Then proceeds to use GPT-5.2 !! (not the Anthropic agents I specified.)

What happened next? We exhausted the Codex tokens. And then everything broke. I had asked for a fallback to the local model but that wasn’t configured with a large enough context window to work with OpenClaw. 

This is the part nobody talks about when they say "just tell the AI what you want."

Context windows drift. Agents forget. Token budgets run dry in the background while you think work is happening.

I found a way to turn this into an asset. But first I had to live through the mistake.

That's the beauty of AI MISTAKES. Right?

Go make some.

ServiceNow AI Consultant | AI MISTAKES | Transformation | DevOps

GPT-5.3 instant. Gemini 3.1 Flash Lite. (That's just today)

Every week, we get a new capability. 

Skills. Agents. Interfaces. Apps.

Here's the problem: People often don't really know what to do with it all.

Model choice is a thing, as are all the other new things that arrive apace.

But if your team can't turn that choice into value, you're just adding complexity.

I see this constantly in enterprise AI: Companies and their knowledgeable experts obsess over which model to use rather than asking what problem they're trying to solve. 

Especially when it comes to advanced AI models, we all want the latest, the most accurate, the fastest, the cheapest...

The real problem is neglecting to define success.

The best AI strategy isn't about the model. 

It's about:
→ Knowing your use case
→ Picking the right tool for the job
→ Shipping something that works
→ Iterating based on real feedback

Stop optimizing for choice. Start optimizing for impact.

We are no longer talking about just an AI chatbot, or even an AI agent. 

This is the help I've been excited about Generative AI making real.

For years, people have been talking about AI as a tool – something you use, something you prompt. What we’re building at  ServiceNow is different – where AI is a true teammate working alongside you. 

We’re launching two new agentic innovations: ServiceNow EmployeeWorks, and near and dear to my heart (and team), ServiceNow Autonomous Workforce! 🚀  

With ServiceNow Autonomous Workforce, AI specialists are augmenting human teams by actually executing jobs – coordinating across systems, making decisions, acting with precision. Because ServiceNow combines AI intelligence with workflow orchestration, AI specialists can interpret a request, decide the right action using business context, and execute autonomously across systems with governance built in.

It’s a new class of digital workforce designed to earn trust every step of the way. Built on our AI Platform. Grounded in our Knowledge Graph. Orchestrated through Workflow Data Fabric. Governed by AI Control Tower.

We’re just getting started! Kudos to everyone on my amazing team who has been working behind the scenes to bring this to life for our customers. Julie Black Greg George Simon Fauvel Amanda Joslin  

Check out my video with Bhavin Shah below and learn more: https://lnkd.in/d56whgXh

Not AI hype. 

Not AI in theory. 

AI you can actually use. 

Register now and let's get to work. 

Seriously. I'll see you there, come on, let's go!

https://lnkd.in/gTj6hDXf

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You've seen it, you visit a website that gives you that great new chatbot experience, and it tells you what to do. 

What you probably haven't see is AI that does it.

Does what?

Expense reports.

Drafting emails.

Handling service desk tickets.

It's not just a platform. It works! 

Let's put AI to work, so you can focus.

Join us live on February 26 to see it in action. https://lnkd.in/gkhd-GGi

The future isn't waiting for your permission to arrive.

Right now, AI is rewriting the rules of work, creativity, and problem-solving at a pace that makes yesterday's innovations feel ancient. 

Some people are paralyzed by the speed of change.
Some are trusting AI blindly, copying and pasting their way to mediocrity.
Others are sitting on the sidelines, hoping it all goes away.

Here's the thing about transformation:
It doesn't care if you're ready.

It's not all good though, so the only way to know is start. 

You need to get started. 

Think about it this way:
→ Every breakthrough started with someone willing to experiment
→ Every innovation came from learning what didn't work
→ Every expert was once a beginner who kept going

MISTAKES aren't just acceptable in this new world.
They're the curriculum.

When you learn to work WITH AI instead of being replaced BY it, something shifts.

You stop being a passenger.
You become a pilot.

The world needs people who can think critically about AI, use it responsibly, and create real value.

What's one AI experiment you've been hesitant to try? 

What's holding you back?

How can I help?

REST is great. 

CRUD is essential.  

But neither of these things means you’ll come away with a functioning app that is easy to use while also data-model compliant 
with governance enabled. 

Build Agent solves this.

For those evaluating vibe coding on ServiceNow: I ran the same app through five approaches and documented everything.

Three paths used Claude Opus from outside the platform. Namely, raw REST API calls, REST to build a workspace, and the ServiceNow SDK with its Fluent API. Two used ServiceNow Build Agent from within. Same brief, a Travel Request app.

Every approach created the data model in minutes. The UI is the hard part.

REST took 2 hours and 4 failed iterations to land on a working UI. The workspace attempt never finished. The SDK deployed the entire backend from a single prompt in 18 minutes and its Workspace Fluent API successfully created a native workspace, something REST couldn't achieve at all. But the custom UI page and dashboard still couldn't reach parity with Build Agent despite multiple iterations.

Build Agent delivered a working custom UI from one prompt in 20 minutes. A full workspace with dashboard, charts, business rules, Polaris, mobile-ready, upgrade-safe. All within five prompts, under 15 minutes.

The AI models are equally capable. The variable is where the agent operates. Build Agent is fastest for interactive building. The SDK is the path for teams who need Git, CI/CD, and promotion pipelines. Both are platform-native. Both sidestep the friction that REST hits.

Full comparison, iteration artefacts, and every limitation documented, see comments for the GitHub repo in case you want to reproduce this.

The Cumaean Sibyl wrote her prophecies on oak leaves and let the wind decide how they landed. Most of us speak in vibes now.

Manu Rehani is a pioneering innovator in the field of Applied Behavioral & Cognitive Linguistics. 

He is an inventor, mentor and board advisor for a number of startups in Texas, California and the EU. After successfully exiting two tech ventures he’s currently focused on developing a new class of emotion-aware AI for general application in custom GPT’s and LLM’s, and specific use in mental health and wellness applications to foster a healthy relationship with self and others through AI. 

His significant prior achievements include authoring patents fundamental to cloud storage and AI language models, and he plays an active role in mentoring at various incubators, at the University of Texas and in the Austin area.

Manu R.

Are we approaching the last year of meaningful work?

Before answering that, I’m more curious about something else: What does meaningful work actually mean to you? When was the last time you genuinely felt that what you do matters?

It’s not every morning that we wake up thinking, “I can’t wait to work.” Let’s be honest. And with AI entering almost every corner of what we do — bringing both anxiety and incredible convenience, the tension feels real. It’s a double-edged sword.

My experience: Working on HeyIssa has felt different from my earlier projects. The team is smaller, and I’ve been directly involved in almost everything... the structure, the copy, the flow. It’s still in the waitlist phase, but I’ve spent time going over the sign-up page more than most people probably would. Rewriting lines. Simplifying what we’re trying to say. Making sure it doesn’t overpromise. One evening, while adjusting a sentence again, I realized I wasn’t doing it for optics or growth. I just didn’t want it to feel sloppy or insincere. That’s when it felt meaningful. I cared so much about getting it right.

Care to share how you define "meaning"? I’ve been exploring this question through the lens of AI, identity, and judgment. 

Article coming soon; tune in here -  https://lnkd.in/gUu7u6Sf

Manu Rehani

Founder | Inventor | Advisor | Mentor | Interdisciplinary Innovator

Y2K was a glitch. 2012 was a myth. But what if 2026 being the “last year of meaningful work” isn’t either?

Chad Hurley's lament on X has been sitting with me ever since I heard it. This piece is an attempt to take it seriously... as a question about how work is changing, and what remains deeply human when machines begin to understand us.

“Hope everyone enjoys their last year of meaningful work.” - Chad Hurley, YouTube CoFounder Chad Hurley said it lightly, almost as an aside. It drew a

Open article: The Last Year of Meaningful Work? by Manu Rehani • 6 min read

Last week, I wrote about a line from Chad Hurley that lingered longer than expected: “Hope everyone enjoys their last year of meaningful work.”
It sparked a lot of conversation. But when you look at the early data, the story becomes more nuanced.

A recent brief from The Budget Lab at Yale suggests that most jobs aren’t single tasks. They’re bundles of different activities... and AI tends to affect only some of them.

So if this isn’t the last year of meaningful work…what is actually happening to work? I explore that question in the latest issue of The Manuscript. Read on:

Hello again, and welcome back to The Manuscript. Let's backtrack and pick up the discussion on what we've gone over the last week... “Hope everyone en

Open article: If This Isn’t the Last Year of Meaningful Work… by Manu Rehani • 5 min read

If This Isn’t the Last Year of Meaningful Work…

Last week I wrote about a line from Chad Hurley that stayed with me longer than expected... until now! (“Hope everyone enjoys their last year of meaningful work!” he said)

The piece explored a question I’ve been sitting with: if AI begins to handle more of the execution in knowledge work, does it change what we mean by meaningful work?

While working on the next article, I came across an analysis from the The Budget Lab at Yale that looks at how AI exposure varies across occupations. One chart in particular caught my attention.

It breaks exposure down by the share of women in different professions. A few patterns stood out:
- Occupations with the lowest AI exposure (construction, maintenance, physical trades) are still heavily male-dominated.
 - Occupations with mid-level female representation (around 30-50%) show the highest disagreement about AI exposure.
 - Majority-women professions split into two different worlds: office administration (more exposed) and healthcare/care roles (much less exposed).

I attached a screenshot but I would want you to visit their analysis so you can see for yourself: https://lnkd.in/gwpmijgP

But I guarantee, the data, It’s interesting. The impact of AI may depend less on gender itself and more on the structure of the work being done. 

As we mark Women’s History Month, it’s a good moment to reflect on how many different forms of work women contribute across industries, and how those roles continue to evolve alongside new technologies.

More soon!

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