Signal & Horizon

03/25/2026

Your AI program dashboard is green across the board.

And that might be the most dangerous thing about it.

Active users up. Sessions climbing. Adoption metrics on track.

Here's what those numbers can't tell you: whether your organization is actually making better decisions. Faster decisions. Decisions that compound into competitive advantage over time.

They can't tell you that because they weren't designed to. Adoption metrics measure what people did with the tool. Not what changed as a result.

A workforce of 10,000 people using AI to write better emails isn't a transformation. It's an expensive spell-checker. And it looks identical on your dashboard to a workforce using AI to fundamentally accelerate how it processes intelligence and makes decisions that matter.

The organizations that will define their industries over the next five years aren't optimizing for adoption rates. They're optimizing for decision velocity — how fast they can move from signal to insight to action, and how much smarter they get every time they do it.

Those are completely different things. And the gap between them is where most enterprise AI programs are quietly disappearing.

I wrote about this in depth — what decision velocity actually looks like, why adoption metrics are politically convenient but strategically dangerous, and the challenge every AI program leader should run on their own scorecard right now.

Read it. And if it lands for someone you know, send it their way.

🔗 Article:

Why utilization dashboards are quietly undermining enterprise AI strategy — and what decision velocity actually looks like

03/15/2026

Most AI transformations aren't transformations at all.

They're automations. Of the same broken structure. With a bigger budget and a better press release.

Here's the uncomfortable truth nobody wants to say in the strategy meeting:
Your org chart is older than your AI strategy. And AI doesn't change org charts — it inherits them.

That means every siloed team, every misaligned incentive, every decision that takes six approvals and three weeks — AI just runs on top of all of it. Faster. At scale.

You didn't transform the organization. You gave it a turbocharger. And now it's reaching the wrong destination more efficiently than ever.

The organizations actually winning with AI figured something out early: this is an organizational design problem first. A technology problem second.

Most companies have that sequencing exactly backwards.

I wrote about this in depth — the org chart problem, the autonomy illusion, and the question every leadership team should be asking before their next AI strategy session.

Read it. Share it if it hits a nerve.

And if you want this kind of thinking in your feed every week — no hype, no vendor talking points, just the signal that matters — subscribe to Signal and Horizon.

🔗 https://substack.com/home/post/p-190993197

📩 Subscribe: substack.com/

03/13/2026

I put on a pair of AI glasses this week.

And something clicked.

Not the device. My thinking.

For years, the AI conversation has been a screen conversation. Chatbots. Prompts. Copilots. You go to the AI. The AI waits for you.

That era is ending.

AI is leaving the building.

It's embedding itself into factories, vehicles, bodies, and buildings. It's not something you consult anymore — it's something that moves with you, sees what you see, and acts in the physical world in real time.

Most executive teams are not ready for what that means.

In my latest piece, I cover:
→ What ambient AI actually feels like (and why your phone is no longer the interface)
→ How manufacturing, automotive, and physical industries are being transformed right now — not someday
→ The 5 predictions every leader should understand for the next 12-24 months
→ 6 things executives need to do before this wave hits their organization

The physical AI era isn't coming. It's already here.

The question is whether your organization is building for it — or about to be disrupted by it.

🔗 Read the full article here: https://bit.ly/4usLB1N

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03/10/2026

There's no shortage of AI content.

There's a significant shortage of AI thinking.

Every week, leaders and practitioners are buried under a flood of hot takes, vendor hype, and breathless predictions — with very little that actually helps them make better decisions or build better organizations.

That's the gap I built my new Substack page to fill.

Each week, I publish one in-depth article on enterprise AI, emerging technology, and innovation — written for people who need to think clearly and act decisively in a rapidly shifting landscape.

No hype. No doom. No content optimized for clicks.

Just rigorous, experience-backed analysis on:
→ AI strategy, governance, and scalable adoption
→ Emerging tech that actually matters — and why
→ How organizations are winning (and failing) at innovation

If you're a leader setting AI strategy, a product builder working on the frontier, or a practitioner trying to make AI work at scale — this is for you.

I just launched and would love to have you as one of the first readers.

🔗 https://substack.com/

Join me, shall you?

03/06/2026

The AI platform landscape is evolving quickly — and even the U.S. government and the Pentagon appear to be shifting their center of gravity away from Anthropic and toward OpenAI in several initiatives.

For enterprise leaders, this raises an interesting question:
Which models and platforms are actually becoming part of our daily professional workflows?

In my own work leading enterprise AI and emerging technology initiatives, OpenAI has become my primary daily platform. Not because it's the only strong model ecosystem — but because it has proven to be the most practical for how I work.

In my professional workflow I regularly use it for things like:
• Strategic thinking and synthesis — shaping ideas, frameworks, and operating models for enterprise AI adoption
• Writing and thought leadership — developing articles, executive briefs, and content for leaders navigating AI transformation
• Rapid research and signal detection — exploring emerging technologies, trends, and patterns across the AI ecosystem
• Structured problem solving — pressure-testing concepts, refining narratives, and exploring multiple solution paths

In other words, it has become less of a “tool” and more of a thinking partner that accelerates clarity.

That said, the model ecosystem is diversifying fast — Claude, Gemini, open-source models, domain-specific AI platforms, and enterprise copilots are all evolving rapidly.

So I’m curious what others in the community are actually using in practice.
Not just which platform — but what you’re using it for in real work. And, if you're open to sharing in the comments — what are you using and what do you use it for most? Strategy, coding, writing, research, copilots, something else?

02/18/2026

Most enterprises aren’t ready for Agentic AI. Not because the models aren’t good enough — but because operating models aren’t ready for delegation.

We’re rolling out copilots, chaining tools, and calling it “agentic.” That’s still assistive automation.

True Agentic AI forces uncomfortable questions about: decision rights, guardrails, rollback, ownership, observability, and cost control.
Agentic AI won’t reward speed alone.

It will reward operating maturity.

Before you deploy agents, pressure-test your operating model: decision rights, guardrails, rollback, ownership, and observability.

If those aren’t clear, you’re not behind on tooling — you’re behind on readiness.

Full breakdown in the article:

There’s a growing assumption in boardrooms that Agentic AI is just the next logical step after copilots.

08/16/2025

My New Book Is Here – Beautifully Human: Designing AI with Soul and Purpose

I’m thrilled to announce the release of my new book, now available on Amazon Kindle for just $.99 THIS WEEK ONLY (through 6AM Monday, 8/22) 👉 https://www.amazon.com/dp/B0FMLC95Y2

AI is no longer just about algorithms and hype. The future of AI depends on how we design it for people—with trust, usability, and measurable business impact at the center.

This book is a practical playbook for leaders, strategists, designers, and technologists who want to move beyond theory and start building human-centered AI that works in the real world.

Inside, you’ll find:
✅ Case studies from enterprise AI adoption (the wins and the lessons learned)
✅ Frameworks for evaluating, governing, and scaling AI responsibly
✅ Methodologies to bridge business goals, design needs, and technical ex*****on
✅ Templates & tools you can use immediately in your own projects (with links to downloadable resources)
✅ Actionable lessons for executives, UX pros, architects, engineers, and developers alike

Whether you’re guiding strategy, shaping user experience, architecting solutions, or writing the code, Beautifully Human will give you the clarity, tools, and confidence to:
- Align AI to business outcomes
- Design experiences that foster trust and adoption
- Build and scale AI systems that are resilient, responsible, and impactful

This isn’t just a book to read—it’s a book to use. 🔥 Don’t miss out: For this week only, get the Kindle edition for $.99 (offer ends 6AM Monday, 8/22).

👉 Grab your copy here: https://www.amazon.com/dp/B0FMLC95Y2

07/28/2025

The AI Blind Spot

AI is showing up everywhere in Fortune 100 insurance companies — copilots, RAGs, chatbots, underwriting tools, claims automation.

The upside? Efficiency, speed, better CX.
The risk? A regulatory, legal, and reputational minefield that most leaders aren’t seeing coming.

In my work and experience, I keep seeing the same five high-impact risks:
- Regulatory Non-Compliance – State insurance laws, NAIC, FTC, EU AI Act… all coming fast.
- Data Privacy & Security – PII/PHI in AI pipelines is a breach waiting to happen without strict controls.
- Bias & Discrimination – Historical data doesn’t magically become fair because you use AI.
- Explainability Gaps – “The AI said so” isn’t a defense in front of a regulator.
- Operational & Brand Risk – One bad chatbot answer can undo decades of trust.

I built a risk matrix & heat map to help leaders visualize where these risks land. It’s clear: Innovation without governance isn’t leadership — it’s liability.

💬 Question for executives & boards: What’s your AI blind spot — and who’s accountable for seeing it before the regulators do?

Read my article here:

Fortune 100 insurers are rapidly deploying AI copilots, RAGs, chatbots, and decision agents. The promise? Faster underwriting, smoother…

07/27/2025

The AI model isn’t the problem — the power bill is.
(And most orgs have no idea how bad it’s about to get.)

Everyone’s busy fine-tuning prompts and scaling GenAI. But here’s what’s being ignored: AI is an energy hog. And that “cool pilot” you launched? It might quietly be draining your budget, crushing your ESG narrative, and hitting infrastructure limits no one planned for.

In my latest article, I break it down—clearly, urgently, and without the fluff:
🔋 How GenAI is stressing global power grids
📉 Why inference costs now eclipse training
🧠 What smart orgs are doing to avoid scale collapse
🌿 Why ESG teams are starting to ask tough AI questions
⚙️ How to build AI strategy that’s smart and sustainable
This isn’t about theory. It’s about traction, risk, and value.

If you’re in the C-suite—or trying to get there—you need to read this.

👉 Read it here: https://bit.ly/46wgGZ2
🔁 Share with your AI, infra, ESG, and finance leads.

07/27/2025

🔥 BREAKING: Your GenAI Strategy Isn’t a Tech Problem — It’s a People Problem (And if you don’t fix it now, you're going to waste millions.)

You can deploy the smartest models.
You can partner with top-tier vendors.
You can even have flashy demos that impress the board…

🚫 But if your people don’t understand, trust, or embrace AI — none of it matters. That’s not a tech failure. That’s a culture failure.

In my newest article, I lay out the uncomfortable truth every executive needs to hear: 🧠 Your organization’s AI IQ is too low to scale. This isn’t about theory. It’s about traction. And I’ll show you exactly how to fix it — with field-tested, measurable tactics drawn from the trenches of enterprise transformation.

Here’s what’s inside:
✅ How to launch a company-wide AI fluency program
✅ How to turn skeptics into champions
✅ How to measure cultural readiness
✅ Why fear is killing your ROI — and how to replace it with momentum
✅ Plus insights from my books Lean Innovation + Wired for Change

📣 If you lead AI, innovation, strategy, or transformation — you need to read this.

👉 Read the article here: https://bit.ly/474iEjp
🔁 Share it with the people responsible for enabling your teams, not just your tech.

07/27/2025

Most enterprise AI efforts fail—not because of bad tech, but because of bad strategy.

Too many companies chase the latest model or pilot without ever answering the big questions:

🔍 What’s the business outcome?
💡 Who’s going to use it—and how do they experience it?
🧠 Does our culture even want this change?

In my latest article, I lay out a clearer path forward:
👉 “From Ambition to Adoption: Building an AI Strategy That Actually Works”

This isn’t just about LLMs, copilots, or generative hype—it’s about aligning people, purpose, and platforms across the enterprise.

Inside the article, I break down:
✅ How to move from pilot purgatory to operational impact
✅ What Gartner’s 7-pillar AI Maturity Model reveals about where you are (and where to go)
✅ Why experience design is the real accelerator of adoption
✅ How culture, literacy, and governance determine your ceiling—not just your tools

Whether you’re building, buying, or scaling AI inside your org—this is the practical blueprint for doing it right.

📖 Read the article now → https://bit.ly/4l2elZt

Let’s make AI real. Not just possible.