Most companies are trying to make AI smarter.
They should be making their data more trustworthy.

AI breaks when engagement data is missing context, inconsistent, or defined differently across teams. Cleaning it up later is slow, expensive, and never quite works.

There’s a better approach: lock down the ten processes where engagement data is created—so clean, usable data is captured by default.

When workflows are easy to use and impossible to mess up, AI starts working the way everyone expects it to.

In my latest article, I walk through the 10 processes you must lock down to fix your AI data problems—without ripping out your tech stack or adding bureaucracy.

Most AI implementations don’t fail because of the model.
They fail because of the data underneath it.

Before you invest in another AI initiative, it’s worth asking a harder question:
Is your data structured in a way AI can actually learn from?

In this article, I cover:
• The hidden Achilles’ heel behind most AI efforts
• The four customer-engagement AI use cases that actually matter
• How to fix data at the source—without ripping out your tech stack

If AI is on your roadmap, this is the part that determines whether it delivers ROI or just more noise.

Every team on your org chart has a wish list this time of year:
marketing wants alignment, sales wants better leads, creative wants fewer fire drills, finance wants clearer results.

You could buy them all gift baskets… or you could give them something they can actually use together: a shared way to see the customer lifecycle and describe the work they do to support it.

In this new piece, I talk about why a simple operating model (built around six plain-English questions) can be the one gift that keeps paying off long after the holiday season.

Most companies don’t have a “content problem.”
They have a context problem.

If every team describes audiences, channels, products, and requests in their own way… it doesn’t matter how good your tech stack is. Nothing connects.

That’s why I built the 6Qs—a simple, shared metadata model (Who, What, Where, When, Why, How) that makes every part of content operations faster, cleaner, and more scalable.

In my latest article, I walk through exactly how I’d implement it inside a complex org—starting with one process, proving value in weeks, and expanding from there.

If you’ve ever tried to streamline content ops, align teams, or automate workflows across Workfront, AEM, Salesforce, or Seismic… this one is for you.

If you lead marketing, RevOps, data, or content teams and feel buried under reporting requests, inconsistent tagging, or endless rework, this story will feel familiar. And it points to a reusable pattern you can apply immediately.

“What do you actually do?”
If you’ve ever stumbled through that question—at work or over Thanksgiving dinner—this article is for you. Meet the Customer Lifecycle Funnel, an easy way to explain your role, your impact, and how it all fits together.

Most organizations don’t struggle because they have too much data.
They struggle because that data can’t be reused, governed, or automated.

In my latest article, I introduce the 6Qs Framework—a simple, universal, and understandable way to describe every engagement: Who, What, Where, When, Why, and How.

Why this matters:

• Silos can keep operating as they always have—while their data becomes reusable across the enterprise.

• A small, finite vocabulary (about 1,200 terms) can combine into more than 3 trillion unique engagement descriptions.

• Plain-English classifications make engagement context easy to interpret and apply.

The 6Qs is a mapping layer, not a replacement. It doesn’t change data—it describes it. And that shift unlocks clarity, speed, and scale across marketing, sales, product, and service.

📩 Read the full piece in my newsletter: The 6Qs: A Smarter Way to Plan, Create, and Deliver Engagements

If your fix for data chaos is “break down silos,” you’re solving the wrong problem.
Silos create depth; the issue is how they connect—especially as the distance between data creation and data use grows.

This article covers how to:
• Capture context at creation (not months later in a lake)
• Translate local terms into shared meaning via the 6Qs
• Make mapping painless inside existing workflows
• Govern IDs so meaning travels with the data

When faced with complexity, the natural response is to try and "keep it simple." The challenge is, simple is a lot harder than it sounds. Find out how to embed real simplicity into your organization in my latest newsletter article.

More data, fewer decisions?

Every week I sit with Go-to-Market leaders facing different obstacles—but one shared blocker: data. Too little. Too much. The wrong kind. Too late. Scattered. In a digital business, why is data still so painful?

In short:

The transition from data scarcity to data abundance (and why GTM lagged)
Why purpose-built definitions break across teams
The Schema6 approach: Finance-level rigor + Ops-level granularity to address the data challenges unique to GTM.

If you’re serious about automation, personalization, and AI—start with a new approach to data, not another tool.

CMOs manage a maze of specialized teams, but without a shared language, collaboration breaks down. This post explores how Schema6 helps unify strategy, creative, and execution through a simple, structured framework—driving clarity, efficiency, and better outcomes.