If your Solution Design Reference (SDR) could talk, it wouldn’t be furious. It would just sigh. Why? Because no one’s opened it in months.

An SDR isn’t supposed to be some ceremonial spreadsheet you build once and never touch again. It’s the running record of how your analytics actually works. The decisions. The logic. The weird edge cases that made it into production at 11:47 p.m. on a Thursday.

This post isn’t glorifying the documentation as much as it’s about making it less mysterious, and way less painful. In environments like Adobe Customer Journey… complexity sneaks up fast. When no one remembers how the system works, things get confusing AND expensive. Let’s fix that.

This year’s Super Bowl wasn’t just a cultural moment — it was a positioning showdown between OpenAI and Anthropic. AI wasn’t hiding in feature lists anymore. It was the headline. OpenAI spotlighted OpenAI Codex and framed AI as acceleration… a way to build faster and remove friction between idea and execution. Anthropic took the opposite angle with Claude, positioning itself around trust and signaling resistance to an ad-saturated AI future.

Let’s pull the curtain back on a topic that rarely gets top billing in marketing or analytics conversations, yet quietly determines whether everything else works. I’m talking about governance.

Yes, that word. The one that makes people worry about red tape, approval chains, and someone saying “no” in a Slack thread.

But if you work anywhere near Marketing Technology, Revenue Operations, or Advertising Technology, governance and security aren’t optional background tasks. They’re the difference between a data stack that earns trust and one that slowly erodes it.

Time for some honesty… your reports don’t actually tell the truth about customers. There, I said it. They tell the truth about whatever identity system you fed them. And if that identity system is a mash of cookies, CRM IDs, hashed emails, device IDs, and guesses — then your “truth” is basically a polite fiction with confidence issues.

Welcome to the world where identity stitching is the star player in modern reporting, not just an afterthought.

Is it just me or do dashboards feel… slow? Most teams can pull data from Adobe Analytics, Customer Journey Analytics, or a BI dashboard without breaking a sweat. The real problem is what happens after you see the numbers: validating, interpreting, coordinating, deciding, acting. By the time the insight reaches the right person, the opportunity has often passed.

That’s where agentic workflows come in.

These aren’t magic dashboards or futuristic “AI assistants.” They’re systems that watch your data continuously, reason across metrics and behaviors, and respond when outcomes matter. Instead of waiting for you to notice a spike, dip, or anomaly, they flag it, explain why it matters, and even suggest — or execute — the next steps. Analytics stops sitting on the sidelines and starts participating.

Less busywork. More timely action. And finally, insights that actually influence outcomes.

Hot take (because when have I ever not got one?):
I’m willing to bet most of you reading this have some complicated way of connecting your data and trying to keep up with the management of it is a burden. And, you probably haven’t used MCP yet to try to solve it.

Not the sexy, buzzword-friendly “we need AI to save us!” problem. The you can’t stitch revenue and journey data without breaking a sweat problem. The why-is-the-pipeline-question-still-a-slog problem.

Enter MCP connections between data sources — quietly evolving into the unsung hero of modern analytics stacks. If it sounds like just another acronym du jour, stay with me. This one actually matters. And yes, I’m going to explain it like your fellow MarTech pro who knows you’re tired of jargon.

Hot take…many marketing, advertising, and revenue teams are getting ahead of the data problem, but they still have an insight problem. Despite powerful platforms and endless dashboards, getting from signal to understanding still takes too long. From my perspective working at Adobe and with many high-tech customers, generative AI is finally changing that. By enabling teams to ask natural-language questions, auto-generate insight narratives, and connect web, media, and revenue data more intuitively, GenAI is shifting analytics from reporting to reasoning. The real impact is helping humans understand their data faster, communicate insights more clearly, and make better decisions across the entire customer journey.