Artificial Intelligence on How Marketing Technology Works®

Everyone's Got to Get Paid: Why AI Pricing Isn't the Threat You Think It Is

Wed, 27 May 2026 12:00:00 +0000

Everyone’s Got to Get Paid

A recent piece in The Verge frames AI’s pricing trajectory as ominous: investors poured hundreds of billions into AI infrastructure, the free tier is disappearing, and users are about to feel the squeeze (1. Field, 2026). The tone suggests something unprecedented is unfolding, that rising AI prices represent a betrayal of the early promise.

Rising prices are the most predictable move in business.

When was the last time you walked into a grocery store and got your groceries for free? Someone built the store. Someone stocked the shelves. Someone kept the lights on. Everyone’s got to get paid. AI is no different.

Why New York Life's CMO Built a Data Foundation Before Deploying AI

Tue, 19 May 2026 12:00:00 +0000

The Failure Rate Nobody Talks About at AI Conferences

Most organizations approach AI as a procurement decision. Find the right platform, negotiate the contract, train the team, expect results. RAND Corporation research tells a different story: more than 80% of AI projects fail to deliver expected value, with data quality and organizational readiness as the consistent root causes (1. RAND Corporation, 2025). The technology works. The organizations deploying it usually haven’t built the foundation to make it work for them.

Your Company Mandated AI. Nobody's Coming to Help.

Tue, 12 May 2026 12:00:00 +0000

Here’s what I believe: if your company mandated AI and didn’t give you training, guidance, or anyone to ask when you’re stuck, leadership has told you something important. They moved faster on the mandate than they could on the enablement. The support isn’t coming on anyone’s timeline but yours.

That sounds cynical. It’s the most liberating thing I can tell you. Once you stop waiting for enablement to catch up to the mandate, you can start getting better at the job you’re paid to do, and building skills that compound.

AI ROI Is an Operating Model Problem

Mon, 11 May 2026 11:12:00 -0400

The Belief That Won’t Die

Every conversation about AI underperformance follows the same script. The technology isn’t advanced enough. Teams don’t have the right skills. Vendors oversold the platform. These three explanations absorb nearly all the budget, attention, and executive frustration aimed at closing the AI ROI gap.

They also account for roughly a third of the actual problem.

What 20,000 AI Users Revealed

Microsoft’s 2026 Work Trend Index surveyed 20,000 AI users across 10 countries and measured which factors are associated with AI performance outcomes (1. Microsoft, 2026). Organizational factors, including how decisions get made, how workflows are designed, and how goals get communicated, account for approximately 67% of the variance. Individual factors, including skill, training, and personal adoption habits, account for 32%.

The Agentic Marketing Organization Won't Work Without a Capability Plan

Mon, 11 May 2026 12:00:00 +0000

Every technology era produces a new operating model for marketing. DXP promised unified digital experiences. Most organizations discovered the platform outpaced their ability to govern content, manage permissions, and coordinate across the teams required to operate it. CDP promised unified customer data. Same outcome: the architecture worked, but the governance, data ownership, and cross-functional coordination never materialized on schedule.

A recent HBR article proposes the agentic marketing organization: a four-layer system where a “brand code” feeds AI agents that handle content, testing, distribution, and reporting across five coordinated workstreams (1. Taite, Winsor, & Fernandez, 2026). The framework is architecturally sound. The problem it addresses is real. And it solves for architecture when the problem is capability.

Context Engineering, Decoded: Why CMOs Need to Know Which Definition Their Team Means

Thu, 07 May 2026 12:00:00 +0000

One Term, Two Definitions

Context engineering is showing up more frequently than “Ambient Realism” photography. You’ll find it in vendor pitches, board decks, and the State of Martech 2026 report. The term means two different things, and most CMOs hearing it can’t tell which one their team is being sold. One version is real engineering work that lives with technical teams. The other repackages governance and brand work the marketing function has done for decades, with a new label and a new pitch around it.

How to Evaluate an AI Vendor in 30 Minutes

Mon, 04 May 2026 15:46:00 -0400

The Stability Revolt: Why Senior Martech Leaders Are Choosing Foundations Over Agentic DXP Theater

Mon, 04 May 2026 12:00:00 +0000

The Award-Winning Contradiction

The DXP and headless CMS market spent the last six months shipping agentic AI features that redistribute operational complexity rather than eliminating it. Vendors celebrate the capability. Practitioners absorb the overhead. The pattern is complexity redistribution: AI automation shifts governance, monitoring, and coordination burdens onto buyer teams without reducing total workload. It runs through every major vendor category. And the most telling evidence comes from the vendors themselves.

"I mandated AI at my company. It almost backfired."

Sun, 03 May 2026 17:18:00 -0400

Why Agentic AI Projects Fail: The Decision Architecture Gap

Fri, 01 May 2026 12:00:00 +0000

Adobe’s CX Enterprise Coworker launched at Summit 2026 with a clear pitch: give it a business objective, and it assembles agents across your CDP, journey analytics, and content optimizer to build and execute the plan (1. Adobe, 2026). Salesforce introduced Agentforce Operations the same month, automating back-office workflows with over 30 blueprints for everything from invoice auditing to onboarding. The technology vendors have delivered on agentic AI. The question nobody’s answering: has anyone on the receiving end built the infrastructure to run it?

AI Agent Governance: Why Centralized Approval Backfires

Thu, 30 Apr 2026 12:00:00 +0000

As AI agents multiply across marketing teams, leaders face a genuine design choice. Not whether to govern agents, but how. Two models dominate the conversation, and most organizations pick one without evaluating what each produces in practice.

When the official approval process takes a week and building an agent independently takes an afternoon, teams choose speed. A marketing ops manager who needs a campaign performance agent running before next week’s leadership meeting isn’t going to wait for a governance committee to meet. They’ll build it with their own API credentials, connect it to the data sources they have access to, and have it running by Thursday. That dynamic shapes everything that follows, regardless of which governance model you choose.

Your Vendor Calls It Agentic. Your Operating Model Doesn't Care.

Wed, 29 Apr 2026 12:00:00 +0000

Every major martech vendor now sells something labeled “agentic AI.” Eighty-eight percent of organizations are experimenting with it. But 81% of those organizations report no meaningful bottom-line gains (1. McKinsey, 2026).

That number should stop the conversation. It doesn’t. Boards want an “AI strategy.” Vendors pitch autonomy. CMOs worry about falling behind. And on the other side of the table, operations leaders point to broken data foundations and absent governance and ask who’s going to manage these things once they’re running.

Operationalize Marketing, Not AI

Tue, 28 Apr 2026 12:00:00 +0000

The marketing industry is treating AI as the thing that needs to be deployed, integrated, governed, and scaled. It’s not. AI is a diagnostic instrument, and what it diagnosed is uncomfortable: marketing operations were never fully operationalized in the first place.

Six independent marketing communities, surveyed separately in 2026, converge on the same structural failures: bad data, unclear positioning, misaligned stakeholders, fragmented stacks, broken executive alignment (1. De Libero, 2026). None of these problems are new. None are caused by AI. They’re the organizational debt that accumulates when marketing teams buy platforms instead of building the operational capability to run them .

Enterprise AI That Learns: Marketing Leader Success Guide

Fri, 03 Apr 2026 16:03:00 -0400

Enterprise AI investments fail at a 95% rate, according to MIT’s NANDA initiative, despite $30-40 billion in aggregate spending (1. MIT NANDA, 2025). And context rot ensures that even the systems that survive launch degrade silently when nobody audits the data feeding them. The tools aren’t the problem. The sequence is. Organizations deploy AI as a product launch, skip the foundational work, and then wonder why performance flatlines after the first quarter.