Strategy on How Marketing Technology Works®

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.

The Outside-In CX Playbook: How to Create Remarkable Experiences Without Buying Another Platform

Thu, 14 May 2026 12:00:00 +0000

The Question Personalization Left on the Table

The case against personalization-as-strategy is well-documented at this point . Billions spent. Marginal returns. A growing body of evidence that the biggest investments in marketing technology aren’t producing proportional customer outcomes.

Tearing something down creates an obligation: what works instead?

The answer isn’t another framework dressed up as a product pitch. It’s a set of disciplines that three CX practitioners have been refining in different lanes, all pointing at the same conclusion. Remarkable customer experience is designed from the outside in. And the hardest parts of it can’t be purchased.

Why Personalization Fails: The Consumer Promise Was Always Wrong and Your Stack Made It Worse

Thu, 14 May 2026 12:00:00 +0000

The Consumer Promise That Never Landed

The pitch has been remarkably consistent for two decades. Personalize the experience, and customers will pay more, stay longer, and love you forever. Every vendor keynote, every analyst report, every martech conference panel has carried some version of this message. And every year, marketers pour more budget into the tools that promise to deliver it.

The problem isn’t that personalization technology doesn’t exist. It does. The problem is that the consumer half of the equation was never what the industry claimed it was.

Your Martech Stack Is Becoming Someone Else's Architecture

Thu, 14 May 2026 12:00:00 +0000

In his May 14, 2026, newsletter article , Scott Brinker (Independent Analyst & Advisor, chiefmartec.com) is right about the direction. Three to five years from now, “the martech stack” as a marketing-owned system probably won’t exist in its current form. Marketing will be one tenant on a company-wide data and AI architecture. But here’s what most of the commentary is skipping: the gap between that vision and where marketing teams sit today. And the work to close it doesn’t start with architecture. It starts with a question most CMOs can’t answer yet.

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%.

Feature Utilization Is Two Problems, Not One

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

The Myth: Utilization Is a Diagnosis

“Our utilization is low” has become the universal martech complaint. It shows up in vendor QBRs, in consulting assessments, in executive reviews of marketing technology ROI. And the response is almost always the same: training programs, adoption campaigns, change management workshops, sometimes even replacing team members in hopes that new hires will bring the right skillset. All designed to push the number up.

Here’s what that response gets wrong: “low utilization” describes 2 completely different conditions sharing the same label.

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.

Your Vendor Says You'll Need It Later. Here's How to Know.

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

The Objection Nobody Challenges

Your DXP contract is up for renewal. Personalization was a key selling point when you signed: behavioral targeting, audience segmentation, content variants served by visitor profile. Fourteen months in, nobody’s configured a single personalization rule. No audience segments built. No content variants live. It’s $35K on the annual license. The room is ready to cut it. Then a director says: “We’ll need that when we launch the redesign next year.”

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.

Free-Year Migration Offers: The Math Vendors Skip

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

The pitch sounds great. A vendor offers to cover your first year of licensing if you sign a multi-year contract. Procurement loves the headline. Your CFO loves the headline. The press release writes itself.

The current live example is Optimizely’s SiteSwitch program, which covers Year 1 if you sign a 3-year CMS contract before June 30, 2026 (3. Optimizely, 2026). The pattern repeats whenever a vendor needs to clear pipeline against a competitor’s installed base. The deadline is calendrical. The structure is universal.

How to Evaluate an AI Vendor in 30 Minutes

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

The Measurement Contract Your Analytics Team Never Signed

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

Every marketing analytics team I’ve worked with built the dashboard before anyone agreed on what would change when the numbers arrived. Attribution models, media mix models, incrementality tests, real-time data flowing from every touchpoint. The analysis was sound every time. The tools worked. Leadership nodded at the slides in quarterly reviews and didn’t change a single budget line.

After a decade of watching that pattern hold across industries and stack sizes, the conviction is plain: measurement fails because leadership never committed to acting on what data shows before they know what the data will say. That commitment gap is where insight dies. The contract was never signed.

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.

Context Rot: Why AI-Powered Martech Degrades When Nobody's Watching

Sun, 03 May 2026 12:00:00 +0000

Your AI agent doesn’t know the migration ran at noon. It’s still operating on this morning’s data. The recommendation it produces in five minutes will be technically fluent and functionally wrong. Nothing will flag the error. The output will arrive on schedule, formatted correctly, referencing data that expired hours ago. AI systems that learn from their outputs compound this problem when the feedback loop itself is built on stale inputs.

That’s context rot: a slow, silent divergence between what your systems believe is true and what’s true in your organization right now. It persists because the context layer has no owner and no maintenance schedule (1. Mourão, 2026). “A brilliant model with bad data access makes confident mistakes” (2. Salesforce, 2026). The quality of the model isn’t what determines output quality. Whether anyone refreshed the context the model consumed is.

How Organizational Efficiency Kills Strategic Reinvention

Sun, 03 May 2026 12:00:00 +0000

PwC’s 29th Global CEO Survey puts a number on the tension: only 30% of CEOs express confidence about revenue growth over the next 12 months (1. PwC, 2026). These are CEOs. Revenue growth is the primary metric they’re paid to deliver.

The reflexive diagnosis is leadership failure. That’s too simple. Maybe they’re being honest about chaotic conditions: geopolitical fragmentation, rapid AI shifts, tariff volatility. Maybe admitting uncertainty is more responsible than projecting false certainty.

The Transaction Trap: When Your Marketing Technology Stack Fights Your Customer Relationships

Sun, 03 May 2026 12:00:00 +0000

Your most loyal customer just opened a support ticket. They’ve been with you for 5 years, expanded their account every renewal cycle, and advocate for you at industry events. Your marketing automation doesn’t know any of that. It fired a “Why haven’t you tried our premium features?” email because the campaign rules triggered on a behavior score, not a relationship.

That disconnect reveals which of two operating models your stack is built for. Both models exist for reasons. The question is whether you chose yours deliberately or inherited it from the defaults your platforms shipped with.

Why Marketing-IT Alignment Keeps Failing

Sun, 03 May 2026 12:00:00 +0000

The Dependency Paradox

The marketing-IT relationship runs on a paradox. Marketing owns revenue, customer experience, campaign velocity. IT owns the systems that deliver all three. Marketing can’t execute without IT’s involvement. IT bears no accountability for marketing’s results.

Ronald Gaines, a martech and data analytics leader who has built marketing operations at Cisco, Dell, and Sunbelt Rentals, called it what it is: “Marketing Ops is accountable for outcomes it doesn’t control. You touch everything. You own nothing” (1. Gaines, 2026).

How the 80/20 Rule Shapes Martech Implementation Success

Sat, 02 May 2026 12:00:00 +0000

The Pareto Principle shows up everywhere in business: 80% of outcomes tend to come from 20% of inputs. In martech, that ratio plays out in a way most organizations don’t recognize until they’re stuck with the consequences.

Look at your stack. The part that handles email delivery, form capture, lead scoring, basic segmentation, CRM sync, and reporting dashboards? That’s the commodity 80%. Every competitor in your space has those same capabilities. They’re table stakes. They qualify you to play.

The Single View of the Customer Farce

Sat, 02 May 2026 12:00:00 +0000

The Promise Under Examination

One complete customer record. Every interaction tracked. Perfect visibility across channels. Sales, marketing, support, and finance all working from the same truth. The single view of the customer has been the centerpiece of CDP marketing for a decade.

The belief rests on several pillars: that identity can be resolved with certainty, that systems can stay synchronized, that teams can agree on a single data model, and that the investment pays for itself. Each pillar collapses under scrutiny.

Capability Optimization: Why Your Martech Stack Underperforms

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

I have watched organizations spend millions on martech training that changed nothing. Certification programs, vendor boot camps, analytics workshops. The teams learned. Then they went back to the same approval chains, the same misaligned reporting structures, the same dashboards that track clicks instead of capability. And the performance gap held.

This is what I believe: your martech stack underperforms because the organization around it was never rebuilt to match what the platform can do. The features exist. The institutional muscle to activate them doesn’t. Training alone won’t close that gap. Neither will replacing the platform. The gap travels with you because it lives in the organization, not the technology. A diagnostic run before the platform decision is how you distinguish organizational constraints from genuine platform limitations.

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?

Why Your Martech Stack Is a Frozen Org Chart (And Why AI Just Made That Visible)

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

The Architecture Nobody Designed

Forty-eight hours ago, Salesforce launched Agentforce Operations. The promise: AI agents that automate back-office processes across systems. The requirement nobody mentioned in the press release: those systems need to share context, decision authority, and outcome definitions that most enterprises have never established.

That requirement exposes how every martech stack actually got built. Not from a blueprint, but from reporting lines. Marketing ops bought marketing tools. Sales ops bought sales tools. Customer success bought its own engagement platform. Each team governed its own piece. Scott Brinker identified three ways to organize a martech stack: around the customer journey, around technology categories, or around internal organizational structure (1. Brinker via martech.org, 2025). The third option is what most enterprises actually have. And the least intentional.

Your Martech Tools Talk to Each Other. They Don't Agree on Anything.

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

Three AI agents. One customer. One week.

Your marketing agent sends a premium positioning email Monday. Your sales agent follows with a discount offer Wednesday. Your support agent fires a win-back sequence Friday because the account went quiet.

All three had the same customer data. All three were optimizing for their own goals. The customer, a $200,000 renewal, forwarded all three emails to your VP of Sales: “Can someone tell me what’s actually going on over there?” (1. Martinez, 2026)

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.

The Martech Assessment That Comes Before the Platform Decision

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

Marketing automation replacement dropped from 31% to 19% in a single year. CRM replacement fell from 22% to 10% (1. 2025 MarTech Replacement Survey). After years of routine rip-and-replace cycles, teams stopped swapping platforms.

But they didn’t start doing the work that makes the keeping decision productive. They stopped replacing. They didn’t start diagnosing. What happened instead was a holding pattern: teams kept platforms they were frustrated with, kept the same complaints, and waited for the next vendor release or AI feature to solve what was broken.

Why CEOs Call Marketing a Cost Center (and What CMOs Should Do About It)

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

Record trust. Record alignment. Record loyalty. And a 25-point collapse in marketing’s standing as a value creator, all in the same year. That’s the contradiction sitting inside the Boathouse 2026 CEO Study (1. The Boathouse Group, 2026), and it stops looking contradictory once you read it as organizational design producing its intended output.

The mechanism is straightforward, and the three prescriptions that follow depend on understanding it first. When 57% of CEOs frame their CMO as an execution leader rather than a strategic advisor, they’ve already decided what kind of marketing organization they want (1. The Boathouse Group, 2026). An execution machine. That framing shapes every downstream conversation: CEO-CMO discussions shift toward metrics and performance, teams get described as methodical and analytical rather than creative, and the function gets optimized for speed and predictability over strategic contribution.

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.

Stop Blaming the Vendor: Why Martech Buyers Own Their Implementation Failures

Tue, 28 Apr 2026 09:48:00 -0400

Here is the position, stated plainly: most martech implementation failures belong to the buyer.

Vendors oversell capabilities. Integration partners underestimate complexity. Sales teams fabricate timelines to win deals. All true. None of it is news, and none of it is going to change. What can change is what happens on the buyer’s side of the table. And after watching this pattern repeat across industries for three decades, I’m convinced that the vendor-blame reflex is the single biggest obstacle to getting different results. Not because vendors are blameless. Because blame is where accountability goes to die.

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 .

Should You Trust a Martech Sales Rep Who Changed Vendors?

Mon, 27 Apr 2026 12:00:00 +0000

We’ve all seen it. A sales rep spends two years telling you their platform is the best thing to happen to marketing since email. Then they show up at a new vendor and, with equal conviction, explain why this platform is the answer. Same passion. Same certainty. Different logo on the business card.

Nobody’s lying here. Most of these people are talented professionals making rational career moves in a volatile industry. Average sales rep tenure has dropped from four to five years in the early 2000s to a projected 18-30 months in 2026 (1. JustSalesJobs, 2026). That’s the modern SaaS sales career. The churn is structural.

Outcome-Focused Martech: A Smarter Way to Implement

Wed, 15 Apr 2026 11:59:00 -0400

Every martech implementation carries an implicit mindset. That mindset determines what you optimize for, how you measure success, and where the stack eventually breaks. Three mindsets dominate. They look similar at the start. They produce very different results.

What each mindset optimizes for

Vendor-focused implementation puts the vendor at the center of every decision. Integration partners follow vendor playbooks, chase certifications, and deliver patterns that serve the vendor’s ecosystem. The priority is ecosystem alignment. Tools get selected because they integrate with the vendor’s other products, not because they solve a specific business problem. Partner incentives reinforce the pattern: revenue shares, co-sell programs, and certification tiers all reward ecosystem adoption.

Martech Vendor Selection: How to Spot Vendor Agility

Wed, 08 Apr 2026 12:48:00 -0400

31% of martech implementations fail outright or deliver neutral results (1. GNW Consulting & Demand Metric, 2025). That number surprises nobody who has lived through one. What surprises them is how visible the warning signs were during evaluation.

Vendor agility during selection is the most undervalued signal. Feature lists get scrutinized. Pricing gets negotiated. How the vendor responds when your reality breaks their playbook? That barely gets tested.

Push beyond standard demo scripts

Standard demo requests produce polished presentations. Clean data, perfect workflows, flawless execution. You leave impressed. Then implementation starts.

Martech Value: Find Hidden ROI in Your Marketing Technology

Mon, 06 Apr 2026 15:06:00 -0400

The CMO Survey Spring 2026 found that nearly 55% of marketing leaders report disappointment in martech payoff compared to expectations (1. CMO Survey, 2026). Those numbers reflect measurement failure, not technology failure. Organizations measure feature activation and call it performance. Attribution models try to connect campaigns to revenue but miss the efficiency gains, experience improvements, and strategic capabilities that compound daily without appearing in any dashboard.

The IAB’s State of Data 2026 found that 75% of marketers say their core measurement approaches underperform on rigor, timeliness, and trust (3. IAB, 2026). The technology performs. The investigation approach is broken. Here’s where to look and what to document in each area.

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.

Martech Stack Optimization: The Martech Flexibility Rule

Thu, 02 Apr 2026 12:08:00 -0400

Martech stack optimization works when you treat your platforms as capabilities that improve through continuous evaluation, not fixed assets you defend until renewal.

The approach that saved me from more disasters than I can count: “This is what I believe today, but I reserve the right to change my mind tomorrow.”

Early in my career, I defended decisions long past the point where they stopped working. Rigidity felt like conviction. It wasn’t. The willingness to pivot, to admit my first approach might not be right, transformed both how I work and the outcomes I deliver.

Stop Configuring Your Martech Stack for Buyers Who No Longer Exist

Thu, 16 Oct 2025 12:00:00 +0000

The outside-in shift

Most martech is configured for internal convenience: what’s easy to measure, simple to automate, clean to report. The result is stacks built for your workflows, not buyer experience.

Outside-in configuration starts with how buyers behave. What signals evaluation versus browsing? What information do they need when comparing alternatives? How do buying committees build consensus across different priorities?

Map those patterns first. Configure tools to support them second. Six areas where typical configurations miss buyer reality: scoring, nurture, content, segmentation, attribution, and integration.

Digital Maturity Models: A Mirage in the Desert of Transformation

Fri, 05 Apr 2024 14:25:00 -0400

Digital maturity models promise structured assessment of transformation readiness. Clean levels, tidy progressions, a sense of control. The belief rests on three pillars: that rigid scoring captures organizational reality, that the frameworks are objective, and that technology maturity predicts transformation success. Each pillar falls apart under examination.

The structured assessment collapses on contact

DMMs force-fit dynamic organizations into one-size-fits-all molds that prioritize scoring well over achieving results.

Consider a SaaS company prioritizing speed-to-market in launching new products. They’ve delayed back-end integrations to stay agile and capture market share. A DMM penalizes them for this “deficiency,” ignoring their competitive success and pressuring them to divert resources toward capabilities they won’t need for years.