This week's research from Gartner and Forrester keeps circling the same (potentially uncomfortable) point for CMOs. AI is pulling apart tools marketers have leaned on for decades, and it's quietly raising the bar on the work that's left: brand, customer experience, and commerce all now demand more rigor and a more human touch.

A split runs through this week's research, and it's the uncomfortable kind for CMOs. Companies are pointing AI in two opposite directions. One group uses it to defend what they already do: trimming costs, automating the busywork. The other points it at growth: new revenue channels, a customer experience built to win people the old funnel never reached.

This article was written by Greg Kihlström for CMSWire. CRMC 2026 surfaced three recurring failures in retail loyalty strategy — brands that listen superficially, friction that persists because CX teams are excluded from policy decisions and disruptive ideas that die inside siloed organizations. AI tools are accelerating all three problems for brands that have not addressed the fundamentals first.

The dominant story in recent enterprise marketing research is a widening execution gap in agentic AI. Investment keeps climbing. Scaling doesn't. Across the new reports, the same picture keeps surfacing — most organizations are spending on AI without operationalizing it.

This article was written by Greg Kihlström for CMSWire. Most brands treat travel data as somebody else's category. Unless you sell flights or hotel rooms, it lives on a different brand’s dashboards, or in a different agency's deck.

Recent research keeps circling the same problem: companies want AI to do more than they've built the infrastructure to support, and the gap is starting to show up on the scoreboard. Three findings stand out.

Salesforce agreed to buy Contentful on June 1. Sitecore bought Scrunch on June 3. Inside 48 hours, two of the larger experience-platform vendors paid to move AI agents to the center of how content gets made and found. Salesforce is taking Contentful, the Berlin-founded headless content platform that serves more than 4,800 brands. Sitecore took Scrunch, an AI-search visibility platform, in a deal Bloomberg reported at about $225 million.

Recent research and press releases tell a story vendor marketing won't. The gap between AI investment and AI readiness keeps widening. CMOs are caught between two pressures at once: deploy AI at scale, while their own customers turn to AI to screen out AI-generated marketing. Better messaging won't close that gap. It's structural, and it changes how a marketing organization has to work.

AI made marketing faster at producing almost everything, and the speed has surfaced a quieter problem underneath it. The output now arrives faster than teams can review it, the metrics that used to prove a campaign worked are losing their grip as buyers move to AI, and the customer on the other end has grown harder to convince. Four reports this week land on that problem from different angles. They point to where the real work sits over the next year: building the capacity to review what AI produces, prove that it performed, and make it credible enough that people act on it.

A growing share of product research, comparison, and purchasing now happens inside AI agents — ChatGPT, Perplexity, Claude, and the assistants embedded in retailer apps — that assemble a shortlist before a human reviews anything. Adobe Analytics recorded generative-AI-referred traffic to US retail sites growing roughly 690% year over year across the 2025 holiday season (Adobe Analytics, 2026). Most brands have no diagnostic for whether their products clear the bar to appear in that shortlist, and no measurement infrastructure that would catch it when they stop. A brand can be filtered out of consideration with no traffic decline, no campaign failure, nothing obvious to investigate.

This article was written for MarTech by Greg Kihlström. In agentic commerce, brand shifts from a perception advantage to a verifiable advantage. Customers may still choose brands based on emotion or identity, but their AI agents will evaluate those brands using measurable signals such as price transparency, fulfillment reliability, reviews, loyalty value, privacy practices, and service history.

Three years into the enterprise AI cycle, productivity gains are no longer enough to satisfy boards. The gap separating the firms building durable advantage from the firms still chasing tool adoption keeps widening, and the McKinsey, Forrester, and HBR research from this week makes the structural reasons for that gap unusually clear.

Marketing and sales have been focused on the funnel for decades now, with roles and in some cases, entire teams taking actions to move customers through one stage to the next. Now, with agentic commerce in the rise, the traditional multi-step journey of browsing visual catalogs, reading reviews, comparing prices, and manually checking out is being condensed in some instances into a single interaction. 

Recent research surfaces a single dominant pattern: the AI infrastructure layer and the data layer underneath it are consolidating faster than most marketing organizations are restructuring around them. Gartner's latest forecast puts global AI spending at $2.59 trillion in 2026, a 47% jump year-over-year, with enterprises still trailing vendors and hyperscalers on actual deployment. At the same time, two of the largest martech and CX acquisitions of the cycle closed or were announced inside a single week

A growing share of product research, comparison, and purchasing now runs through an AI agent rather than a person. The agent interprets the request, narrows the field, and returns a small set of purchase-ready options, and the customer chooses from those. This is measurable. Adobe Analytics recorded generative-AI-referred traffic to US retail sites growing roughly 690% year over year across the 2025 holiday season (Adobe Analytics, 2026).

The Gartner Marketing Symposium/Xpo in London (May 11-12) produced the densest week of CMO research so far this year, and the signal across every session converges on one point: AI adoption is now universal, but value capture is not. While 70% of CMOs say becoming an AI leader is a critical goal for 2026, only 30% report mature or fully developed AI readiness capabilities. The gap between intent and operating capability now defines competitive position.

This week's scan across Harvard Business Review, McKinsey, Gartner, Forrester, MarketingProfs, and the AMA Journal of Marketing converges on a structural problem: the tactics marketers use to persuade buyers and the metrics they use to prove value both assume a human on the other side. AI now occupies that position. It selects products, mediates discovery, and absorbs the traffic that legacy attribution depends on. The persuasion playbook and the measurement model degrade at the same time, and the organizations adapting fastest are rebuilding both rather than patching either

This article was written by Greg Kihlström for CMSWire. Since User Experience (UX) has been a discipline, the "user" has been synonymous with a human being for obvious reasons: humans were the consumers of software tools, websites, mobile apps and all the other experiences that fall under this umbrella. Designers optimize interfaces for eyeballs, thumbs and dopamine receptors, crafting interfaces that guide human behavior through visual hierarchy and emotional resonance.

The most consequential insight in this week's research comes wrapped in a single line from Forrester: "Efficiency isn't a relationship strategy." It was written about retail banking — but it could just as easily be the headline for nearly every AI-driven marketing transformation happening right now. Across new research from HBR, Gartner, and Forrester this week, the same pattern keeps surfacing: marketing leaders are deploying AI to make existing workflows cheaper and faster, and in the process are quietly accelerating commoditization, transparency loss, and customer detachment.

The latest insights converge on a single, uncomfortable truth: most marketing organizations are investing in AI at the wrong layer. They're deploying tools on top of broken operating models and dirty data, then wondering why the ROI isn't materializing. The gap between AI ambition and AI readiness has never been wider — and the organizations pulling ahead aren't the ones with the most tools. They're the ones that restructured first.