The Intent Economy: Why AI Intermediaries Are Disintermediating Your Brand — and What to Do About It

See also (wiki): assistive-to-agentic-shift, agentic-ai-governance, data-readiness

Executive Summary

• IBM IBV and Adobe surveyed 1,000 senior marketing, digital, data, and technology executives across 13 industries and 14 countries (April 2026). Only 34% of the customer data organizations collect is actually used for customer experience decisions. The remaining 66% sits idle in silos.
• AI-assisted shopping is redrawing the customer discovery funnel. Consumer use of AI applications (ChatGPT, Gemini, chatbots) to aid purchase decisions surged 62% in two years — and faster for Gen X (82%) and Boomers (92%). 41% of consumers use AI assistants to research products, 33% to look for reviews, 31% to hunt for deals (IBM IBV + NRF, n=18,000 consumers, January 2026).
• The performance gap is in execution speed, not technology. Organizations with slower detection-to-action times than industry peers see return on marketing investment drop 30–40 percentage points, and the average operating waste from excessive delay is $29M annually.
• The high-performing cohort — organizations that pair AI-fueled responsiveness with embedded governance — reports 12% higher marketing ROI, 38% higher customer lifetime value, 7% lower customer acquisition cost, and 1% higher conversion rate vs. peers.
• Apply vendor caveat: IBM Consulting and Adobe are co-publishers with direct commercial interest in customer experience transformation programs and agentic orchestration platforms. The survey methodology is credible; the prescriptive framing points toward services and platforms both firms sell.

What “Customer Intent” Actually Means — and Why It’s New

The study’s central claim: customer intent — the specific outcome a person is trying to accomplish in a given moment — has become the currency of competition. It is measurable, perishable, and increasingly captured by intermediaries brands do not control.

The shift is structural. Until recently, customers expressed intent by browsing a site, searching a catalog, or calling support. Every click, query, and conversation generated a signal the brand could see. When that same customer now asks ChatGPT, Gemini, or Perplexity to research and recommend products, the brand loses the signal entirely. OpenAI’s instant checkout for Etsy and Shopify (and PayPal integration in 2026) and Perplexity’s “Shop Like a Pro” mean the first — and sometimes only — touchpoint in a purchase journey happens inside an AI application the brand has no visibility into.

Executives surveyed expect the share of customer signals coming from AI-enabled channels to nearly double, from 35% today to 63% by 2028. 61% of marketing content is projected to be AI-generated by the same date.

The 34% Data Utilization Problem

The most citable single statistic in the report: only 34% of customer data collected today is used for customer experience decisions. Profile, demographics, channel preferences, purchase history, website engagement, mobile app usage, email interactions, social activity, service interactions — most of it enters a system and never informs an offer, intervention, or experience.

75% of executives say most organizations are too slow to respond to changing customer expectations. The root cause is not data volume. It is data utilization.

The study tracks what it calls the detection-to-action window — the time between capturing an intent signal and acting on it. The gap between industry leaders (top 20%) and average performers is large:

The honest reading: a media company that takes 92 hours to respond to an intent signal is competing with one that takes 4. In a world where a consumer can finish a purchase journey inside ChatGPT in 30 seconds, multi-day organizational response loops are a structural disadvantage, not a pacing issue.

The Personalization Coordination Tax

The study maps respondents across five personalization maturity stages — foundational, behavioral, predictive, real-time adaptive, and AI-orchestrated — and measures average annual revenue growth and operating margin over the last three years:

The non-linear pattern is the finding. Companies moving from foundational to behavioral personalization gain. Companies pushing into predictive and real-time adaptive often hit a plateau — what the authors call a coordination tax from multiple handoffs, systems, and decision triggers that must reconcile in real time. Only organizations that cross into AI-orchestrated (agentic) personalization recapture growth.

The implication for a mid-market CMO: adding another personalization tool to a stack that already cannot reconcile identity across channels does not produce more personalization. It produces more coordination cost. The gains show up only when the orchestration layer catches up to the data and decisioning layers.

The $29 Million Number — and How It Was Calculated

The headline cost figure: organizations with detection-to-action delay that exceeds industry norms lose an average of $29 million annually in operating waste. The methodology note is important: the figure is an estimate calculated by benchmarking industry-specific detection-to-action times, measuring how much longer an organization takes to respond, and translating that delay into cost using the organization’s marketing and content spend. Outliers were excluded.

This is a modeled estimate, not a directly measured P&L impact. It is defensible as a planning figure for organizations with $500M+ revenue and significant marketing spend. For a $50M–$200M mid-market company, the absolute dollar figure will be materially smaller, but the directional logic — delay compounds into wasted marketing investment — holds.

The study also reports that organizations that successfully decode intent see:

• 13% lower customer acquisition costs
• 6% higher retention rates
• 4-point advantage in customer satisfaction scores
• 3-point edge in NPS

The Speed + Governance Cohort

The most commercially relevant finding is the characterization of the high-performing cohort. Organizations that pair AI-fueled responsiveness with embedded governance report measurably better outcomes than peers:

71% of executives admit they struggle to balance intelligent personalization with trust and privacy. 75% say connecting front and back offices to enable end-to-end journeys will be more important in the next year. The cohort that resolves this tension has three things the others do not: timely decoding of intent, anticipatory engines, and governance that enables action instead of blocking it.

The top operational barriers to cross-functional collaboration: poor communication (48%), conflicting timelines (47%), unclear roles (46%). 77% cite resistance to change as a top customer experience challenge.

How This Compares to the Rest of the Corpus

The 38% CLV lift and 12% marketing ROI lift for the top cohort align directionally with the structural pattern the corpus documents repeatedly: a small share of organizations (5–6% in BCG and McKinsey framings) capture disproportionate value by pairing deployment with governance, workflow redesign, and cross-functional alignment. The specific CX-focused metrics are new.

The 34% data utilization figure triangulates with the broader “data readiness is the constraint” finding from Atlan, McKinsey State of AI (Nov 2025), and MIT CISR — organizations collect far more data than they use, and the unused portion is what AI needs to produce value.

The projected 35% → 63% shift in intent signals moving to AI-mediated channels by 2028 is the single most actionable market-structure data point in the report for B2C executives. If accurate, it means the brand’s traditional direct-to-consumer funnel will have to contend with an increasingly thick intermediary layer — and brands that are not discoverable, structured, and credible inside AI ecosystems by then will have limited defense.

Credibility Rating

MEDIUM. The survey is methodologically serious — 1,000 senior executives, 13 industries, 14 countries, ordinal factor modeling, causal estimation techniques with placebo testing, clear disclosure of methodology on page 26. The authors include academic-quality research leaders at IBM IBV. The detection-to-action causal analysis is more rigorous than the typical consulting survey.

Apply vendor caveat: IBM Consulting and Adobe co-publish this report with direct commercial interest in agentic orchestration programs, customer experience transformation engagements, and Adobe Experience Cloud deployments. The prescriptive framing — cross-functional orchestration teams, composable technology stacks, agentic AI platforms — maps onto services both firms sell. The 21,000-practitioner IBM-Adobe partnership is disclosed on page 2.

Cross-reference the 38% CLV / 12% ROI figures against independent data before making investment decisions. The underlying directional finding — speed + governance separates the top cohort — is consistent with BCG, McKinsey, Deloitte, and MIT CISR findings; the specific magnitudes are IBM/Adobe’s and have no independent verification.

Key Data Points

What This Means for Your Organization

If you lead marketing, digital, or customer operations at a B2C or hybrid company, the 35% → 63% shift in intent signals moving through AI intermediaries is the figure to act on. It says the runway to make your brand discoverable and credible inside AI-driven discovery is short — roughly 24 months — and the companies that capture the next generation of customer relationships will be the ones whose product information is structured for AI agents to reason over, whose identity resolution works across channels in real time, and whose governance is embedded rather than bolted on.

The practical starting point is not buying an agentic platform. It is measuring your own detection-to-action window on three representative customer journeys — an acquisition signal, a service signal, a retention signal — and comparing it to the industry benchmarks in this report. If your response time is multiple times slower than the top 20%, the constraint is organizational, not technological. Orchestration platforms cannot fix data that sits in silos or decisions that require three committees. The speed + governance cohort in the study is distinctive not because it spent more on technology but because marketing and IT share ownership of customer technology and ROI metrics, and because governance is designed as enablement rather than a gate.

For mid-market CMOs and CIOs (200–2,000 employees), the $29M waste figure is not directly applicable — your absolute dollar exposure is smaller. The directional logic is. The companies that spend six months negotiating ownership of the customer data platform while competitors ship are the ones that discover, a year from now, that AI intermediaries have already re-routed a meaningful share of their discovery funnel. If this report raised questions specific to your organization’s intent architecture — who owns the detection-to-action window, whether your data is actually usable, where governance is slowing you down — I’d welcome the conversation at brandon@brandonsneider.com.

Sources

1. Chan, Jana; Charchaflian, Pierre; Gonzalez, Saralyssa; Kohli, Nisha; Titherley, Dylan; Trestain, Jay; Young, Christopher. “Win the moment: Reclaiming relevance by mastering customer intent.” IBM Institute for Business Value in partnership with Adobe. April 15, 2026. Global survey of 1,000 senior executives; 13 industries; 14 countries. URL: https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/customer-intent. PDF: https://www.ibm.com/downloads/documents/us-en/15db45f02cd20dad. Credibility: MEDIUM (vendor co-publisher caveat applies).

2. Waddell, Dee et al. “Own the agentic commerce experience: Consumers are ready.” IBM Institute for Business Value in partnership with NRF. January 2026. n=18,000 consumers. URL: https://ibm.biz/agentic-commerce. Source of the 62% / 82% / 92% AI application usage surge and 41% / 33% / 31% AI-assisted shopping stats.

3. OpenAI. “Buy it in ChatGPT” (Etsy and Shopify instant checkout). 2025–2026. URL: https://openai.com/index/buy-it-in-chatgpt/.

4. PayPal. “OpenAI and PayPal Team Up to Power Instant Checkout and Agentic Commerce in ChatGPT.” October 28, 2025. URL: https://newsroom.paypal-corp.com/2025-10-28-OpenAI-and-PayPal-Team-Up-to-Power-Instant-Checkout-and-Agentic-Commerce-in-ChatGPT.

5. Perplexity AI. “Shop Like a Pro” documentation. URL: https://www.perplexity.ai/help-center/en/articles/10352906-what-is-shop-like-a-pro.

Brandon Sneider | brandon@brandonsneider.com April 2026