Bynder

AI Raises Importance of Digital Asset Management

A MarTech analysis of Bynder’s “State of DAM Report 2026” argues that generative AI is exposing limits in rules-based automation and making Digital Asset Management (DAM) platforms central to content governance. Bynder’s report finds 93% of enterprise organizations face content challenges that existing rule-based workflows cannot solve; top issues include detecting off‑brand assets, governing AI‑generated content, producing personalized content at scale, and managing complex workflows. Security, legal/regulatory compliance and hallucinated outputs are marketers’ main AI concerns. The survey shows common human-in-the-loop approaches—roughly 40–44% say automation performs the work while people make final decisions, and 31–35% use mixed automation/manual workflows. The article concludes that well-organized assets, consistent metadata and clear approval processes within DAM are essential as AI becomes embedded in marketing operations.

Consumers Prefer AI Content — Until It's Labeled

Surveys from Validity and Bynder reported in MarTech show a paradox: consumers often prefer AI-written content when they don't know its origin but penalize brands once AI authorship is disclosed. Validity data (500 U.S. marketers; 1,000 U.S. consumers) finds 74% of marketers are deploying or testing AI-generated content and 43% plan to increase investment. Validity also reports 40% of consumers would trust retailer marketing emails less if they knew they were AI-written, while 55% now make inbox decisions based on AI-generated email summaries. A Bynder study of 2,000 U.K./U.S. consumers showed unlabeled AI copy was chosen as more engaging by 56% of those with a preference, but 52% felt less engaged when told the piece was AI-generated. The article outlines email strategy implications including subject-line auditing, attribution changes, and light-touch disclosure policies.

Aligning Complex Customer Journeys Beyond Silos

This MarTech guidance article (published May 7, 2026) explains how martech practitioners can reduce friction and keep multichannel customer journeys aligned by taking a broad operational view across platforms, channels, data flows, QA and cross-team coordination. It outlines common platform types (CRM, CDP, DMP, DSP, SSP, ESP, CMS, analytics, attribution, personalization engines, etc.) and channel types (web, mobile apps, email, SMS, paid media, social, messaging apps, DOOH), highlights issues around platform and channel ownership, and recommends end-to-end QA/UAT and root-cause troubleshooting. The piece emphasizes timing and data propagation between touchpoints, the need to map touchpoints for QA, and the role of martech practitioners in bridging business and technical teams to prevent campaign issues before launch.