April 27, 2026
Most Brands Are Failing to Turn AI Into Customer Engagement Results
Almost every brand has invested in AI for customer engagement. Yet, very few are seeing returns. Two new studies, one from Forrester Consulting and one from behavioural science consultancy Cowry, suggest that the industry’s spending spree has not yet produced the business outcomes to justify it.
The research, commissioned by customer engagement platform Braze, describes an industry stuck between experimentation and execution. Brands have the tools and the data, but what they lack is an operational model that embeds AI into live customer journeys in a way that produces results.
More Messages, Less Impact
Consumers are now exposed to as many as 10,000 commercial messages per day. In that environment, using AI to produce more content faster only accelerates disengagement.
Astha Malik, Chief Business Officer of Braze, said: “While AI-generated content is potentially infinite, customer attention is finite and loyalty is fragile. Every piece of ‘AI slop‘ that reaches a customer simply trains them to tune out.”
Malik drew a line between brands that use AI to increase output and those that use it to improve outcomes like conversion, retention, and revenue. The industry, she argued, needs to stop equating AI productivity with AI performance.
A Divide Is Forming
The brands seeing results share several common traits. They activate first-party data in real time rather than batching it into periodic campaigns. They embed AI directly into live customer journeys as a decisioning layer, not as a standalone experiment running alongside existing workflows. Then, they consolidate their technology stacks to reduce fragmentation and give teams enough speed and consistency to execute well. And they treat campaigns as adaptive systems that learn and optimise continuously, rather than fixed assets that sit untouched until someone manually intervenes.
The research also identifies what brands should prioritise going forward, which includes moving from scheduled batch campaigns to signal-based engagement, investing in orchestration and decisioning rather than content creation alone, reducing tool sprawl, and tying every initiative to commercial outcomes.
An Industry-Wide Problem
The technology has matured, and the money has been spent. Still, the organisational and cultural conditions required to make AI deliver, including data quality, leadership alignment, and a willingness to retire legacy campaign thinking, remain unevenly distributed across the market.
A WSJ Intelligence and Code and Theory study found that 93% of executives described their customer experience as “broken”, with cultural dysfunction, leadership misalignment, and organisational silos identified as the main obstacles to progress.
AI adoption alone does not produce engagement results. How it is woven into decisions, operations, and customer-facing workflows determines whether brands see returns or simply add noise to an already overwhelmed audience.
