Marketers are using AI tools almost universally, but they still don't trust the recommendations those tools produce. According to StackAdapt's "The AI Delegation Gap" report, conducted by NewtonX, 88% of global marketing and advertising professionals report AI-driven performance improvements - yet a relative majority (42%) ignore AI recommendations because they feel too generic or unrelated to their campaigns, and 22% ignore them because they don't align with strategy.
The gap between usage and trust is the next hurdle for AI adoption in marketing. Teams are treating AI as an analyst rather than a strategist, and the data shows that simply increasing AI usage doesn't automatically improve trust or usefulness.
What drives marketers to act on AI advice
The report identifies what would make marketers more willing to follow AI recommendations. A third (33%) say a clear explanation or rationale would prompt them to act, while 31% want a direct connection to a KPI they care about.
Benefits increase when AI systems have strong signals on brand voice, customers, objectives, historical performance, and desired outcomes. That puts pressure on brands to connect first-party data and measurement infrastructure to AI workflows - and to tackle data siloes that give AI fragmented information.
There are real limitations to handing campaign decisions over to automation. AI can reduce workload, but if recommendations don't account for brand tenets, specific KPIs, or campaign strategy, it adds a new task: deciding when to trust the machine.
How marketers can close the delegation gap
The report offers practical steps for making AI recommendations more trustworthy. Train AI tools on brand identity, products, prior campaigns, and customer relationships to help shape better recommendations. When sharing proprietary information, use only company-approved models.
Use human judgment to vet AI outputs, which helps avoid generic or strategically misaligned recommendations that fail to aid campaign development. For specific workflows, explore creating customized tools - like ChatGPT's GPTs or Gemini's Gems - trained on content tailored to the task at hand. Set up "undo" functionalities so decisions can be reversed if they don't improve workflows or cause brand concerns.
For marketing teams working through these issues, practical training can help bridge the gap between AI adoption and effective use. Resources like AI for Marketing cover campaign optimization and performance improvements, while AI for Marketing Managers addresses the strategy and adoption challenges this report highlights.
Why this matters for marketers
The report's core finding is that AI adoption has outpaced AI trust. Marketers who want better results from their AI investments need to give the technology more business context - brand voice, campaign history, customer data, and KPI priorities - before expecting recommendations they can act on.
The practical takeaway: treat AI as a tool that needs training, not a black box that should just know what to do. The marketers who close the delegation gap will be the ones who invest in both the technology and the context that makes its advice worth following.
Your membership also unlocks:
