StackAdapt report reveals marketer AI trust issues as autonomous adoption stalls

The findings from StackAdapt indicate that while AI is a standard tool for analysis, a significant trust gap prevents it from taking over execution. For the streaming and ad-tech ecosystem, this suggests that 'black box' algorithms will face increasing resistance unless they provide granular transparency and human-defined guardrails. As leadership pressure to adopt AI grows, the disconnect between executive expectations and operational readiness could slow the deployment of advanced programmatic features. Watch for a shift in ad-tech product roadmaps toward 'explainable AI' features that prioritize strategic rationale over pure automation to win over skeptical media buyers.

StackAdapt operates in a competitive programmatic landscape where multiple demand-side platforms are racing to embed AI-driven automation into their buying workflows. In early 2026, Blue Planet and Telefónica Deutschland completed a proof of concept using agentic AI to automate 5G network slicing service design, demonstrating that intent-based AI agents can reduce multi-week manual processes to minutes. While that deployment targets telecom orchestration rather than ad buying, it illustrates the same delegation challenge StackAdapt's report identifies: operators trust AI for analysis but resist ceding full execution control without transparent guardrails and repeatable workflows.

On the business side, StackAdapt's findings arrive as ad-tech vendors push harder to monetize AI features through premium pricing tiers. Ericsson's own data underscores how AI-driven traffic patterns are reshaping infrastructure economics that underpin ad delivery. Ericsson's June 2025 Mobility Report quantified how generative AI is creating bidirectional traffic demands that strain mobile networks, a shift that increases the cost of serving rich video ad formats programmatically. For platforms like StackAdapt that serve video and CTV inventory, rising infrastructure costs tied to AI-generated content and uplink-heavy workloads add a financial dimension to the trust question: marketers must weigh whether autonomous optimization can deliver enough efficiency to offset growing delivery expenses.

Technical benchmarks from adjacent AI-RAN deployments offer a parallel for how explainability affects adoption. At MWC 2026, Ericsson's networks chief Per Narvinger described how AI models improved a 30-year-old spectrum algorithm by 10 percent, arguing that the value proposition becomes clear when tied to concrete dollar figures such as spectrum license costs. Ericsson separately published guidance claiming agentic AI can reduce time spent on network analysis and decision-making by 80 percent, a figure that mirrors the efficiency gains StackAdapt's respondents report from AI-assisted campaign management. The pattern is consistent across verticals: quantified, transparent performance gains build trust faster than opaque automation promises, suggesting StackAdapt and rival DSPs will need to surface granular attribution and reasoning layers to close the delegation gap their own data reveals.