Agentic AI’s splashy arrival on the business scene is proving to be more of a rapid sociotechnical evolution than a pure-play technological upgrade. As such, this autonomy-driven sociotechnical evolution is itself increasingly being shaped by trust, structural readiness and strategic conviction.
PYMNTS Intelligence research in the August 2025 edition of The CAIO Report, “Tech on Tech: How the Technology Sector Is Powering Agentic AI Adoption,” finds that industry readiness is dividing into a clear line as firms race to move beyond generative artificial intelligence (AI) experiments toward fully agentic AI workflows where systems plan, decide and act autonomously.
Sectors such as software and financial services are surging ahead, buoyed by engineering talent, agile risk cultures and flexible budgets. On the other hand, goods and services industries, including manufacturing, logistics, retail and hospitality, are lagging, held back by structural fragmentation, operational complexity and murkier paths to return in investment (ROI).
Ultimately, what the report highlights is that the shift from AI novelty to full-on AI autonomy hinges on two intertwined axes: trust and sector tailwinds. Trust represents the confidence individual firms have that systems will act as intended, and tailwinds speak to the strategic and structural advantages endemic to certain sectors that help enable rapid, risk-tolerant adoption.
How Purpose and ROI Shape Agentic AI Readiness Across Industries
Across every industry, the raw horsepower of AI adoption starts with engineering and data science talent. In tech-native sectors, especially software, digital platforms and FinTech, firms are flush with AI-literate engineers and forward-leaning leadership. Their capabilities allow rapid prototyping, internal tooling, model fine-tuning and speedy iteration toward agentic prototypes. This, in turn, can allow tech firms to traverse the early stages of the S-curve of innovation more quickly.
Confidence in agentic AI correlates strongly with earlier returns from generative AI. PYMNTS data showed that as firms experienced positive ROI from gen AI, their trust in the next generation of autonomous tools grew.
In contrast, goods and services industries remain peripheral due to lower diffusion of AI experiments and fewer internal catalysts for exploration. Without sufficient in-house AI fluency (few data scientists, minimal MLOps capabilities), these firms can struggle to build trust in systems that act on their behalf.
After all, trust doesn’t exist in a vacuum. It is built, or eroded, through repeated interactions with AI systems.
Differences in operational context shape trust in agentic AI. Goods and services companies manage sprawling, bespoke systems.
Example: an intelligent autonomous logistics agent must coordinate across warehouse management systems, fleet scheduling, ERP, customer portals and external carriers. Each integration surface is a risky fault line. Building trust in autonomy means accounting for every touchpoint, including real-time sensor feeds, regulatory compliance and human override capabilities, all while ensuring reliability. That complexity can make autonomous systems appear brittle rather than transformative, amplifying risks associated with autonomous decision-making.
Read the report: Tech on Tech: How the Technology Sector Is Powering Agentic AI Adoption
Services firms, including customer-facing and digital operations businesses, prioritize platform compatibility and human oversight. The relational nature of services underscores the value of human or assisted judgment over full autonomy.
Tech firms, by contrast, focus heavily on bias monitoring and reputational risk management, reflecting a more advanced AI literacy and data-driven governance framework. These sectoral distinctions suggest that agentic AI’s appeal can be framed not only in efficiency terms but also anchored to operational metrics, trust constructs, and risk appetites.
Yet while tech firms provide valuable proof of concept for agentic AI, they are not a one-size-fits-all model. Their control over code bases, access to AI-native talent, and innovation-first cultures give them advantages that other industries cannot easily replicate.
But for goods and services firms, by building the infrastructure, cultivating AI fluency, expanding risk-tolerant zones, investing with longer horizons, and rearchitecting for modular autonomy, agentic AI can shift from a distant promise to an integrated industrial reality.
Source: https://www.pymnts.com/
