AI agent survey: PwC

2025-05-16 · Source: Curated for you: AI: PwC · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Human Resources & Workforce Development · Depth: Intermediate, medium

Summary

A May 2025 PwC survey of 308 US business executives reveals surging AI agent adoption and investment, with 88% planning budget increases and 79% already using agents. Two-thirds (66%) of adopters report measurable value, primarily increased productivity, cost savings (57%), and faster decision-making (55%). Despite this momentum, most companies are not achieving deep transformation, often limiting agents to routine tasks rather than integrating multi-agent models across complex, cross-functional workflows. Key barriers include organizational mindset, change readiness, and employee engagement, rather than technology itself. Trust in AI agents remains low for high-stakes tasks like financial transactions (20%), highlighting the critical need for responsible AI strategies. Few businesses are fundamentally rethinking operating models or developing new agentic products, risking being outpaced by competitors.

Key takeaway

For Directors of AI/ML or VPs of Engineering tasked with enterprise transformation, you must move beyond isolated AI agent pilots to orchestrate multi-agent systems across functions. Prioritize redesigning operating models and workflows, as organizational change and trust, not technology, are the primary barriers. Embed responsible AI from the start, especially for high-stakes applications, to realize greater value and avoid falling behind competitors who are already reinventing their businesses.

Key insights

Enterprise AI agent adoption delivers productivity, but deep transformation requires strategic integration and addressing human-centric barriers.

Principles

• Organizational mindset, not technology, impedes AI agent transformation.
• Orchestrating multi-agent systems across functions unlocks significant value.
• Responsible AI frameworks are crucial for building trust in high-stakes applications.

In practice

• Integrate multi-agent models across diverse business functions.
• Redesign core operating models and processes around AI agents.
• Develop new agentic products and services.

Topics

• AI Agents
• Enterprise AI
• Multi-Agent Systems
• Organizational Change
• Responsible AI
• Digital Transformation

Best for: Executive, CTO, AI Product Manager, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Curated for you: AI: PwC.