KPMG AI Quarterly Pulse Survey

• 93% of leaders agree that investments to-date in GenAI have allowed their company to enhance its competitive position and long-term strategic performance.
• Leaders plan to invest nearly $114 million in GenAI over the next year, up from $89 million last quarter.
• Organizations are rapidly accelerating from experimentation to piloting AI-agents – the latter is up from 37% to 65% since last quarter. Those deploying agents remains flat at 11%.
• 82% of leaders expect risk management to be the biggest challenge to their GenAI strategies for the remainder of 2025, followed by quality of organizational data (64%) and personal trust in the technology (35%).
• Productivity tool usage on a daily basis is up to 58% from 22%. Knowledge assistant usage on a weekly basis is up to 61% from 48%, as is GenAI usage embedded into existing workflows, jumping from 24% to 35%.
• 32% of leaders believe trust in the accuracy and fairness of AI outputs will be the greatest society-wide challenge with AI between now and 2030.

NEW YORK, April 16, 2025 – The vast majority of leaders (93%) agree that GenAI investments to-date have enhanced their company’s competitive position and are planning to increase investments to nearly $114 million over the next year, according to our latest AI Quarterly Pulse Survey.

This increase in investment comes as investor pressure has spiked from 68% to 90% since last quarter, driving companies to accelerate their AI initiatives. Leaders are responding confidently with concrete ROI metrics that justify their next steps – 97% today report measurable profitability and 94% cite significant productivity gains from GenAI implementations.

However, to effectively operationalize their strategies, organizations must overcome critical barriers in data infrastructure and workforce preparation. Looking ahead, leaders expect risk management (82%) to be the biggest challenge to their GenAI strategy throughout the remainder of 2025, followed by quality of organizational data (64%). As a result of these growing priorities, Chief Information Officers’ roles in leading AI strategies increased from 31% to 86%, while CEO involvement dropped from 34% to 8%, year-over year.  

“We’re seeing a clear shift from pilots to scaled execution, with CIOs increasingly leading the charge,” said Steve Chase, Vice Chair of AI and Digital Innovation at KPMG. “While that may seem like a natural decision given the scale of change in both technology and how it’s delivered – AI is also an enterprise transformation. It requires rewriting business processes, disrupting offerings, and driving cultural change. Leading organizations are creating space – often through a dedicated AI leader – to fully own that broader vision and protect the transformation from unnecessary risk.”

AI-agent strategies: pilot-heavy but deployment-light

Organizations have largely moved beyond AI-agent experimentation and into the pilot phase, increasing from 37% last quarter to 65%; however, full deployment remains stagnant at 11%.

Of those experimenting, piloting and deploying AI-agents, over a quarter (26%) of organizations are using agents to recruit and source new employee candidates (up from 15% last quarter) with another 60% of organizations planning to utilize them in the next 12 months (up from 50% last quarter). Another 61% are using agents for call centers (up from 16%), 66% use them for administrative duties (up from 27%) and 78% of organizations are using agents to analyze complex data sets (up from 70%). Notably, there has been a jump in the percentage of organizations transitioning from planning to use agents to actively piloting agents, across most categories.

AI is reshaping how work gets done

AI productivity tools and knowledge assistants are becoming mainstream for over half of knowledge workers, indicating organizations have moved into a more transformative phase, reshaping how work fundamentally gets done as AI is increasingly incorporated into business operations. Productivity tools are being used on a daily basis – up to 58% from 22% last quarter. Knowledge assistant (RAG models)[1] usage on a weekly basis is up to 61% from 48% as is GenAI usage embedded into existing workflows, up from 24% to 35%.

As adoption has surged, leaders have grown increasingly confident about AI's role in workplace transformation. In the next 12 months, 76% of leaders agree or strongly agree AI will automate specific tasks but will not replace roles entirely, while 69% believe AI will help high performers focus on more strategic work. Additionally, 57% expect AI to help underperforming employees improve their capabilities.

Despite an increase in the adoption of agents and assistants, organizations face hurdles in preparing their workforce for an AI-powered future. The top anticipated challenges in training employees to work with agents include the complexity of systems (66%), keeping pace with rapidly evolving technology (56%), and technical skills gaps (51%).  

“Agents are increasingly recognized as essential assets that need to be integrated into our workforce ecosystem,” said John Doel, Principal, Human Capital Advisory at KPMG. “As employees acquire new skills to interact with AI and organizations develop capabilities to manage these agents, a future hybrid digital workforce will emerge, unlocking unprecedented value from GenAI.”

As AI drives competitive edge, trust emerges as a critical priority

While organizations express an overwhelming confidence in AI’s ability to enhance long-term performance, concerns about trust highlight an emerging paradox. Leaders believe trust in the accuracy and fairness of AI outputs will now be the greatest society-wide challenge with AI between now and 2030 (32%), followed by the misuse of AI by bad actors (30%). Personal trust in GenAI is also now considered a top three challenge in 2025, according to over a third of leaders. This tension underscores the critical importance of balancing bold, fast implementation with responsible governance to capture AI's transformative potential while mitigating its risks simultaneously.

“As agents move into workflows, trust is back at the center of the AI conversation,” continued Chase. “There is no agent-powered future without a strong foundation of trust—grounded in governance, data integrity, and responsible use. If you want to scale with confidence, trust has to come first.”

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[1] Retrieval augmented generation (RAG) is a technique used to augment interactions with AI, using external data such as a specified set of documents, to respond to user questions.

The KPMG Quarterly Pulse Survey captures perspectives from 130 U.S.-based C-suite and business leaders representing organizations with an annual revenue of $1 billion or more. Read additional findings below:

• The top three categories projected to spend between $10-49.9 million include research & development (34%), new technology solutions (28%), and data and analytics (26%).
• Technology (76%), Operations (74%) and Risk (56%) are the functions most likely to benefit from Agentic AI according to leaders, followed by Finance (39%), Marketing and Sales (35%) and Human Resources (8%).
• Data privacy and security are now the most important decision points when choosing a GenAI (LLM) provider, increasing from 43% to 73% since last quarter.
• 67% of organizations intend to purchase a pre-built AI agent solution, while another 27% are planning to adopt a combination of internally built and purchased solutions.
• In the next 12 months, 76% of leaders agree or strongly agree AI will automate specific tasks but will not replace roles entirely. 69% agree or strongly agree AI will help strong performers focus on more strategic work. Another 57% believe AI will help low performers become stronger performers. 
• 54% of organizations now include GenAI as part of their formal performance track, up from 19% last quarter.
• The top risk mitigation measures organizations are putting into place in the next 6-12 months as it relates to AI-agents include deploying agents by trusted tech providers (63%) and not allowing agents access to sensitive data without human oversight (52%). 

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