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Enterprises of all sizes around the world are trying to make sense of generative AI and determine where it might add value. The good news: The majority of organizations are actually making it work.
According to a new report today from Deloitte, the majority of enterprises are actually meeting or exceeding their own expectations for return on investment (ROI) from gen AI. The “State of Generative AI Q4” report, based on a survey of 2,773 leaders across 14 countries, highlights both the progress and challenges organizations face in their gen AI journeys.
The report shows considerable progress from the first version released a year ago, in which business leaders expressed multiple concerns. There is also positive progress over the third quarter report, which showed that the majority of organizations had avoided some gen AI use cases due to data issues.
Despite longer-than-expected time to value, nearly three-quarters (74%) of respondents reported that their most advanced gen AI initiatives are meeting or exceeding ROI expectations. Cybersecurity and IT functions are leading the way in terms of ROI and successful scaling.
Key findings include:
- Organizations require at least 12 months to resolve major adoption challenges
- IT, cybersecurity, operations, marketing and customer service show strongest adoption and results
- Regulatory compliance has emerged as the top barrier to gen AI deployment
- 78% of respondents expect to increase their overall AI spending in the next fiscal year
Jim Rowan, head of AI at Deloitte, told VentureBeat that the biggest gains enterprises are reporting from AI usage are efficiency and cost savings.
“We’re taking time out of day-to-day tasks and activities and making individuals more efficient,” said Rowan.
The challenge of gen AI moving at enterprise speed
Enterprise technology by definition is about stability and resilience. It is supposed to be the stuff businesses run on. For many types of technology, enterprise adoption can take multiple years as organizations first need to validate use cases and ROI potential.
While the rapid advancements in gen AI capabilities have captured the public’s imagination, enterprises are often moving at a much slower pace when it comes to adoption. This disconnect between the breakneck speed of AI innovation and the more deliberate nature of enterprise technology rollouts presents a significant challenge.
“Enterprises are moving at enterprise speed,” said Rowan. “That plays out in a couple different areas within the report, in terms of scaling questions, risk and regulatory challenges that organizations are facing across the board.”
This disparity in speed is further complicated by the fact that many enterprises are still grappling with foundational technology challenges, such as data governance and platform modernization. Rowan noted that those underlying issues must be addressed before enterprises can fully capitalize on the potential of generative AI.
Rather than rushing to deploy the latest gen AI tools, Rowan emphasized the importance of a more measured, strategic approach that focuses on building the necessary infrastructure and cultural readiness. By taking the time to properly integrate gen AI into existing operations and workflows, enterprises can ensure that the technology delivers tangible, long-term value, rather than just serving as a fleeting novelty. This patient, deliberate approach, while potentially slower in the short term, may ultimately prove more effective in driving lasting transformation.
Where enterprise AI is delivering the most ROI today
One of the key areas where enterprises are seeing tangible value from AI is in the software development lifecycle.
According to the report, AI is helping to drive efficiency gains across the entire process — from requirements gathering to testing and deployment.
“We’re seeing it a ton in the software development life cycle,” Rowan said. “This is why IT has been a big, big proponent of this.”
Beyond software development, enterprises are tapping into AI to enhance their customer service and contact center operations. By automating certain tasks and interactions, companies are able to improve efficiency and responsiveness. “The other big use case is around contact centers, customer service, sort of engagement from those two,” said Rowan. “So those tend to be the largest areas where we’re seeing the most amount of efficiency being taken out.”
How enterprises can measure the impact of gen AI
As enterprises seek to quantify the impact of their AI investments, Rowan emphasized the importance of looking at both quantitative and qualitative metrics.
While cost savings and efficiency gains are important, companies should also track the number of new ideas and use cases generated, as well as the impact on employee skills and culture.
In the quantitative categories Rowan cited a few key metrics:
- Efficiency measurement through cost savings
- Increased revenue generation
- Increased efficiency per full-time equivalent employee (FTE) on some activities.
On the qualitative side, Rowan pointed to metrics around employee development, continuous learning and the overall transformation of business processes.
“How are your employees’ skills improving? How are you using this moment to really change the culture around learning and development?” he said.
Benefitting from the promise of agentic AI
Perhaps the biggest area of innovation for enterprises to consider in 2025 is agentic AI.
The report indicates that 52% of organizations are pursuing AI agents, with 45% specifically exploring multi-agent systems. Rowan expressed optimism about the potential of agentic AI, but noted that it will take time for enterprises to fully adopt and integrate this technology. He explained that enterprises will likely start with simpler, more focused agent applications before expanding their use.
Rowan said that agentic AI has the potential to fundamentally transform enterprise processes and drive significant ROI, but only if approached strategically. With the initial rollouts of gen AI, enterprises often focussed on proof of concept (PoC) deployments. A different approach will be required for agentic AI. Instead of looking at individual use cases, enterprises will be well served looking at the broader process chain. He explained that the true value of agentic AI will come from rethinking entire business processes to be AI-driven, rather than just implementing individual use cases.
“To do agentic, you actually have to think about how you’re going to rebuild processes with the idea that this is all going to be AI driven, not human driven,” he said.
Overcoming adoption challenges
Despite the clear benefits, enterprises continue to face significant hurdles in scaling their AI deployments.
One of the key barriers, according to Deloitte, is the limited access and usage of AI tools within the workforce. According to the report, less than 40% of the workforce in most organizations have access to gen AI tools.
This lack of widespread adoption points to the need for a cultural shift, in which employees are not only given tools, but understand the value and importance of incorporating AI into their daily workflows.
“If you’re not using AI once a day for your day-to-day, whether it’s the corporate tool that you’ve been given or a consumer based tool, I think you’re missing out,” said Rowan.
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