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Businesses of all sizes around the world are struggling to understand the meaning of generative artificial intelligence and determine where it could add value. The good news: Most organizations actually do.
Most companies are actually meeting or exceeding their own return on investment (ROI) expectations from gen AI, according to a new report from Deloitte today. Based on a survey of 2,773 leaders in 14 countries, the State of Generative AI Q4 report highlights both the progress and challenges organizations face on their AI journeys.
The report shows significant progress over the first version released a year ago, in which business leaders raised numerous concerns. It’s also positive progress from the third-quarter report, which showed that most organizations avoided some gen AI use cases due to data issues.
Despite longer-than-expected time to value, nearly three-quarters (74%) of respondents said their most advanced AI initiatives are meeting or exceeding ROI expectations. Cyber security and IT functions lead in terms of return on investment and successful scaling.
Key findings include:
- Organizations need at least 12 months to resolve major adoption issues
- IT, cybersecurity, operations, marketing and customer service show the strongest adoption and results
- Regulatory compliance has emerged as a major 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 benefits companies report from using AI are efficiency and cost savings.
“We’re taking time out of everyday tasks and activities and increasing the efficiency of individuals,” Rowan said.
The challenge of genetic artificial intelligence moving at enterprise speed
Enterprise technology is inherently about stability and resilience. These should be the things businesses run on. For many types of technology, business adoption can take years, as organizations must first validate use cases and ROI potential.
While rapid advances in genetic AI capabilities have captured the public imagination, businesses are often much slower to adopt. This disconnect between the breakneck speed of AI innovation and the more deliberate adoption of enterprise technology presents a significant challenge.
“Businesses move at enterprise speed,” Rowan said. “This plays out in several different areas of the report in terms of scaling issues, risks and regulatory challenges that organizations face across all areas.”
This disparity in speed is further complicated by the fact that many enterprises are still grappling with fundamental technology challenges such as data management and platform modernization. Rowan noted that these fundamental issues must be addressed before businesses can fully exploit the potential of generative artificial intelligence.
Rather than rushing to deploy the latest generation of AI tools, Rowan emphasized the importance of a more thoughtful and strategic approach that focuses on building the necessary infrastructure and cultural readiness. By taking the time to properly integrate genetic AI into existing operations and workflows, businesses can ensure that the technology delivers tangible long-term value and is not just a passing 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 greatest return on investment today
One of the key areas where businesses are seeing tangible value from AI is in the software development lifecycle.
According to the report, AI helps increase efficiency throughout the process – from requirements gathering to testing and deployment.
“We see this a lot in the software development lifecycle,” Rowan said. “That’s why IT has been a big, big supporter of this.”
In addition to software development, companies are using AI to improve 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 and some engagement of the two,” said Rowan. “So those tend to be the biggest areas where we see the biggest reduction in efficiency.”
How businesses can measure the impact of gen AI
As businesses 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 monitor the number of new ideas and use cases created, as well as the impact on employee skills and culture.
In quantitative terms, Rowan cited several key metrics:
- Measuring efficiency through cost savings
- Increased revenue generation
- Increased efficiency per full-time employee (FTE) for some activities.
On the qualitative side, Rowan pointed to metrics around employee development, continuous learning and overall business process transformation.
“How are your employees’ skills improving? How are you using this moment to truly change the culture around learning and development? he said.
Benefit from the promise of agentive AI
Perhaps the biggest area of innovation that businesses should consider in 2025 is agentic AI.
The report states that 52% of organizations are pursuing AI agents, with 45% specifically exploring multi-agent systems. Rowan expressed optimism about the potential of agent-based AI, but noted that it will take time for enterprises to fully adopt and integrate the technology. He explained that enterprises are likely to start with simpler and more targeted agent applications before expanding their use.
Rowan said agentic AI has the potential to fundamentally transform business processes and drive significant ROI, but only if approached strategically. With the initial adoption of gen AI, businesses often focused on proof of concept (PoC) implementations. A different approach will be required for agent AI. Instead of looking at individual use cases, businesses will be well served by looking at the broader process chain. He explained that the real value of agent-based AI will come from rethinking entire business processes to be driven by AI, rather than simply implementing individual use cases.
“To do agents, you really have to think about how you’re going to redesign processes with the idea that it’s all going to be driven by AI, not humans,” he said.
Overcoming adoption issues
Despite the clear benefits, businesses continue to face significant barriers to expanding AI deployment.
One of the key barriers, according to Deloitte, is the limited access and use of AI tools within the workforce. According to the report, less than 40% of the workforce in most organizations has access to gen AI tools.
This lack of widespread adoption points to the need for a cultural shift where employees are not only given the tools, but understand the value and importance of incorporating AI into their daily work practices.
“If you’re not using AI once a day for your daily life, whether it’s a business tool that you’re given or a consumer tool, I think you’re missing out,” Rowan said.
