Altman Solon: Enterprise Gen AI shifts focus to strategic innovation

Adoption of Gen AI (generative AI) is widespread in companies of various types and sizes, with nearly 80% of respondents reporting tangible benefits, according to a new survey of IT leaders. But the commercial impact is still developing, with few companies managing to scale.

A new report from Altman Solon, a leading global telecommunications, media, and technology consulting firm, in collaboration with Cowen, reveals an evolution in how large companies are leveraging Gen AI. Findings from their survey show that the motivation for adopting these tools has been shifting from simple cost reduction more towards broader goals, like keeping up with innovation.

Although organizations are seeing pronounced improvements in efficiency and productivity, these gains have not yet fully translated into corresponding profit growth or broad cost savings. In many cases, the technology work, but its overall scale of impact and pricing structure may need to be adjusted.

AI tools have become incredibly popular in recent years, with countless companies investing in pilot programs, hoping to eventually unlock real business value. There is still, however, a lot of mystification and hype that can be an obstacle in the way of gaining actual results. There are also lingering concerns regarding data privacy, accuracy, and responsible governance.

Finding value in Gen AI

A shift in strategic intent is clear: In 2024, 68% of respondents cited cost reduction as their primary goal for utilizing Gen AI, but that figure has dropped to 50% in 2025. Meanwhile, the percentage of IT leaders that said they are motivated mainly by innovation has increased from 55% to 64%.

Across all functions, the top driver has long been ‘expediting processes’, cited by 87% of respondents. This suggests that Gen AI is increasingly viewed as an engine for long-term capability building rather than a simple tool for short-term savings, though that has been one of its main attractions.

Despite high intentions, the pace of large-scale deployment has been slower than anticipated. 83% of organizations predicted they would be using Gen AI by 2025, a target that has not yet been met. This execution gap may point to the difficulty of scaling the technology.

Risks and concerns

Companies are also considering risks: Data security was cited as the main implementation barrier by 71% of respondents. Other top concerns include realizing value and ensuring model accuracy, though worries over the immaturity of AI tools have slightly decreased, which shows growing confidence in the rapidly maturing technology.

While a majority (nearly 70%) of respondents said that the value of Gen AI was unclear in their business contexts, that percentage has fallen dramatically to 38%. This might be due to more creative thinking when it comes to implementation of AI tools, or might have to do with the growing number of available Gen AI solutions on the market.

Agentic AI

Enterprise use is expanding across functions such as software development and back-office operations like marketing and human resources. Companies are beginning to move beyond automating isolated tasks to experimenting with agentic AI, which can autonomously execute complex workflows.

A quarter of enterprises surveyed already employ these advanced systems, with even more planning adoption within three years. This shift reflects a maturing market that is moving from initial experimentation toward deliberate, value-driven implementation, aiming to link measurable productivity gains to overarching business outcomes.

More productive and efficient

Overall, the vast majority of respondents agree that productivity and efficiency benefits are the main commercial impact that Gen AI brings to the table. A whopping 91% agreed that Gen AI impacted productivity and efficiency in their organizations. A majority also noted quality improvements and 45% said they were confident Gen AI is having a commercial impact.

The real challenge for companies that already adopted Gen AI tools has been to link these productivity gains to measurable business outcomes, the Altman Solon report notes. An increase in productivity does not necessarily lead automatically to additional value creation.

“As the enterprise AI landscape matures, success will depend less on early adoption and more on effective integration, measurement, and governance,” said Ben Matthews, partner at Altman Solon.

“The next wave of leaders will be those who treat AI not as a cost-saving experiment but as a strategic lever for innovation, differentiation, and growth.”