Generative AI is now virtually ubiquitous in global businesses, with major companies prioritizing it and its deployments spreading at an unprecedented pace. New research from Bain & Company reveals that the adoption of this technology is still accelerating.
Bain’s latest proprietary cross-industry Generative AI Survey indicates that almost 9 in 10 companies (87%, up from 83% in October last year) have already deployed or are now piloting the technology, with adoption continuing to climb rapidly across all use cases.
Bain’s analysis shows a significant increase in businesses’ spending and other commitments to generative AI use. More than 60% of businesses surveyed rank it among their top three priorities for this year and next, with 87% ranking it among their top five priorities for the next three to four years.
On average, companies are already budgeting around $5 million per year for generative AI activities and technology infrastructure, with this average rising to US$50 million per year for 20% of the largest companies, indicating an increasingly large-scale commitment to generative AI implementation.
The survey of senior executives in 200 businesses, split evenly between technology and non-technology companies, also shows a rapid scaling of teams working on generative AI. On average, companies have around 100 people involved in some capacity, with large companies having as many as 240 team members.
As companies worldwide race to capture generative AI’s competitive advantages, the greatest focus for executives is on harnessing benefits from revenue boosts alongside enhanced efficiency and productivity. Both goals are cited by 68% of companies surveyed as among their top three primary objectives.
However, the survey data also reveals that only about 36% of executives feel their organization has a strong, well-defined vision for AI deployments, with a sequenced roadmap and clear value expectations. Additionally, 21% of organizations have ideas for generative AI deployment but have not yet made coordinated efforts.
Despite this, generative AI is meeting or exceeding expectations in 75% of instances. Around 80% of respondents noted that prototyping for generative AI use is faster than with earlier AI technologies and machine learning.
Where AI deployments have fallen short, the most common issues cited are poor output quality or the technology not meeting performance needs. The next most common issues involve user adoption and off-the-shelf tooling not delivering expected value.
However, performance improvements are being seen in key use cases such as sales, sales operations, marketing, customer service, and customer onboarding. Concerns over risk, data security, privacy, and regulation have also declined. Most firms still see room to improve their generative AI preparedness across data readiness, data security, and talent.
“The scale and pace of generative AI adoption across the business landscape is remarkable. It speaks to this technology having a truly far-reaching and transformative impact for companies across sectors as it continues to develop – and as deployments continue to accelerate,” said Gene Rapoport, Bain & Company partner and leader of AI initiatives for Bain’s Private Equity practice.
“It’s equally impressive that, with most major businesses already putting money and muscle behind generative AI implementations, the majority are seeing a path towards realizing real business value. CEOs and executive committees need to take clear ownership of activating AI in their organizations and ensuring a clear, well-defined vision for its use.
“The businesses that do are going to emerge quickly as leaders, securing the best results and the greatest competitive advantage.”
Sanjin Bicanic, Bain & Company partner and member of Bain’s Advanced Analytics Group, added, “Many software companies are adding AI features to their products at a breakneck pace, but our research shows those solutions are not yet fully featured enough to create value for the enterprise.
“This gap in perceived value, combined with the availability of frontier models as APIs, is why we’re seeing so many companies choose to build to capture value quickly. As solutions improve, we expect more buying, but the landscape is shifting rapidly and it’s not yet clear where building might be the correct long-term solution.”
Four evolving themes from Bain’s analysis show how companies are thinking about generative AI:
- Delivering Value: Conversations about generative AI are moving from excitement and hype to more realistic assessments of value delivery;
- Promise Areas: Successful use cases include sales, software development, marketing, knowledge worker assistants, and customer service;
- Tech Industry Readiness: Technology companies are more prepared for generative AI in terms of data and security protocols and are further ahead in adoption compared to non-tech industries; and
- Buy or Build: Both approaches are being tested. Companies are buying third-party solutions when available but are also investing in tailoring them to their needs.
As generative AI adoption increases, concerns around organizational readiness have grown, indicating the need for companies to refine their strategies and infrastructure to fully leverage this transformative technology.
