Key findings include:
21% of enterprises have a blank cheque from their board for all types of AI, with 72% saying they have sufficient budget.
While GenAI gets all the attention, slightly more companies are getting predictive AI into production (53%), indicating that traditional machine learning is still the workhorse.
More than 90% of enterprises plan to make some infrastructure adjustments to account for their GenAI journey, most commonly using updated versions of their pre-GenAI stacks.
Everyone needs to upgrade AI governance: 95% face a governance remodel or reboot to update their frameworks and processes for today’s modern model landscape.
Not many companies are hindered by compute scarcity, with 9 out of 10 saying it’s not impacting their needs, and most (87%) are confident in leveraging their AI stacks across various vendor silicon and infrastructure.
When it comes to enterprise attitudes on AI, one thing is for sure: corporate boards are all-in. The survey revealed that 21% of enterprises have a blank cheque from the board for all types of AI. Additionally, nearly all boards are paying attention to AI. Only 5% of respondents said their board hadn’t engaged or set a strategy on AI, and 72% reported their boards provided sufficient support for all AI, including GenAI.
However, the research revealed that some boards may be overly excited by GenAI. A third (34%) of respondents said GenAI initiatives get more board support than they deserve, depriving support from other forms of AI. 8% said it was difficult to get funding for anything other than GenAI.
While attitudes are solid and often very focused on pushing GenAI, actions are more varied. Companies are rapidly putting GenAI into production, but more traditional forms of AI and data analytics remain the workhorse. Slightly more companies, 53% and 57% respectively, are putting predictive AI — i.e. machine learning — and advanced analytics projects into production than GenAI (49%). However, the distinction between these types of projects is blurring. 41% of leaders say that they have projects that use both predictive and generative AI in production.
This survey also found that enterprises are still early in their AI journeys. Over half of respondents are still in the planning, researching, or proof of concept stage when it comes to GenAI, and 47% have not yet put predictive AI projects into production even though these technologies have been available for decades — an indication that companies still struggle to move from experimentation phases to productising all types of AI.
As GenAI reshapes the AI landscape, enterprises in EMEA are taking a more cautious and measured approach, reflecting unique regional challenges and regulatory considerations. Respondents in EMEA are nearly twice as likely (47%) than their North American counterparts (31%) to keep their existing AI stack as a result of GenAI’s arrival. Perhaps this is because EMEA AI Leaders were less likely to rate their boards’ support for all AI as sufficient (66% of EMEA respondents vs. 77% of North American respondents).
If companies are indeed amidst a rapid AI deployment spree, then this comes with the need for robust governance frameworks and scalable infrastructure to support these advanced technologies. The study found that 95% face a governance remodel or reboot to update their frameworks and processes for today’s modern model landscape.
Reassuringly, most companies say they have a baseline of necessary responsible AI infrastructure and processes in place. They believe that they can incorporate more data sources and more data into the AI equation. Most (65%) of the respondents said their companies need only add new processes to existing governance frameworks to compensate for this change. Also, in a possible reaction by companies now subject to the new EU AI Act, nearly double the percentage of EMEA respondents (16%) indicated that they would fully replace their current governance framework in the era of GenAI, compared to just 9% of respondents in North America.
