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A recent study conducted by open-source AI solutions firm ClearML in partnership with the AI Infrastructure Alliance (AIIA) has shed light on the adoption of generative AI among Fortune 1000 (F-1000) enterprises.
The study, “Enterprise Generative AI Adoption: C-Level Key Considerations, Challenges, and Strategies for Unleashing AI at Scale,” revealed the economic impact and significant challenges top C-level executives face in harnessing AI’s potential within their organizations.
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According to the global study, 59% of C-suite executives lack the necessary resources to meet the expectations of generative AI innovation set by business leadership. Budget constraints and limited resources emerged as critical barriers to successful AI adoption across enterprises, hampering creation of tangible value.
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The study also found that 66% of respondents cannot fully measure the impact and return on investment (ROI) of their AI/ML projects on the bottom line. This highlights the profound inability of underfunded, understaffed and under-governed AI, ML and engineering teams in large enterprises to quantify results effectively.
“While most respondents said they need to scale AI, they also said they lack the budget, resources, talent, time and technology to do so,” Moses Guttman, cofounder and CEO of ClearML, told VentureBeat. “Given AI’s force-multiplier effect on revenue, new product ideas, and functional optimization, we believe critical resource allocation is needed now for companies to invest in AI to transform their organization effectively.”
The study also highlights the soaring revenue expectations from AI and ML investments. More than half of respondents (57%) report that their boards anticipate a double-digit increase in revenue from these investments in the coming fiscal year, while 37% expect a single-digit growth.
The study collected responses from 1,000 C-level executives, including CDOs, CIOs, CDAOs, VPs of AI and digital transformation, and CTOs. According to ClearML, these executives spearhead generative AI transformation in Fortune 1000 and large enterprises.
The state of generative AI adoption
According to the study, most respondents believe unleashing AI and machine learning use cases to create business value is critical. Eighty-one percent of respondents rated it a top priority or one of their top three priorities.
Moreover, 78% of enterprises plan to adopt xGPT/LLMs/generative AI as part of their AI transformation initiatives in fiscal year 2023, with an additional 9% planning to start adoption in 2024, bringing the total to 87%.
Respondents were also nearly unanimous (88%) on their organizations’ plan to implement policies specific to the adoption and use of generative AI across enterprise business units.
However, despite generative AI and ML adoption being a key revenue and ingenuity engine within the enterprise, 59% of C-level leaders lack adequate resources to deliver on business leadership’s expectations of gen AI innovation.
They face budget and resource constraints that hinder adoption and value creation. Specifically, people, process and technology are all critical pain points identified by F-1000 and large enterprise executives when it comes to building, executing and managing AI and machine learning processes:
- 42% indicate a critical need for talent, especially expert AI and machine learning personnel, to drive success.
- An additional 28% flag technology as the key barrier, indicating a lack of a unified software platform to manage all aspects of their organization’s AI/ML processes.
- 22% cite time as a key challenge, describing the excessive time spent on data collection, preparation and manual pipeline building.
In addition, 88% of respondents indicated their organization seeks to standardize on a single AI/ML platform across departments versus using different point solutions for different teams.
“Enterprise decision-makers are poised to increase investment in generative AI and ML this year, but according to our survey results, they’re seeking a centralized end-to-end platform, not scattering spend across multiple point solutions,” ClearML’s Guttmann told VentureBeat. “With growing interest in materializing business value from AI and ML investments, we expect that the demand for increased visibility, seamless integration and low code will drive generative AI adoption.”
Key challenges hindering generative AI adoption
The study revealed that rising AI and generative AI governance concerns have led to dire financial and economic consequences.
It was found that 54% percent of CDOs, CEOs, CIOs, heads of AI, and CTOs reported that their failure to govern AI/ML applications resulted in losses to the enterprise, while 63% of respondents reported losses of $50 million or more due to inadequate governance of their AI/ML applications.
When asked about the key challenges and blockers in adopting generative AI/LLMs/xGPT solutions across their organization and business units, respondents identified five main challenges:
- 64% of respondents expressed concerns about customization and flexibility, particularly the ability to tailor models using their fresh internal data.
- 63% of respondents ranked data preservation as a top priority, focusing on generating AI models and safeguarding company knowledge to maintain a competitive edge while protecting corporate IP.
- 60% of respondents highlighted governance as a significant challenge, emphasizing the importance of restricting access to and governing sensitive data within the organization.
- 56% of respondents indicated that security and compliance were top-of-mind, given that enterprises rely on public APIs to access generative AI models and xGPT solutions, which exposes them to potential data leaks and privacy concerns.
- 53% of respondents cited performance and cost as one of the top challenges, primarily related to fixed GPT performance and associated costs.
According to Guttmann, the lack of visibility, measurability, and predictability identified in the survey poses a troublesome obstacle to success in adopting new technology. All those factors are crucial for success.
“Enterprise customers should strive to get out-of-the-box LLM performance, trained on their internal business data securely on their on-prem installations, resulting in cloud cost reduction and better ROI,” he said.
During VB Transform, ClearML unveiled a new Enterprise Cost Management Center. This center enables enterprise customers to manage, predict and reduce rising cloud costs efficiently.
Moreover, the company plans to release a calculator to help enterprises comprehend and predict their total cost of ownership and the hidden enterprise costs of gen AI. ClearML said this tool will provide valuable insights for better cost management and informed decision-making.
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