59% of Organizations Lack Resources to Meet Generative AI Expectations: Study

59% of Organizations Lack Resources to Meet Generative AI Expectations: Study

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A recent study by an open source AI solutions company ClearML in collaboration with the AI Infrastructure Alliance (AIIA) shed light on the adoption of generative AI among Fortune 1000 (F-1000) companies.

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 senior executives face in harnessing the potential of AI within their organizations.

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According to the global study, 59% of C-suite executives lack the resources to meet the AI ​​generative innovation expectations set by business leadership. Budget constraints and limited resources have emerged as critical barriers to successful AI adoption in enterprises, hindering the creation of tangible value.


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The study also found that 66% of respondents are unable to fully measure the impact and return on investment (ROI) of their AI/ML projects on bottom line. This highlights the profound inability of underfunded, understaffed, and undergoverned AI, ML, and engineering teams in large enterprises to effectively quantify outcomes.

“While most respondents said they needed to scale AI, they also said they didn’t have the budget, resources, talent, time and technology to do it,” Moses Guttman, co-founder and CEO, told VentureBeat. by ClearML. “Given the force-multiplying effect of AI on revenue, new product ideas, and functional optimization, we believe critical resource allocation is now needed for companies to invest in AI to transform their organization in a way effective”.

The study also highlights the growing revenue expectations from investments in AI and ML. More than half of respondents (57%) report that their boards of directors anticipate a double-digit increase in revenue from these investments in the next fiscal year, while 37% expect single-digit growth.

The study collected responses from 1,000 C-level executives, including CDOs, CIOs, CDAOs, VPs of AI & Digital Transformation, and CTOs. According to ClearML, these executives lead the generative transformation of AI in Fortune 1000 companies and large enterprises.

The state of adoption of generative AI

According to the study, a majority of respondents believe it is critical to unleash AI and machine learning use cases to create business value. Eighty-one percent of respondents rated it as a top priority or one of their top three priorities.

Additionally, 78% of enterprises plan to adopt xGPT/LLM/Generative AI as part of their AI transformation initiatives in FY2023, with an additional 9% planning to begin adoption in 2024, bringing the total at 87%.

Respondents were also nearly unanimous (88%) about their organizations’ plans to implement specific policies for the adoption and use of generative AI in corporate business units.

However, while the adoption of generative AI and machine learning is a key driver of revenue and ingenuity within the enterprise, 59% of C-level leaders lack adequate resources to meet business expectations. business leadership on generation AI innovation.

They face budget and resource constraints that hinder adoption and value creation. Specifically, people, processes, and technology are all critical pain points identified by F-1000 and large enterprise executives when it comes to building, running, and managing AI and machine learning processes:

  • 42% indicate a critical need for talent, especially AI and machine learning experts, to drive success.
  • A further 28% cite technology as a major 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 excessive time spent on data collection, preparation and manual pipeline construction.

Additionally, 88% of respondents indicated that their organization seeks to standardize on a single AI/ML platform across departments rather than using different point solutions for different teams.

“Business decision makers are poised to increase their investments in Generative AI and ML this year, but according to our survey results, they are looking for a centralized end-to-end platform, without spreading spend across multiple point solutions.” said Guttmann of ClearML VentureBeat. “With growing interest in materializing business value from investments in AI and ML, we expect demand for greater visibility, continuous integration, and low-code to drive generative adoption of AI.”

Key challenges hindering the adoption of generative AI

The study revealed that growing concerns about the governance of AI and generative AI have led to dire financial and economic consequences.

We found that 54% percent of CDOs, CEOs, CIOs, AI leaders, and CTOs reported that their inability to govern AI/ML applications resulted in business losses, while 63% of respondents reported losses of at least $50 million due to inadequate governance of their AI/ML applications.

When asked about the top challenges and barriers to adopting generative AI/LLM/xGPT solutions within their organization and business unit, respondents identified five top challenges:

  • 64% of respondents expressed concerns about customization and flexibility, especially the ability to adjust models using their own up-to-date internal data.
  • 63% of respondents ranked data retention as a top priority, focusing on AI modeling and safeguarding corporate knowledge to maintain a competitive edge while protecting corporate intellectual property.
  • 60% of respondents highlighted governance as a significant challenge, highlighting the importance of limiting access and governing sensitive data within the organization.
  • 56% of respondents indicated that security and compliance came first, as companies rely on public APIs to access AI models and xGPT solutions, which exposes them to potential data leakage and privacy issues.
  • 53% of respondents cited performance and cost as a top challenge, primarily related to fixed GPT performance and associated costs.

According to Guttmann, the lack of visibility, measurability and predictability identified in the survey is a vexing barrier to successful adoption of new technologies. All of these factors are crucial to success.

“Enterprise customers should strive for out-of-the-box LLM performance trained on their internal enterprise data securely on their on-prem installations, resulting in lower cloud costs and better ROI,” he said.

During VB Transform, ClearML unveiled a new Enterprise Cost Management Center. This center enables enterprise customers to efficiently manage, predict and reduce the escalating costs of the cloud.

In addition, the company plans to release a calculator to help businesses understand and predict their total cost of ownership and the hidden business costs of gen AI. ClearML said this tool will provide valuable insights for better cost management and informed decision making.


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