New global Fortune 1000 survey illustrates tremendous economic impact and key challenges that top C-level executives face as they work to unleash the power of AI in their enterprises
SAN FRANCISCO, CA / ACCESSWIRE / July 19, 2023 / ClearML, in partnership with the AI Infrastructure Alliance (AIIA), today announced it has published "Enterprise Generative AI Adoption: C-Level Key Considerations, Challenges, and Strategies for Unleashing AI at Scale," a new survey on global Fortune 1000 enterprise adoption of Generative AI. AIIA is an organization dedicated to bringing together the essential building blocks for the Artificial Intelligence applications of today and tomorrow, and ClearML is the leading open source, end-to-end solution for unleashing AI in the enterprise.
The extensive global survey includes responses from 1,000 CDOs, CIOs, CDAOs, VPs of AI and Digital Transformation, and CTOs in charge of adopting and spearheading Generative AI transformation in Fortune 1000 and large enterprises.
Key Findings
To start, it's not surprising that the majority of survey respondents believe that unleashing AI and machine learning use cases to create business value was critical, with 81% of respondents rating it a top priority or one of their top-3 priorities. In fact, 78% of enterprises plan to adopt xGPT / LLMs / generative AI as part of their AI transformation initiatives during the fiscal year of 2023 and an additional 9% plan to start adoption in 2024.
The survey results paint a stunning picture of the mounting revenue expectations put on AI transformation leaders and their teams, with 57% of respondents reporting that their board expects a double-digit increase in revenue from AI/ML investments in the coming fiscal year and an additional 37% reporting the expectation of a single-digit increase.
However, although the survey found that while AI and ML adoption is now a key revenue and ingenuity engine within the enterprise, an astonishing 59% of C-level leaders are inadequately resourced to deliver on business leadership's expectations of Generative AI innovation. They lack the budget and resources needed to drive adoption successfully across the enterprise and create value.
The survey sheds light on the profound inability of underfunded, understaffed, and under-governed AI, ML, and engineering teams in large enterprise organizations to quantify results, with a worrying 66% of respondents saying they are unable to fully measure the impact and ROI of their AI/ML projects on the bottom line.
"With the accelerating adoption of AI and ML within the enterprise, we wanted to know its impact on enterprise leaders within the C-suite, and the answers are clear," said Moses Guttmann, Co-founder and CEO of ClearML. "While the majority of respondents said they need to scale AI, they also said they lack the budget, resources, talent, time, and technology to do so. 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 effectively invest in AI to transform their organization."
People, process, and technology are also critical pain points that F1000 executives believe stand in their way as they identified their biggest resource challenges when it comes to building, executing, and managing AI and machine learning processes within their organization:
- 42% indicated that talent was critically needed, especially more expert machine learning personnel to deliver success.
- An additional 28% flagged technology as the key barrier, indicating they don't have a single, unified software platform to manage all aspects of their organization's AI/machine learning processes.
- 22% indicated time as a key challenge, describing they spend too much time collecting and preparing data or manually building pipelines.
Respondents are nearly unanimous (88%) on their organization's plan to implement policies specific to the adoption and usage of Generative AI across the enterprise business units. The same percentage of respondents indicated their organization is seeking to standardize on a single AI/ML platform across departments versus using different point solutions for different teams.
"Enterprises are hungry to adopt the powerful new capabilities of generative AI and LLMs, but they face some major challenges in terms of compliance, competition for talent, and making these systems repeatable and reliable," said Daniel Jeffries, Managing Director of the AI Infrastructure Alliance. "It's not a question of will they overcome them but when and how fast? The race to get there first will help define the powerhouses of tomorrow."
When asked to rate what are the key challenges and blockers in adopting generative AI / LLMs / xGPT solutions across their organization and business units, respondents rated five key challenges as most important:
- 64% of respondents selected customization and flexibility as a leading challenge, voicing their concerns around the ability to customize models with their own fresh internal data.
- 63% of respondents ranked data and primarily the ability to preserve company knowledge, generate AI models, and maintain a competitive edge with corporate IP protection being top of mind.
- 60% of respondents pointed to governance and the ability to restrict access to and govern sensitive data inside the organization.
- That's important, because rising AI and Generative AI governance concerns lead to dire financial and economic consequences, with 54% of CDOs, CDAOs, CIOs, Heads of AI, and CTOs reporting that failure to govern their AI/ML applications incurred losses to the enterprise, with a staggering 63% reporting that failure to properly govern their AI/ML applications translated to losing $50 million or more.
- 56% of respondents indicated that security and compliance is top of mind. That's not surprising, given that enterprises rely on public APIs to access generative AI models and xGPT solutions, leaving them vulnerable to data leaks and privacy concerns, jeopardizing enterprise IP and knowledge ownership of highly sensitive enterprise data shared with third parties.
- 53% of respondents rated performance and cost as one of the top challenges, primarily related to fixed GPT performance and costs.
"Addressing the blockers in adopting generative AI solutions across enterprise business units is the exact reason why we designed ClearGPT for the most demanding, secure, and compliance-driven enterprise environments," Guttmann added. "We heard the same needs from our customers, and we listened to the market -- so we designed ClearGPT as low-code to allow rapid internal adoption by CxOs, business units, and knowledge workers. It's clear that organizations need a GenAI platform that empowers their existing enterprise data engineering and data science teams to fully utilize state-of-the-art LLM models agnostically -- removing blockers like vendor lock-ins, eliminating leakage of corporate knowledge and data, and giving your business a competitive advantage that fits your organization's custom needs while using your internal enterprise data and business insights securely."
To receive an advance copy of the survey report, please email [email protected].
About AIIA
The AI Infrastructure Alliance is dedicated to bringing together the essential building blocks for the Artificial Intelligence applications of today and tomorrow. The Alliance and its members bring striking clarity to this quickly developing field by highlighting the strongest platforms and showing how different components of a complete enterprise machine-learning stack can and should interoperate. They deliver essential reports and research, virtual events packed with fantastic speakers, and visual graphics that make sense of an ever-changing landscape.
About ClearML
ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as healthcare, CPG, retail, financial services, insurance, technology, adtech, and manufacturing, among others. To learn more, visit the company's website at https://clear.ml.
SOURCE: ClearML