New Global Survey Report Finds Majority of Enterprises Lack Critical Resources to Meet Generative AI Expectations

ClearML logo

ClearML is the leading open source, end-to-end solution for unleashing AI in the enterprise

AIIA logo

AI Infrastructure Alliance – Building the canonical stack for machine learning

Survey of leading global companies sheds light on the remarkable economic impact of GenAI and the key challenges faced by top executives

Enterprise decision-makers are poised to increase investment in Gen AI and ML this year, but they’re seeking a centralized end-to-end platform, not scattering spend across multiple point solutions.”

— Moses Guttmann, Co-founder and CEO of ClearML

SAN FRANCISCO, CALIFORNIA, UNITED STATES, August 8, 2023/ — ClearML today announced the general availability of its joint research findings from an extensive global survey recently conducted with the AI Infrastructure Alliance (AIIA). The new report, called “Enterprise Generative AI Adoption: Key Considerations, Challenges, and Strategies for Unleashing AI at Scale” 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. The survey report sheds light on the adoption, economic impact, and significant challenges top C-level executives face in unleashing Generative AI’s potential within their organizations.

Download a free copy of the global survey report here:

“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 GenAI point solutions,” said Moses Guttmann, Co-founder and CEO of ClearML. “With the growing interest in materializing business value from AI and ML investments, we expect that the demand for increased customization on the one hand and an easy self-serve solution on the other hand – together with visibility and predictable running costs – will all drive GenAI adoption at scale.”

“It’s clear that enterprises are going all in on GenAI,” said Daniel Jeffries, Managing Director of the AI Infrastructure Alliance. “They’re excited about it, and they’re building the teams to integrate it into their platforms, but there are challenges. These are non-deterministic systems, and they need new tooling to get it right. They also need to think differently about how these systems work and generate value for them.”

Key Findings from the Survey and Research Report

Enterprise Adoption by Department and Business Unit
When asked what is the expected extent of Generative AI use in core functions of their business in 2025, the majority of respondents believed the following departments will have limited, wide-scale, or critical use case adoption by 2025:

– R&D = 84%
– Sales & Customer Success = 82%
– Marketing & Communications = 82%
– IT = 82%
– HR & Operations = 81%
– Product = 79%
– Finance = 79%

Plans for Adoption
– 81% of respondents consider unleashing AI and machine learning for creating business value as a critical top priority.
– 78% of enterprises plan to adopt xGPT / LLMs / generative AI during 2023, and an additional 9% plan to start adoption in 2024.

Expectations for Revenue
– 57% of respondents’ boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year, while 37% expect a single-digit increase.

Resource Constraints & Impacts
– 59% of C-level leaders lack the necessary budget and resources for successful Generative AI adoption, hindering value creation.
– 66% of respondents face challenges in quantifying the impact and ROI of their AI/ML projects on the bottom line due to underfunding and understaffing.
– 42% need more expert machine learning personnel to ensure success.

To address these pain points, “one solution can be found in the rise of low-code solutions that require less technical and specialized talent to scale Generative AI in the enterprise,” noted Noam Harel, CMO & GM of North America, ClearML. “These solutions give enterprises the ability to do more with less and supercharge a team of specialized engineers to support a large group of knowledge workers and business users through self-serve functionality via a simplified, low-code approach.”

Enterprise Challenges & Blockers
– 64% of respondents selected the ability to customize models with fresh internal data.
– 63% prioritized preserving company knowledge, generating AI models, and protecting corporate IP.
– 60% focused on governance and restricting access to sensitive data.
– 56% emphasized security and compliance, especially with public APIs and third-party data sharing.
– 53% chose performance and cost related to fixed GPT performance and costs.

Platform Standardization
– 88% 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.

Enterprise customers should strive to get out-of-the-box LLM performance, trained on their own internal business data securely on their on-prem installations, resulting in cloud cost reduction and better ROI. That’s why earlier this year, ClearML rolled out its ClearGPT platform, the only secure, enterprise-grade generative AI platform that removes the blockers and risks of using LLMs to fuel innovation. Unlike alternate solutions, ClearGPT offers security, performance, governance, data, and flexibility so that CxOs can drive innovation, productivity, and efficiency at vast scale and develop new internal and external products faster, outmaneuver the competition, and create new AI revenue streams. To request a demo, visit

The new survey report can be downloaded here:

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. To learn more, visit the company’s website at

Noam Harel
email us here
Visit us on social media: