Altair Global Survey Reveals Significant Opportunities to Improve Efficiency, Scale, and Success of Enterprise AI and Data Projects
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Survey identifies opportunities to remove organizational, technological, and financial friction
TROY, Mich., June 6, 2023 /PRNewswire/ -- Altair (NASDAQ: ALTR), a global leader in computational science and artificial intelligence (AI), released results from an international survey which revealed high rates of adoption and implementation of organizational data and AI strategies globally. The survey also revealed that project successes suffer due to three main types of friction: organizational, technological, and financial.
"Organizations today recognize the imperative of using their data as a strategic asset to create competitive advantages," said James R. Scapa, founder and chief executive officer, Altair. "But friction points clearly exist around people, technology, and investment preventing organizations from gaining the data-driven insights needed to deliver results. To achieve what we call 'Frictionless AI,' businesses must make the shift to self-service data analytics tools that empower non-technical users to work easily and cost-effectively across complex technology systems and avoid the friction inhibiting them from moving forward."
The independent survey of more than 2,000 professionals in 10 countries and multiple industries showed a high failure rate of AI and data analytics projects (between 36% and 56%) where friction between organizational departments exists.
The Three Main Areas of Friction
Overall, the survey identified organizational, technological, and financial friction as the main culprits hindering data and AI project success.
Organizational Friction
The survey found organizations are struggling to fill data science roles, which is a significant cause of friction.
Technological Friction
More than half of respondents say their organization often faces technical limitations that are slowing down data and AI initiatives.
Financial friction
Despite organizations' desire to scale their data and AI strategies, teams and individuals keep hitting financial obstacles.
Project Failure is Common, but Optimism Reigns
Organizations across industries and geographic regions using AI persist despite high project failure rates.
Many organizations struggle to complete their data science projects as well.
Friction Exists Around the World
Globally, the survey revealed that both technology and talent are pain points for organizations when deploying organizational data and AI strategies.
What is Frictionless AI?
When organizations achieve "Frictionless AI," data analytics becomes an easy, natural part of their business with projects that are quick, repeatable, and scalable. There is no technical friction between them and their data; no organizational friction between data experts and domain experts; no workflow friction between data application design and production deployment for effective decision making; and no migration friction when infrastructure or tools change.
The global survey was commissioned by Altair and conducted by Atomik Research between March 14-31, 2023. 2,037 professionals responded across several target industries with job functions related to data and data analytics. The sample consisted of participants from 10 different countries across the globe, including the United States, China, France, Germany, India, Italy, Japan, South Korea, Spain, and the United Kingdom.
To read the full Frictionless AI Global Survey Report and to learn more about Altair's frictionless AI solutions, visit https://altair.com/frictionless-ai.
About Altair
Altair is a global leader in computational science and artificial intelligence (AI) that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair enables organizations across all industries to compete more effectively and drive smarter decisions in an increasingly connected world – all while creating a greener, more sustainable future. For more information, visit https://www.altair.com/.
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