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Global research from Altair shows significant opportunities in improving efficiency, scaling, and success in AI and IT projects

Global research from Altair shows significant opportunities in improving efficiency, scaling, and success in AI and IT projects

Troy, Michigan, June 7, 2023eagle (NASDAQ: ALTR), the world leader in computational science and artificial intelligence (AI), now releases the results of an international survey demonstrating the high level of global adoption and implementation of data and artificial intelligence strategies. The survey also reveals three main types of friction that have a negative impact on a project’s chance of success; Organizational, technical and financial friction.

“Today’s organizations recognize the need to use data as a strategic asset to create competitive advantage,” said James R. Scapa, Founder and CEO of Altair. “But it is also clear that friction occurs between people, technology, and finance, preventing organizations from accessing the data-driven insights needed to drive results. To achieve what we call “frictionless AI,” companies must start using self-service data analytics tools so that Users, regardless of technical aptitude, can operate easily and cost-effectively in complex systems thus bypassing various kinds of friction that prevent them from advancing their work.”

It is an independent survey of more than 2,000 professionals across multiple industries in ten countries that shows a high rate of failed projects in AI and data analytics (between 36% and 56%) when friction between organizational departments can be identified.

The three main factors that create friction

The survey identified organizational, technical and financial friction as the main influencing factors working against success in AI and data analytics projects.

organizational friction

The survey shows that organizations find it difficult to find the right IT skills, which is an important cause of contention.

  • 75% of respondents find it difficult to recruit people with sufficiently high technical competence.
  • 35% say the majority of their workforce has a low understanding of how AI works.
  • 58% say the skills gap and time taken to retrain the existing workforce is the most common challenge to being able to implement an AI strategy.
  • Respondents describe the biggest challenges as speed limitations in processing data, the ability to make quick data-driven decisions, and lack of data quality.
  • Nearly two in three respondents (63%) said their organization tends to make working with AI-powered software more complex than it needs to be.
  • 33% cited the inability of legacy systems to develop advanced AI and ML initiatives as a recurring technical challenge that creates friction.
  • 25% of respondents cited financial constraints as a source of contention, which in turn negatively impacts AI initiatives within the organization.
  • 28% say management places too much emphasis on upfront costs to understand how investments in AI and machine learning can benefit their organisation.
  • 33% answered that the “high cost of implementation” – whether real or perceived – is one of the disadvantages of the organization when relying on AI tools to complete projects.
  • One in four answered that more than 50% of projects failed.
  • 42% of respondents admitted that they had experienced failure of AI initiatives in the past two years. Of them, the average percentage of failed projects within an organization is 36%.
  • Despite the failure of AI projects, organizations continue to use AI because they believe in the potential to increase their capabilities and service capabilities in the long term (78%) and that minor successes nonetheless show the potential for positive long-term progress. (54%).
  • 33% of respondents say that in the past two years, more than half of their data-driven projects have not reached production.
  • Additionally, 55% stated that over the past two years, no more than a third of their data-driven projects had reached production.
  • As many as 67% responded that more than a quarter of projects never go into production.
  • Respondents in the Asia Pacific (APAC) and Europe and the Middle East (EMEA) regions experienced more failed AI projects in the past two years (54% and 35%) than the AMER region (29%).
  • 65% of respondents in Asia Pacific and 61% of respondents in EMEA agree that their organization makes working with AI tools unnecessarily complicated.
  • 78% of respondents in the Asia-Pacific region and 75% of respondents in EMEA experience difficulties hiring people with sufficient skills in data science.
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technical friction

More than half of the survey respondents say that the organization often faces technical limitations, which slows down work on various initiatives in the field of artificial intelligence and data analytics.

economic friction

Even as organizations want to scale their data and AI strategies, teams and individuals continue to face financial hurdles.

Optimism prevails despite the failure of many projects

Organizations in various industries and countries continue to use AI, despite the failure of many projects.

Many organizations also struggle with data-driven projects.

friction around the world

The survey also shows global trends where technology and skills are sore points for companies to successfully implement organizational data and AI strategies.

What is frictionless AI?

When organizations experience Frictionless AI, data analysis becomes an easy and natural part of the organization, with fast, scalable projects that can be reused. There is no technical friction between the organization and its data, no organizational friction between IT experts and domain experts, no workflow friction between designing IT applications and performing production to make effective decisions, and no migration friction when infrastructure or tools change.

This global survey was conducted by Altair and conducted by Atomik Research from March 14-31, 2023. Respondents included 2,037 professionals from several selected industries with job positions related to data and data analytics. The selection consisted of participants from ten countries from around the world, including the United States, China, France, Germany, India, Italy, Japan, South Korea, Spain and the United Kingdom.

To read the “Frictionless AI Global Survey Report” and to learn more about Altair’s frictionless AI solutions, visit

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