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Altair Global Survey Reveals Significant Opportunities to Improve Efficiency, Scale, and Success of Enterprise AI and Data Projects

Survey identifies opportunities to remove organizational, technological, and financial friction.

Note: TDWI’s editors carefully choose press releases related to the data and analytics industry. We have edited and/or condensed this release to highlight key information but make no claims as to its accuracy.

Altair, a specialist in computational science and artificial intelligence (AI), released results from an international survey that 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. “Friction points clearly exist around people, technology, and investment preventing organizations from gaining the data-driven insights needed to deliver results.” 

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. 

Project Failure is Common, but Optimism Reigns

Organizations across industries and geographic regions using AI persist despite high project failure rates. 

  • One in four respondents said more than half of their projects fail.
  • Four in ten (42%) respondents admit they experienced AI failure within the past two years; among those respondents, the average failure rate was 36% at their organization.
  • Despite experiencing AI project failures, organizations continue to use AI because they believe there is still an opportunity to level up capabilities or services in the long run (78%) or because its minor successes have shown potential for long-term breakthroughs (54%). Many organizations struggle to complete their data science projects as well. 
  • One-third (33%) of respondents said more than half of their data science projects never made it to production in the last two years.
  • Moreover, 55% said more than a third of their data science projects never made it to production within the past two years.
  • A staggering 67% said more than a quarter of projects never made it to production.

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. 

  • Three-quarters (75%) of respondents say they struggle to find enough data science talent
  • Over one-third (35%) say AI literacy is low among the majority of their workforce
  • Nearly six in ten respondents (58%) say the shortage of talent and the time it takes to upskill current employees is the most prevalent problem in their AI strategy adoption

Technological Friction 

More than half of respondents report that their organization often faces technical limitations that are slowing down data and AI initiatives. 

  • Overall, respondents struggle most with data processing speed, along with making informed decisions quickly and experiencing data quality issues
  • Almost two-thirds of respondents (63%) said their organization tends to make working with AI-driven data tools more complicated than it needs to be
  • A third (33%) cited legacy systems’ inability to develop advanced AI and machine learning initiatives as a recurring technology-related issue that causes friction

Financial Friction

Despite organizations’ desire to scale their data and AI strategies, teams and individuals keep hitting financial obstacles.

  • One-quarter (25%) cited financial constraints as a point of friction that negatively affects AI initiatives within their organization
  • More than a quarter (28%) said leadership is too focused on the strategies’ upfront costs to understand how investing in AI and machine learning would benefit their organization
  • A third (33%) said the “high cost of implementation” -- whether real or perceived -- is one of their organization’s shortfalls when relying on AI tools to complete projects

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: 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, visit (Short registration is required for access.)

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