Altair Global Survey Reveals Significant Opportunities to Improve Efficiency, Scale, and Success of Enterprise AI and Data Projects - Seite 2

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

• 25% of respondents cited financial constraints as a point of friction that negatively affects AI initiatives within their organization
• 28% said leadership is too focused on the strategies' upfront costs to understand how investing in AI and machine learning would benefit their organization
• 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

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 50% of their projects fail
• 42% of 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%) and its minor successes have shown potential for long-term breakthroughs (54%)

Many organizations struggle to complete their data science projects as well.

• 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

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.

• Respondents in the Asia-Pacific (APAC) and Europe-Middle East (EMEA) regions reported experiencing more AI failure in the last two years (54% and 35%) compared to the North-South America (AMER) region (29%)
• 65% of APAC respondents and 61% of EMEA respondents agreed their organization makes working with AI tools more complicated than needed
• 78% of APAC respondents and 75% of EMEA respondents said they struggle to find enough data science talent

What is Frictionless AI?