In this series’ previous five parts, we’ve covered the three types of friction – organizational, technological, and financial – outlined how and why today’s organizational data and artificial intelligence (AI) strategies run into difficulties, and explored how employees of different levels view today’s data and AI problems. In this series’ final article, we’ll examine the data from the Altair 2023 Frictionless AI Global Survey Report to dive deeper into how respondents from different geographical regions and countries responded to the survey’s various questions.
Frictionless AI Regional Breakdown
To begin, let’s first examine the three major geographical regions covered in the survey: United States (AMER), Europe-Middle East (EMEA), and Asia-Pacific (APAC). Data revealed that international regions and countries vary on their use of organizational data and artificial intelligence (AI) strategies, perceive and experience friction differently, and are looking to scale their existing strategies in different ways. Overall, the data suggests that respondents from the APAC region are looking to learn more about organizational data and AI strategies, implement them, reduce friction, and more within the near future.
Respondents from the APAC region were most likely to say their organization is looking to establish data strategies, scale organizational data and AI strategies, and pilot first use cases compared to respondents in the AMER and EMEA regions. In addition, respondents from the APAC region were also the most likely to say they believe their organization will start to implement AI for large-scale projects within the next year or sooner. The data also showed that APAC respondents were the least likely to already be using AI for large-scale projects. This suggests the region’s organizations are looking to scale quickly to make up a gap in adoption and implementation.
Overall, respondents from the three major international regions all indicated that they encountered limitations that slow their AI initiatives at roughly the same rate. This result suggests that friction is a worldwide problem that affects organizations no matter where they’re located. That said, the data did suggest that respondents from the AMER region were far less likely to have experienced an AI failure within the past two years (29%) compared to the APAC (54%) and EMEA (35%) regions.
The data also suggested that respondents from the AMER region (80%) were less likely to have a structured data science enablement program in place compared to respondents from the APAC (91%) and EMEA (90%) regions.
Interestingly, respondents from the APAC and EMEA regions (both 61%) were more optimistic about scaling AI projects without training domain experts in data science than respondents from the AMER region (50%). On a related note – as covered extensively in this series – finding data science talent is a struggle for all organizations. The data suggests that this problem is more pronounced in the APAC and EMEA regions than it is in the AMER region — overall, 78% of APAC respondents and 75% of EMEA respondents said they struggle to find enough data science talent compared to just 61% of AMER respondents. That said, this is still a very acute issue, regardless of region.
Frictionless AI Country Breakdown
Now, let’s turn our attention to what the data revealed about specific countries. According to the data, respondents from China and India — two APAC nations — were the most likely to feel their organizations were AI and digital transformation leaders. On the other hand, respondents from the U.S. were most likely to feel their organization was lagging behind the competition along with France and the U.K.
Regarding when their organization plans to adopt AI for large-scale projects, three APAC countries led the way. China (88%), India (75%), and South Korea (62%) were the most likely nations to say they plan to adopt AI for large-scale projects within the next year or sooner. Respondents from Italy (22%), the U.S. (12%), and Germany (11%) were the most likely to say their organization already uses AI for large-scale projects.
Interestingly, the numbers also suggested that respondents from India experience a disproportionate amount of data and AI failures, and that they’re more likely to believe their organization makes the data and AI process more difficult than it needs to be.
When it comes to finding data science talent, below you can see how each country felt about acquiring talent, creating data science programs for existing employees, and how confident organizations are that they can scale AI initiatives without training domain experts in data science.
Lastly, the graphic below shows how each country’s respondents felt about their ability to scale AI projects without needing to train domain experts in data science.
In general the data shows that, at the regional level, APAC is going all-in on data and AI, despite failure rates. Respondents from the APAC region were most likely to say their organization is looking to establish data strategies, scale organizational data and AI strategies, and pilot first use cases compared to respondents in the AMER and EMEA regions. Respondents from the APAC region were also the most likely to say they believe their organization will start to implement AI for large-scale projects within the next year or sooner.
At the country level, respondents from China were by far the most likely group to indicate that their organizations are trying to establish a data strategy, pilot first use cases, looking to scale, and make investments in data and AI strategies. China, India, and South Korea were the most likely nations to adopt AI for large-scale projects within the next year or sooner.
We hope that this series has given you an in-depth panorama of the state of enterprise data and AI initiatives around the world. Throughout the data, it’s clear that friction in and around organizational data and AI strategies is incredibly common within organizations worldwide, regardless of industry. This friction stems from three key areas: organizational, technological, and financial. Organizations are taking steps to remedy these problems, but they’re still facing roadblocks that frustrate them and make them feel the process is more complex than it needs to be. Thankfully, Altair is here to help you overcome all your friction-related issues.
For more information, be sure to check out more of Altair’s Frictionless AI resources, including:
- Report: Altair 2023 Frictionless AI Global Survey Report
- Webpage: Frictionless AI
- Webpage: Accelerate AI Adoption
- Article: Organizational Friction - Examining Data and AI Friction Part One
- Article: Technological Friction: Examining Data and AI Friction Part Two
- Article: Financial Friction - Examining Data and AI Friction Part Three
- Article: Organizational Data and AI Strategy Adoption – Examining Data and AI Friction Part Four
- Article: Frictionless AI Organizational Role Breakdown: Examining Data and AI Friction Part Five
- Infographic: AI's Breakdown Lanes - The Three Key Areas of Friction
- Infographic: Why Are AI and Data Projects Coming Up Short?
- Infographic: Achieving Frictionless AI - When, Where, and How