In this series’ earlier three parts covering the three types of friction – organizational, technological, and financial – we outlined how and why today’s organizational data and artificial intelligence (AI) strategies run into difficulties. But just how prevalent are these strategies around the world today? How are organizations using data analytics and AI technologies, and how do they feel about them? In this article, we’ll dive into the data to find out.
The Landscape of Organizational Data and AI Strategy Adoption
Although data science and AI can seem like a daunting topic for many, according to the more than 2,000 respondents in our 2023 Frictionless AI Global Survey, people feel comfortable with data and AI strategies. In fact, many respondents thought their organization was ahead of the curve in terms of data and AI strategy adoption. 84% of respondents felt their organization was either “ahead of the curve” or a “leader” in data and AI strategy adoption. Just 12% felt their organization was behind the curve. Keep in mind that this is a subjective reflection of how organizations perceive their capabilities and status – not an objective reflection of these sentiments’ validity.
Moreover, a majority of respondents (52%) indicated their organization is looking to scale its existing data and AI strategy. In addition, 44% said their company is looking to make a financial/resource investment, and 42% said their organization is trying to establish a data and AI strategy. Just 11% of respondents said their organization feels it doesn’t need an organizational data and AI strategy at the moment.
Regarding AI implementation, respondents felt that company-wide adoption was mostly a matter of months rather than years. The data shows that 59% of respondents believe their organizations will begin adopting AI within 12 months or sooner for large-scale projects; 36% believe adoption will occur within six months or sooner. 9% said their organization already uses AI for large-scale projects. Only 8% felt their organization will begin adopting AI for large-scale projects in two years or more.
In the graph below, you can see where and how organizations are using their data and AI strategies. For this question, respondents could select more than one answer. The data revealed that respondents are most likely to use their data and AI strategies for quality assurance, improving customer experiences, and predictive maintenance.
Lastly, it’s crucial to note that while friction is prevalent, the data showed that organizations are generally confident in their ability to leverage data to drive valuable insights and results. 78% of respondents indicated they feel their organization is able to use existing data to improve business performance.
The data regarding the landscape of organizational data and AI strategies reveals that many organizations are comfortable with the initiatives, and that a majority expects to adopt these strategies within 12 months or sooner for large-scale projects. In the same vain, a majority of respondents indicated their organization was looking to scale its existing data and AI strategy. And at the same time, very few respondents said they felt their organization didn’t have a need for broad data and AI strategies at this time – suggesting the importance of organizational data and AI strategies in today’s industries is widely recognized.
Click here to read the next part in this series, "Frictionless AI Organizational Role Breakdown: Examining Data and AI Friction Part Five."
And to learn more, 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
- 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