16 Statistics About Artificial Intelligence (AI): What’s Working And What’s Not

Investment in artificial intelligence (AI) is soaring. AI funding has multiplied in recent years, it’s becoming more central to software platforms of all kinds, and organizations are using more AI internally to drive value and gain a competitive edge.

The trend toward AI is strong and unwavering, and there are huge value-creating possibilities. However, there are still plenty of reasons to be wary of its promises.

While the speed and scale that AI offers are unparalleled, the truth is, there’s still no silver bullet to becoming a fully data-driven organization. We’ve been in the digital age for decades now, and we’ve come a long way in harnessing the power of data, but what’s needed to maintain a competitive edge keeps getting more complicated.

Today, it’s not enough to collect and organize sales data, capture web traffic, keep tabs on competitor activities, monitor your carbon footprint – the list goes on. You need to find ways to harmonize all the structured and unstructured, internal and external data sources. You need cloud architects, MLOps, AI engineers, software developers, and more, just to keep the car running. There are so many ways for your data initiatives to go south.

This is where the technocrat’s utopia meets reality. In the real world, companies need context to embed AI-driven insights into existing processes. What we’ve seen work better than pure technology is domain-specific AI solutions and AI-augmented subject matter experts.

In this blog, we look at 8 statistics that show how AI is benefitting us and 8 others that show we’re still having trouble reaching the data-driven dream.

Part 1: 8 Pro-AI Statistics

The best time to invest in AI was yesterday. The next best time is today.

  1. Corporate investment in AI grew by more than 5x from 2015 to 2020, reaching $67.9 billion in 2020. (Stanford, 2021) 
  2. Private investment in AI more than doubled from 2020 to 2021, reaching $93.5 billion in 2021. (Stanford, 2022) 
  3. The worldwide AI software market is forecasted to reach $62 billion in 2022. (Gartner, 2021) .
  4. 33% of technology providers plan to invest $1 million or more in AI within two years. (Gartner, 2021) 
  5. 79% of respondents said their organizations were exploring or piloting AI projects. (Gartner, 2020)
  6. 91.9% of firms report that the pace of investment in these projects is accelerating, and 62.0% of firms report data and AI investments of greater than $50 million. (HBR, 2021) 
  7. 86% say that AI will be a “mainstream technology” at their company in 2021. (PwC, 2021) 
  8. 52% of our survey respondents have accelerated their AI adoption plans in the wake of the COVID-19 crisis. (PwC, 2021) 
 

Part 2: 8 Cons That Show We’re Still Struggling to be Data-Driven

Companies continue to falter when it comes to operationalizing data-driven initiatives and delivering business outcomes. The success rate was low when we first started using analytics and big data, and it continues to be low, even with AI in the picture.

  1. Just 24% of respondents said that they thought their organization was data-driven this past year, a decline from 37.8% the year before. (HBR, 2021)
  2. Only 29.2% report achieving transformational business outcomes. (HBR, 2021) 
  3. Through 2022, only 20% of analytic insights will deliver business outcomes. (Gartner, 2019) 
  4. 60% of big data projects will fail to go beyond piloting and experimentation. (Gartner, 2015) 
  5. The real failure rate (and the statistic that’s more widely circulated) is “closer to 85%,” according to Gartner analyst Nick Heudecker. (former Heudecker tweet, 2017) 
  6. Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance. (Gartner, 2021) 
  7. Only 21% stated their AI initiatives were in production. (Gartner, 2020) 
  8. 76% of organizations are barely breaking even on their AI investments. (PwC, 2021) 
 

If you’re interested in how Evalueserve solves these challenges for our clients, don’t hesitate to get in touch. We’d love to learn more about your journey towards becoming more data-driven and explore how we can help.

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Susan Xie
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