The Great AI Adoption Race

Adapt or Risk Falling Behind

When it comes to technology, there’s undoubtedly an ongoing race to be the first with the best, most innovative advancements, and AI is no exception. With a global shift to employees working from home during the pandemic, there was a need to expedite AI-powered projects to ease the transition and establish a new sense of normal in a remote world. This urgency triggered the great AI race.

Fast forward two years later, and companies are slowing down to reflect on progress made and focus on identifying optimizations and improvements Business leaders and technologists feel they are now in a place where they can spend more time planning the next stages of the AI projects.

In our 2022 State of AI and Machine Learning report, our fourth key takeaway is adoption.

Current Adoption Trends

With businesses spending more time planning their next decision they are focusing less on early stages of the AI Lifecycle, such as data collection (sourcing) and annotation (preparation). This can correlate to less money actively being spent. At first glance, this can seem like a negative thing, when in actuality, it’s the opposite. We are seeing budget increases for larger sized companies as they are looking to partner with external data partners to meet their AI needs.

Another change in perspective, clearly represented within the responses in our report, is the perception of where a business stands against their competition in the AI race. Organizations no longer feel they’re falling behind, whether they perceive themselves ahead or in line with others varies per country. In the Americas, companies are more likely to say that they are ahead of others when it comes to AI Adoption versus similar organizations in Europe (55% vs 44%).

In conjunction with businesses stating they are no longer behind their competition, AI rollout is shifting focus as well, particularly with ROI factored in. While there is a decrease in multiple product line rollout of 51% down to 42%, there is an increase in single product line rollout up 7% from last year. This shows that initial mass rollout is slowing down, as businesses are opting to specialize in a few designated areas, and place focus on single product lines as 52.2% of deployed projects show meaningful ROI.

Future Expectations for AI Adoption

While overall budgets for AI projects have decreased due to companies spending additional time planning their next steps in AI, the impact has largely been seen with small companies who have less than 1,000 employees. These companies likely have fewer AI projects overall they need implemented and are now moving towards a maintenance phase. As mentioned earlier, we are seeing budget increases for larger sized companies which indicate they have the capacity to continue partnering with external data providers to achieve their machine learning goals.

The decreases for the smaller companies are minimal and the precise data is provided in our 2022 State of AI and Machine Learning report. With these budget increases for larger companies, we also anticipate growth in the AI adoption stage in the coming years.

We anticipate seeing companies continue to focus on single product line rollout as our report indicates initial mass adoption is on the decline from the original pandemic surge. This is particularly due to companies seeing early promise of increased ROI when just focusing on a few specific products / lines to rollout each year, which in turn could potentially lead to increased budgets for future AI projects. 

Learn More about Adoption

 AI adoption and focus is critical to AI model success. Industry experts share their thoughts in our 8th annual State of AI and Machine Learning Report that you can read today to better understand the current industry trends and challenges in relation to adoption, as well as read our other four key takeaways. For further information, watch our webinar, where we discussed in-depth all topics covered in our State of AI Report.



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