Hello. This is Stanton Jones and Alex Bakker with what’s important in the IT and business services industry this week.
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What You Need to Know
Enterprises that have one or more of their top AI uses cases in production say that using high-quality, business-contextual data is the most important lesson learned.
Data Watch
Background
Our most recent Market Lens Buyer Behavior Study focused on enterprise AI adoption through the lens of use cases – specifically, the topmost-funded use cases. We asked enterprises what they learned.
And as you can see in this week’s Data Watch, lessons learned differ depending on where enterprises are in the deployment cycle. For those still moving toward production, the biggest lesson learned was the importance of defining clear goals and KPIs. For those that have already reached production, ensuring data quality and governance was most often cited as the biggest takeaway.
The Details
- 28% of those that have not yet reached production indicated that defining clear goals and KPIs was a key lesson learned. For those that have already reached production, the number fell to 18%.
- 25% of respondents that already reached production indicated that ensuring data quality and governance was a key lesson learned. For those that had not yet reached production, it was 23%.
What It Means
We take two things away from these results. First, prior to getting use cases into production, there is a lot of focus on measuring business impact. Enterprises are finding this hard, which is not a surprise. It is always hard to measure the potential impact of an emerging technology.
What is making this even harder is the fact that production use cases are – on the whole – overperforming on metrics not associated with the corporate P&L and underperforming on ones associated with it. Meaning enterprises are seeing more value in areas like quality and experience than they are with more quantifiable metrics like cost and/or people reduction.
The second takeaway here is the importance of high-quality data. This correlates with other research we’ve done on this topic over the past year. For example, when we studied the emerging concept of an enterprise data tower, we found that data usability for AI applications was – by far –the biggest data challenge organizations were facing. We also saw this in our GCC study earlier this year, which showed data quality as the top challenge for enterprises adopting GenAI.
It’s no surprise that data quality and strong data governance are increasingly being flagged as critical lessons learned. They are essential to not only getting AI use cases to production but also to realizing both expected and unexpected business value from them.
About the authors
Stanton Jones
Stanton helps enterprise technology leaders, IT service providers and buy- and sell-side professionals make sense of the global IT services sector. Stanton's weekly briefing - the Index Insider - is read by thousands of industry stakeholders each week.
