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We released recently the results of a survey conducted to determine the state of AI. The study revealed a surprising level of momentum behind AI adoption: Not only was AI use pervasive, but respondents supported their AI efforts with increasingly mature processes and infrastructure.
Things are unfolding more quickly than many may have predicted — if business and technology leaders were previously taking a cautious, wait-and-see approach, today they’re all in. Just ask Lisa Spelman, VP of Intel’s Data Center Group.
From Intel’s vantage point, “AI is the fastest-growing workload within the data center,” Lisa told me. “If you add in the edge, it only gets bigger. This is only going to continue because AI is basically being built into all applications these days.”
This is great news. AI technology has made huge strides in a short amount of time and is ready for broader adoption. But as companies accelerate their AI efforts, they need to take extra care because even small potholes can cause problems for those traveling at high speeds.
For example, in AI, what appears to be a minor process or data issue can quickly escalate into an issue with wider repercussions. This isn’t a reason to slow down with AI. Just think of it as being more “road aware.”
Fortunately, our previous experience can serve as a beacon for your own AI efforts. Among the customers with whom we’ve worked closely, here are the most frequent issues.
“As companies accelerate their AI efforts, they need to take extra care because even small potholes can cause problems for those traveling at high speeds.”
When I asked Peter Guerra, Accenture’s chief data scientist for North America, whether he’s seen the same level of heightened activity on AI among Accenture’s clients, he agreed. He went on to say that governance, while not a new issue, has emerged as a primary focal point for the companies they’re working with as they take their AI initiatives to the next level.
“We’ve seen clients adopting more AI models across their organizations,” Peter says. “So, governance, model operations and the whole issue of scale all loom large over that expansion. As a result, we’re seeing technology adoption in closely related areas such as cloud data and model management tools.”
Legacy analytics capabilities figure heavily in the AI governance conversation, given the numerous areas of overlap between AI and analytics. The companies that are aggressively expanding their AI capabilities are generally the types of organizations that have lots of legacy analytics capabilities in place. And those capabilities must be modernized and brought into the AI era.
That means reviewing every analytics model and determining whether its underlying assumptions are still true. For companies that have been successfully using the same models for years, this may seem like overkill. But AI introduces new data infrastructures, techniques and data types — not to mention new desired outcomes. Your analytics capabilities need to operate within the same governance structure to ensure that the future growth of these systems unfolds smoothly.
“AI introduces new data infrastructures, techniques and data types — not to mention new desired outcomes.”
Technology leaders often report that due to the enormous amount of hype surrounding AI, their business counterparts frequently seem to want to adopt AI for AI’s sake.
These conversations often start with “how can we be using AI?” rather than “I have an untapped business opportunity or stubborn problem.”
They’re seeing AI work elsewhere, and they want that, too. Reframing these conversations in the context of broader business or technology transformation goals can help. Because today it’s clear that for many of our customers, AI’s highest and best use is as a transformation accelerator.
Planning to completely overhaul your drive-through? Rewiring your customer service experience? Creating a more responsive supply chain?
These are the types of transformation challenges where we have witnessed a growing, heavy reliance on AI to speed up and improve the process. When it’s time to have the AI conversation, transformation may be the best starting point.
“For many business leaders, the emergence of AI is like an unwanted, surprise visit from the doctor.”
For many business leaders, the emergence of AI is like an unwanted, surprise visit from the doctor.
“Can we have a look at that data?” “Why does this process work in this way?” “Does this solution work with that solution?” “Have you seen how they’re doing it in this other department?”
Imagine being on the receiving end of these questions, and it’s easy to see the role that culture plays in AI adoption.
AI is powerful. It can also prove to be a real challenge to organizations accustomed to doing things a certain way.
Perhaps even more importantly, it tends to shine a bright spotlight on parts of the organization that many have been content to simply leave in the dark. By now it’s standard practice to identify cultural issues as a potential obstacle to technology adoption — or, really, any scenario in which people are required to do things differently.
But recent history has shown that if anything, culture plays an even larger role in successful AI adoption. As your organization expands its AI strategies, plan on taking extra care to manage the cultural side of the equation.
Republished with permission, this article first appeared on the SAS Insights blog.
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