CIOs and IT leaders need to know AI in reasonable depth to understand its pragmatic adoption. Otherwise, you may either overestimate or underestimate AI’s impact.
Artificial intelligence (AI) capabilities, from machine learning and deep learning to natural language processing (NLP) and computer vision, are rapidly advancing. “Technology has never moved at such pace, meaning the role of the CIO is harder than ever to stay current and up to date with technology overall, so understanding the vast array of AI capabilities is a stretch for most CIOs right now,” says Wayne Butterfield, director of cognitive automation and innovation technology research at advisory firm ISG.
Naturally, IT leaders are increasingly exploring AI applications in the enterprise. However, AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches.
“It is imperative for CIOs to know AI in reasonable depth to understand its realistic and pragmatic adoption,” explains Yugal Joshi, vice president of digital, cloud, and application services research for Everest Group. “They need to understand what is doable as of today versus 3-5 years from now. Otherwise, there is a risk of them to either overestimate or underestimate AI’s impact on business as well as IT.”
In addition, the business appetite for AI-driven transformation is at an all-time high, even as AI-washing by technology vendors continues to be a very real phenomenon. It’s more important than ever that CIOs “be able to differentiate between what is real versus what is vendor-driven AI marketing to make the best decisions for their business,” Joshi says.
Artificial Intelligence: 9 realities to know
CIOs are increasingly hiring AI-savvy IT pros to further their digital transformation efforts. But those team members are depending on their IT leaders to understand enough about AI to best support and sustain their efforts. To that end, here are nine things CIOs should understand about AI.
1. AI is not one thing
“In actual fact, it’s a group of technologies used to solve specific problems,” says Butterfield. “The catch-all term of Artificial Intelligence is so generic that it is almost meaningless.” In the most simplistic terms, AI is usually geared around providing a data-based answer or providing a data-fueled prediction. Then things begin to diverge.
NLP may be used to automate incoming emails, machine vision to gauge quality on the product line, or advanced analytics to predict a failure of your network. (For more on the various flavors of AI, read 5 AI types, defined.) “CIOs need to at least understand the strands of AI that are relevant to their business and ensure that they have a basic understanding of the problems that AI can solve for their business, and those it will not,” Butterfield says.
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2. AI is not all-powerful
“There is certainly a wide variety of people’s expectations of AI, from realistic to off-the-wall,” says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems in the College of Computing at Michigan Technological University and director of the Institute of Computing and Cybersystems. “CIOs should have at least a decent understanding of the limitations of AI such that they can predicate their expectations and properly evaluate AI solutions they are considering.”
Machine learning, for example, can produce implicit models of very complex processes from representative data or experience. So an ML algorithm can learn to recognize cats by looking at millions of pictures of cats and “not-cats,” but it will not learn that cats meow or eat kibble.
3. AI-enabled projects require ROI patience
The ROI on AI requires more patience than your average IT initiative. An Everest Group survey of more than 200 global IT leaders 84 percent cited “long wait” to return as a challenge. “CIOs need to realize the reasons behind these long waits rather than getting flustered and disappointed with these,” Joshi says.
4. Many people underestimate data’s role
Data is the fuel for AI. Thus any data teams should be involved in developing AI strategy from the start. “Expectations typically outpace the available training data,” says Havens. One can develop a machine learning algorithm to detect cats in images with millions of images of cats, but today it is very difficult to develop an algorithm to detect one specific cat with only one photo of that cat.
“CIOs need to understand the amount of data crunching needed to create an intelligent system,” says Joshi. “Therefore, CIOs need to decide whether the business has data and capability to build or use an AI system.”
Havens advises CIOs to always ask where the training data will come from and how an algorithm is evaluated. That gets at “whether this algorithm has been proven on real-world data that it hasn’t seen before,” Havens says.
In some cases, there may not be sufficient data governance in place. Although most organizations claim data is important, few invest as if that is the case. “Their other enterprise functions such as HR and Finance have much larger teams than their data practice,” says Joshi. “CIOs need to understand what skills they need to invest given their spend appetite as some data skills may not be affordable for enterprises.”
5. Don’t underestimate the data scientists, either
There is often a debate of where data science or AI Centers of Excellence belong, says Dan Simion, vice president of AI & Analytics with Capgemini North America. Some CIOs believe data scientists should sit within IT, while others may suggest data scientists be embedded within the business. “CIOs must ensure that they are not downplaying the role of data scientists,” says Simion, noting that – when used properly – they can do more than descriptive data visualizations but also solve business problems by leveraging AI and machine learning technologies.
“CIOs who want to unlock the full potential of their AI programs should realize the knowledge and skills of their data scientists and give them opportunities to maximize the value they can drive,” Simion says.
6. AI requires massive scale and infrastructure
Thus, the operations team becomes extremely critical to the success or failure of intelligent capabilities. In fact, 61 percent of enterprises said their operations team are leaders in the charge of AI adoption in their organization, according to Everest Group research. […]