Artificial Intelligence (AI): 9 things IT pros wish the CIO knew – The Enterprisers Project

Artificial intelligence(AI) capabilities, frommachine learninganddeep learningtonatural languageprocessing (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 firmISG.

Naturally, IT leaders are increasingly exploring AI applications in the enterprise. However, AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches.

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 forEverest 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 AIs impact on business as well as IT.

[ Do you understandthe main types of AI?Read also:5 artificial intelligence (AI) types, defined.]

In addition, the business appetite for AI-driven transformation is at an all-time high, even asAI-washing by technology vendorscontinues to be a very real phenomenon. Its 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.

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.

In actual fact, its a group of technologies used to solve specific problems, says Butterfield. The catch-all term of Artificial Intelligence is so genericthat 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, read5 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.

"There is certainly a wide variety of people's expectations of AI, from realistic to off-the-wall."

There is certainly a wide variety of peoples expectations of AI, from realistic to off-the-wall, says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems in theCollege of Computing at Michigan Technological Universityand director of theInstitute 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.

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.

In some cases, there may not be sufficient data governance in place.

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 hasnt 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.

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.

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.

The operations team becomes extremely critical to the success or failure of intelligent capabilities.

Though [an increasing number of] enterprises are leveraging cloud-based AI offerings for cloud and SaaS vendors, the operations team is critical to scale such initiatives and create the needed guardrails, Joshi says.

One of the IT leaders most important roles is understanding the technology requirements necessary to support and sustain the companys AI transformations. In order for a company to be successful along its AI journey, Simion says, the CIO needs to make sure the AI technology stack is working and in sync with the overall enterprise technology.

Unlike many historical IT projects, AI initiatives require collaboration across data analytics, infrastructure, applications, data management, and the business. CIOs need to have the vision for creating such pod-based cross-functional teams that are jointly held accountable for the outcome and not for their individual pieces, Joshi says.

Although we throw around the term intelligent, AI is not inherently adaptive. AI algorithms are only good at what they are designed for, and will often fail miserably and in strange ways when applied to problems that may seem similar to humans, but are not similar from an AI-perspective, Havens says. An algorithm that is trained to drive a car in an urban environment may and probably will fail at rural driving, for example.

Is your organization looking to increase efficiency? Improve effectiveness? Transform the customer or user experience? Create entirely new business models? The CIO must understand what value the business wants to derive from AI adoption. Everest Group notes four common business imperatives: Efficiency, Effectiveness, Experience, and Evolution. CIOs may also need to manage inflated expectations of business around AI adoption and its impact on the organization.

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Artificial Intelligence (AI): 9 things IT pros wish the CIO knew - The Enterprisers Project