Leaders Implementing AI Applications Must Address the Elephant in the Room.

Artificial intelligence (AI) abilities, from Machine Learning (ML) and Deep Learning (DL) to Natural Language Processing (NLP) and Computer Vision (CV), are quickly developing. “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 innovative technology research at advisory firm ISG. Generally, IT leaders are frequently exploring AI applications. However, AI-enabled initiatives do not significantly lend themselves to conventional IT approaches.

Leaders must address the elephant in the house. To do that, they must know AI in understandable depth to know its realistic and logical adoption. Also, they need to understand what is doable as of today versus 3-5 years from now. There is always a risk of them either overestimating or underestimating AI’s impact on business and IT. Besides, the business appetite for AI-driven transformation is at an all-time high. It’s more important than ever that CIOs differentiate between authentic versus vendor-driven AI marketing to make their business's best choices.

Artificial Intelligence: Seven Critical Mysteries 

Leaders implementing AI Applications and, in this process, hiring AI-savvy IT specialists to further their digital transformation efforts. But those team members depend on their IT leaders to understand enough about AI to best support and sustain their efforts. To that end, here are seven critical mysteries leaders should know about Artificial Intelligence.

1. Artificial Intelligence Is Not All Alone

Artificial Intelligence is a combination of technologies answering specific problems. The catch-all term of Artificial Intelligence is so general that it is almost meaningless. In the most simplistic terms, AI is equipped to provide a data-based answer or provide 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 your network's failure. CIOs must understand the strands of AI that apply 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.

2. Artificial Intelligence Is Not All Authoritative.

There is unquestionably a wide variety of people's expectations of AI, from genuine to off-the-wall. CIOs should have at least an adequate understanding of AI's limitations such that they can predicate their expectations and properly evaluate AI solutions about to be considered. For example, machine learning 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 understand that cats meow or eat kibble.

3. The Actual Costs and ROI of Implementing AI requires Patience.

The ROI on AI requires more patience than your average IT initiative. In an Everest Group survey of more than 200 global IT leaders, 84 percent cited "long wait" to return as a challenge. CIOs must realize the reasons behind these long waits rather than getting flustered and disappointed with these. Ideally, ROI is well on its way when the AI/ML reduces time to diagnosis, saves working hours, and, hopefully, reduces margins for error. By investing in employees as part of their AI initiatives, companies can retain employees and build human workforce skills and capabilities. Companies should include this cost as positive returns on investment in ROI formulas but often aren't.

4. Underestimating the Data's Role in Decision Making.

We all know and do say that data is the fuel for AI. Thus data teams should be involved in developing an AI strategy right from the start. Expectations typically outpace the available training data. One can create a machine-learning algorithm to detect cats in images with millions of pictures of cats. Still, today it isn't easy to develop an algorithm to see one specific cat with only one photo of that cat.

It is crucial to understand the amount of data crunching needed to create an intelligent system. Therefore, CIOs must know whether their business has data and capability to build or use an AI system. Always ask where the training data will come from and how an algorithm is evaluated. That gets at "whether the algorithm has proven on real-world data that it hasn't seen before. In some cases, there may not be sufficient data governance in place. Although most organizations claim data is essential, few invest as if that is the case. 

Companies other departments have much larger teams than the data practice. Here, CIOs need to call on what skills they need to invest, given their spending appetite as some data skills may not be affordable.

5. Don't Underestimate the Data Scientists.

"There is often a debate of where Data Science or AI Centers of Excellence belong," says Dan Simion, VP - 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. It's time to make a difference and ensure that you are not downplaying data scientists' role. If used correctly – they can do more than descriptive data visualizations and solve business problems by leveraging AI and machine learning technologies. If you are looking to unbar the full potential of AI programs, you must recognize data scientists' knowledge and skills and give them opportunities to maximize the value they can drive. 

6. Cross-functional Teams are Mandatory.

Unlike traditional IT projects, AI initiatives require collaboration across data analytics, infrastructure, applications, data management, and the business. AI has the most significant impact when it’s developed by cross-functional teams with a mix of skills and perspectives. Having a business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues. Diverse teams can also think through the operational changes new applications may require—they’re likelier to recognize. 

Say that an overhaul of maintenance workflows should accompany the introduction of an algorithm that predicts maintenance needs; when development teams involve end-users in the design of applications, the chances of adoption increase dramatically. The need of the hour is to have the vision for creating such pod-based cross-functional teams that are jointly held accountable for the outcome and not for their pieces.

7. AI Infrastructure doesn’t have to be Complicated or Overwhelming.

“The complexity of data required for AI projects does not necessarily mean unreasonable complexity for AI infrastructure,” said Kurt Kuckein, vice president of marketing at DDN. “By selecting the right storage, that is complementary to and optimized for AI computational demands, and the business can achieve scalable and successful AI with relative simplicity.”

One of the most prominent mistakes CIOs make to view AI as a plug-and-play technology with immediate returns. Deciding to get a few projects up and running, they begin investing millions in data infrastructure, AI software tools, data expertise, and model development. Some of the pilots manage to eke out small gains in pockets of businesses. But then months or years pass without bringing the notable wins executives expected. Firms struggle to move from the pilots to companywide programs—and from a focus on discrete business problems, such as improved customer segmentation, to significant business challenges, like optimizing the entire customer journey.

Leaders also often think too narrowly about AI requirements. While cutting-edge technology and talent are certainly needed, it’s equally important to align a company’s culture, structure, and ways of working to support broad AI adoption. But at most businesses that aren’t born-digital, traditional mindsets and ways of working run counter to those needed for AI.

Conclusion

Companies that excel at implementing AI throughout the organization will find themselves at a significant advantage in a world where humans and machines working together outperform either humans or machines working independently. The speed of innovation picks up as the rest of the organization adopts the test-and-learn approaches that successfully propelled the pilots—the ways AI can be used to augment decision making to keep expanding. New applications will create fundamental and sometimes difficult changes in workflows, roles, and culture, which leaders will need to shepherd their organizations through carefully.