Selected CIO Strategies for AI Business Success


IT executives often find themselves in a balancing act to achieve two things for business leaders — quick IT project success, perhaps a proof-of-concept project, and then also preparing the larger organization for the long-term potential and value of an emerging technology.

Artificial intelligence and all the technologies that go with it fall into this category. Your IT organization may be at the start of the journey with some initial projects. Or you may be working to integrate the value of AI more deeply into your organization’s technology and process infrastructure. Yet you also know that the biggest value will come from long-term investments in this emerging field.

But just because there’s value here, doesn’t mean your organization’s top executives have realistic expectations of when that value will be delivered. That’s according to Gartner distinguished VP analyst Janelle Hill, who provided practical advice for CIOs and other IT executives who may need to reset executive expectations at the same time they lay the groundwork for a successful short-term and long-term AI program in the enterprise. Hill provided these perspectives to IT leaders attending the Gartner Symposium/ITExpo in Orlando recently.

Some AI technologies are closer to offering transformational business value than others are. For instance, Hill said that speech recognition and automation software are among the technologies that will reach mainstream adoption in the next two years. You should probably be working on them already.

In the next 2 to 5 years the following technologies are among those headed for mainstream adoption: chatbots, deep neural nets, intelligent applications, machine learning, virtual assistants, AI developer toolkits, commercial drones, and natural language generation.

“If you aren’t doing some kind of agent-based technology now, you are probably behind the curve,” said Hill. Agent growth will taper off very fast in 2019 and deep neural networks will surpass agents in terms of business value very soon, she said.

In other words, the time to be investing in deep neural networks is now, according to Hill.

“Those of you who can climb the learning curve quicker and invest in things like DNN sooner, you will get that higher business value sooner than others,” she said. “For those who wait, the growth in business value slows down.”

Hill breaks AI down into the following three big categories:

  • Rethinking your business model and potentially adding an AI product to what your company already offers,
  • Enhancing, improving, and customizing your customer experience, and
  • Transforming decision making by driving intelligence into your processes.

“The first step is to actually take a step back and self-assess,” Hill said. “Where are you on the maturity journey for AI?”

Hill said that 75% of organizations that Gartner surveys fit into level 1. They are still learning and exploring. They have some very early and speculative use cases around AI. A growing number, 20% to 30% are at level 2, active participation in AI, where they have already deployed some successful AI proof-of-concept projects.

If you are looking to jump start your effort and perhaps get ahead of your competitors in terms of AI maturity, Hill suggests taking a use case from a different industry and applying it in your industry.

“So as an example, machine vision and collaborative robots have been used a lot in the manufacturing industry,” Hill said. “One of the things that is really good about robots is they never get sick, they don’t need to be fed, they don’t need to sleep, they are not susceptible to germs, they don’t mind doing dirty work.”

What’s another industry where this could be applied? Hill suggested, as an example, the restaurant and hospitality industry.

“You could use robots to wash dishes,” she said. “If you’re in the restaurant business or the hospitality industry, could you use robots there?”

Read the source article in Information Week.