newsroom

Gartner research states that “Most [AI] technologies are nascent at best, with only 4 percent of organizations having deployed enterprise production AI technologies by 2018.” With that said, 86 percent of businesses have taken a strategic interest in AI. Yours is probably one of them.* Why is the gap so large?

Artificial intelligence introduces new challenges to building the business case for investment that can limit adoption of the technology, no matter how dynamic the results might be. Unlike traditional IT deployments, AI projects require new skills and new methods, and have unpredictable results, all of which make them more difficult to forecast for the business.

Despite high enterprise-level interest in an AI project, defining the AI strategy is cited as a top challenge by 37 percent of the respondents in the survey conducted by the Gartner Research Circle. Inherent risks result from opaqueness in the decision-making process for AI technologies. To build a strategy, CIOs must articulate a clear business purpose and rationale for investing in AI.

But AI projects are not like other IT solutions; the return on investment is not always immediately clear. These projects also require a serious approach across the business, as they incorporate big topics such as acquiring new talent, managing cultural change and ensuring good corporate governance.

AI projects also challenge CIOs to balance the two consistently competing pressures of the IT organization:

• Providing stable, reliable and high-performance analytics services for enterprise reporting.
• Delivering innovative, flexible and quick-turnaround analytics services for augmented data discovery and process automation.

“According to the Gartner 2017 CIO survey, 43 percent of enterprises globally approach the problems they face by balancing the two above methods. However, 71 percent of top performers report that this method improves innovation.”*

As organizations consider AI projects, they must weigh existing strength in digital adoption, seriousness in investment in AI, and the strength of management’s support for the effort. Gartner recommends “being upfront about expected areas of cost but keeping in mind they might change considerably as the solution scope is explored and refined.”

And IT leaders have to have the guts to pull the plug. Gartner adds, “There also needs to be readiness to close down experimental AI projects where no clear benefit is emerging from the early stages.”

IBM is providing this critical Gartner report on the seven factors that make the business case for AI projects different. Download the report now.

* Gartner, Seven Factors That Make Business Cases for Artificial Intelligence Projects Different, 8 February 2018, Moutusi Sau, Whit Andrews, Alan D. Duncan

The post Read Gartner’s seven reasons why AI isn’t like any other project appeared first on IBM IT Infrastructure Blog.