Despite all this promise, adoption of AI is not where many expected (or hoped) that it might be. Research continues to improve the underlying AI technology--the recent of development of both Midjourney and Stable Diffusion4 is a case in point--and firms continue to invest in AI.5 We even saw a bump in investment during the first couple of years of the pandemic.6 However, a majority of AI projects fail.7 Compelling demonstrations are not transitioning into value creating solutions. Autonomous cars are a prime example, where commercial, mass market, versions constantly seem to be a decade away, despite early success and significant investment. We hear a similar story from AI practitioners working in firms attempting to leverage AI, with carefully developed models and solutions left on the bench as they are either not compelling enough or too fragile to replace existing solutions. There are notable successes, such as machine language translation, however there appears to have been more misses.

Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies. What exactly is digital transformation? You may hear the term often, but everyone seems to have a different definition. See how our experts define digitization, and how you can get started in this free guide.

Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies. Research outfit Gartner advises that CIOs proceed step-by-step through a "framework" designed to sort out the type of AI applications, AI solutions and the core AI technologies they will need to produce the kind of results the company is looking for. After CIOs nail down business objectives and use cases, they should start to think about how the technology will be used to integrate AI in the enterprise. Next, CIOs should look into the common solutions used by various enterprise applications such as a virtual customer assistant, an employee training tool or a process efficiency tool.

Any AI effort will rely on three main building blocks: data, infrastructure, and talent. The following is a guest post by Rita C. Waite, a Growth Strategy & Investments Manager at Juniper Networks. Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings. AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.

Although many core AI technologies are proven, the market for solutions using those technologies overall is in its infancy, such that CIOs should expect rapid product and solution change. Some industries have utilized AI to great success. In healthcare, for example, thanks to "computer-assisted diagnosis," a computer was able to spot 52% of breast cancers based on mammography scans up to one year before the women were officially diagnosed. But there are limits to AI solutions, especially if there isn't enough data available or if it's of poor quality.By jump starting innovation, CIOs in combination with business peers can jointly figure out how to best use AI technologies in their industry. Companies that commit to promoting experimentation across the organization can encourage their employees to interact with low cost AI products -- Alexa, Cortana, a drone, a wearable, and so on.

With all the hype (and confusion) from vendors around AI, Gartner is taking the "bull" by the horns and launching a new collection of cool vendor reports just focused on AI. We have identified the most innovative emerging vendors with "cool" AI offerings that are having a real impact across different business functions, industry verticals, and geographies. This has meant sifting through the hundreds and hundreds of startups to find the ones that have a genuine AI offering (not simply slapping AI on their websites) and are really unique. Gartner defines AI through three key traits -- the ability to learn, predict and SURPRISE. Last week, my colleagues and I published our first "Cool Vendors in Core Technologies" research report highlighting five vendors that do indeed SURPRISE!