Artificial intelligence (AI) projects are slowly gaining momentum, according to the latest worldwide market study by Gartner. That said, just four percent of CIO respondents have already implemented AI technologies, while 46 percent have started to work on deployment plans.
"Despite huge levels of interest in AI technologies, current implementations remain at quite low levels," said Whit Andrews, vice president at Gartner. "However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts."
Artificial Intelligence Market Development
As with most emerging technologies, the early adopters are facing many obstacles to the progress of AI within their organizations. Gartner analysts have identified the following four lessons-learned that have emerged from these early AI projects.
"Don’t fall into the trap of primarily seeking hard outcomes, such as direct financial gains, with AI projects," said Mr. Andrews. "In general, it’s best to start AI projects with a small scope and aim for 'soft' outcomes, such as process improvements, customer satisfaction or financial benchmarking."
Expect AI projects to produce, at best, lessons that will help with subsequent, larger experiments, pilots and implementations. In some organizations, a financial target will be a requirement to start the project.
Big technological advances are often historically associated with a reduction in staff head count. While reducing labor costs is attractive to business executives, it is likely to create resistance from those whose jobs appear to be at risk.
In pursuing this way of thinking, organizations can miss out on real opportunities to use the technology effectively. Gartner predicts that by 2020, around 20 percent of organizations will dedicate workers to monitoring and guiding neural networks.
Most organizations aren't well-prepared for implementing AI projects. They lack internal skills in data science and plan to rely on external providers to fill the knowledge gap. Fifty-three percent of organizations in the CIO survey rated their own ability to mine and exploit data as "limited".
As a result, Gartner predicts that through 2022, 85 percent of AI projects will likely deliver erroneous outcomes -- due to bias in data, algorithms or the teams responsible for managing them.
Outlook for New AI Applications
According to the Gartner assessment, AI projects will often involve software or systems from external service providers. It’s important that some insight into how decisions are reached is built into any service agreement.
Although it may not always be possible to explain all the details of an advanced analytical model -- such as a deep neural network -- it’s also important to at least offer some kind of visualization of the potential choices.
"Despite huge levels of interest in AI technologies, current implementations remain at quite low levels," said Whit Andrews, vice president at Gartner. "However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts."
Artificial Intelligence Market Development
As with most emerging technologies, the early adopters are facing many obstacles to the progress of AI within their organizations. Gartner analysts have identified the following four lessons-learned that have emerged from these early AI projects.
"Don’t fall into the trap of primarily seeking hard outcomes, such as direct financial gains, with AI projects," said Mr. Andrews. "In general, it’s best to start AI projects with a small scope and aim for 'soft' outcomes, such as process improvements, customer satisfaction or financial benchmarking."
Expect AI projects to produce, at best, lessons that will help with subsequent, larger experiments, pilots and implementations. In some organizations, a financial target will be a requirement to start the project.
Big technological advances are often historically associated with a reduction in staff head count. While reducing labor costs is attractive to business executives, it is likely to create resistance from those whose jobs appear to be at risk.
In pursuing this way of thinking, organizations can miss out on real opportunities to use the technology effectively. Gartner predicts that by 2020, around 20 percent of organizations will dedicate workers to monitoring and guiding neural networks.
Most organizations aren't well-prepared for implementing AI projects. They lack internal skills in data science and plan to rely on external providers to fill the knowledge gap. Fifty-three percent of organizations in the CIO survey rated their own ability to mine and exploit data as "limited".
As a result, Gartner predicts that through 2022, 85 percent of AI projects will likely deliver erroneous outcomes -- due to bias in data, algorithms or the teams responsible for managing them.
Outlook for New AI Applications
According to the Gartner assessment, AI projects will often involve software or systems from external service providers. It’s important that some insight into how decisions are reached is built into any service agreement.
Although it may not always be possible to explain all the details of an advanced analytical model -- such as a deep neural network -- it’s also important to at least offer some kind of visualization of the potential choices.