Meaningful artificial intelligence (AI) deployments are now just starting to take place.
According to Gartner’s 2018 CIO Agenda Survey, four percent of CIOs have implemented AI while a further 46 percent have plans to do so.
"Despite huge levels of interest in AI technologies, current implementations remain at quite low levels," says Gartner research vice president and distinguished analyst Whit Andrews.
"However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts."
There are a number of obstacles to the progress of AI adoption for early adopters – as is the case with most emerging or unfamiliar technologies. Gartner has identified four ‘lessons’ that have been revealed from these early AI projects.
Aim low at first
"Don’t fall into the trap of primarily seeking hard outcomes, such as direct financial gains, with AI projects," says 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."
Andrews says organisations should expect Ai projects to produce at best lessons that will help with larger future experiments, pilots and implementations. And while a financial target will be required to start an AI project in some organisations, Andrews says to set the target as low as possible.
"Think of targets in the thousands or tens of thousands of dollars, understand what you’re trying to accomplish on a small scale, and only then pursue more-dramatic benefits."
Focus on augmenting people, not replacing them
People often associate technological advances with a reduction in staff. While this notion is undoubtedly attractive to business executives, Gartner says it is likely to create resistance from those whose jobs appear to be at risk.
"We advise our clients that the most transformational benefits of AI in the near term will arise from using it to enable employees to pursue higher-value activities," says Andrews.
Gartner has forecast that by 2020, a fifth of all organisations will dedicate workers monitoring and guiding neural networks.
"Leave behind notions of vast teams of infinitely duplicable 'smart agents' able to execute tasks just like humans," says Andrews.
"It will be far more productive to engage with workers on the front line. Get them excited and engaged with the idea that AI-powered decision support can enhance and elevate the work they do every day."
Plan for knowledge transfer
It may come as no surprise that most organisations aren’t well prepared for implementing AI. Gartner says that in particular they lack internal skills in data science and plan to rely to a high degree on external providers to fill the gap
Fifty-three percent of organisations in the CIO survey rated their own ability to mine and exploit data as "limited" — the lowest level.
Gartner has predicted that because of this, through 2022 85 percent of AI projects will incorrect outcomes due to bias in data, algorithms or the teams responsible for managing them.
"Data is the fuel for AI, so organisations need to prepare now to store and manage even larger amounts of data for AI initiatives," says Gartner research vice president Jim Hare.
"Relying mostly on external suppliers for these skills is not an ideal long-term solution. Therefore, ensure that early AI projects help transfer knowledge from external experts to your employees, and build up your organisation’s in-house capabilities before moving on to large-scale projects."
Choose transparent AI solutions
It’s almost inevitable that AI projects will include software or systems from external service providers, which Gartner says is why it’s vital that some insight into how decisions are reached is built into any service agreement.
"Whether an AI system produces the right answer is not the only concern," says Andrews.
"Executives need to understand why it is effective, and offer insights into its reasoning when it’s not."
While it may not always be possible to explain all the details of an advanced analytical model like a deep neural network, Gartner says it’s important to at least offer some form of visualisation of the potential choices.