The CIO community – in retail and across multiple other industries – has shown a significant level of interest in artificial intelligence (AI), but implementation remains at low levels, according to Gartner.
However, the analyst group suggests that is about to change. A Gartner CIO survey discovered that the business world moving into the next stage of AI deployment, based on four key learnings from early projects in the field.
Meaningful machine learning deployments are just beginning to take place, the research found, with 4% of CIOs having already implemented the technology and a further 46% having made plans to do so.
Whit Andrews, research vice president and analyst at Gartner, remarks: “There is potential for strong growth as CIOs begin piloting AI programmes through a combination of buy, build and outsource efforts.”
Here are the four keys lessons learnt about machine learning so far, according to Gartner.
How to start with AI
“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.”
Gartner says CIOs should expect artificial intelligence projects to produce lessons that will help with larger projects and experimentation later down the line. Where financial targets are needed to start a machine learning project, it says, companies should set low initial return-on-investment goals and then pursue the areas where the most dramatic benefits emerge.
Augment your people with AI
Gartner warns that if organisations automate systems too quickly and cut costs through subsequent job reductions they can miss out on real opportunities to use the technology effectively.
“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,” Andrews notes
Benefits can be gained from getting front-line staff “excited and engaged” about how AI-powered decision support can raise the level of the work they do on a daily basis, he says.
Gartner predicts that in two years’ time 20% of organisations will dedicate workers to monitoring and guiding computer system modelled on the human brain and nervous system, known as neural networks.
Prepare for AI
Internal preparation for use of this technology is still required across many industries, with Gartner’s research showing that 53% of organisations in the CIO survey rated their own ability to mine and exploit data as “limited”. The analyst group predicts that over the coming years, through to 2022, the majority of this type of project will be inaccurate due to multiple factors, such as bias in data 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,” explains Jim Hare, research vice president at Gartner.
“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
Andrews says: “Whether an AI system produces the right answer is not the only concern.
“Executives need to understand why it is effective, and offer insights into its reasoning when it’s not.”
Gartner analysts advise CIOs that it is important there is some insight into how decisions are reached via machine learning built into any service agreement with external suppliers.
The group acknowledges that it may not always be possible to explain all the details of an advanced analytical model, but nonetheless there needs to be some form of visualisation of the potential choices. Indeed, in some cases, it may be a legal requirement to provide this kind of transparency.