Artificial Intelligence (AI) will become a feature of almost every new software product and service by 2020, according to forecasts from Gartner.
Gartner’s analysts also predict that by 2020, AI will be a top five investment priority for more than 30 per cent of CIOs.
But in the rush to cash in on the opportunity, software buyers and vendors need to be wary of ‘AI washing’.
In the same way as software vendors liberally applied the term ‘cloud’ to their solutions ten years ago (“cloudwashing”), Gartner analyst Jim Hare says there is now widespread use of “AI washing” by enterprise software vendors hoping to cash in on “the biggest gold rush in recent years.”
The growing interest in AI for enterprise software is evident in Gartner’s search data; in January 2016, the term “artificial intelligence” was not in the top 100 search terms on gartner.com. By May 2017, the term ranked at number seven.
“As AI accelerates up the Hype Cycle, many software providers are looking to stake their claim in the biggest gold rush in recent years,” said Hare.
“AI offers exciting possibilities, but unfortunately, most vendors are focused on the goal of simply building and marketing an AI-based product rather than first identifying needs, potential uses and the business value to customers.”
Hype and “AI washing” is obscuring the real benefits to be gained by the technology. To successfully exploit the AI opportunity, technology providers need to understand how to respond to three key issues:
1) Lack of differentiation is creating confusion and delaying purchase decisions
The huge increase in startups and established vendors all claiming to offer AI products without any real differentiation is confusing buyers. More than 1,000 vendors with applications and platforms describe themselves as AI vendors, or say they employ AI in their products.
To build trust with end-user organisations vendors should focus on building a collection of case studies with quantifiable results achieved using AI.
“Use the term ‘AI’ wisely in your sales and marketing materials,” Hare said. “Be clear what differentiates your AI offering and what problem it solves.”
2) Proven, less complex machine-learning capabilities can address many end-user needs
Advancements in AI, such as deep learning, are getting a lot of buzz but are obfuscating the value of more straightforward, proven approaches. Gartner recommends that vendors use the simplest approach that can do the job over cutting-edge AI techniques.
3) Organisations lack the skills to evaluate, build and deploy AI solutions
More than half the respondents to Gartner’s 2017 AI development strategies survey indicated that the lack of necessary staff skills was the top challenge to adopting AI in their organisation.
The survey found organisations are currently seeking AI solutions that can improve decision making and process automation. If they had a choice, most organisations would prefer to buy embedded or packaged AI solutions rather than trying to build a custom solution.
“Software vendors need to focus on offering solutions to business problems rather than just cutting-edge technology,” said Hare. “Highlight how your AI solution helps address the skills shortage and how it can deliver value faster than trying to build a custom AI solution in-house.”