The combination of hype and interest in artificial intelligence (AI) is driving companies to increase their investments in the technology but is also but sowing confusion that the channel is best placed to solve.
In a recent announcement, Gartner predicted that by 2020, AI technologies will be in almost every new software product and service brought by the market. By the same time, more than 30 percent of CIOs will have made AI a top-five investment priority.
But with the huge amount of hype around AI, some vendors are indiscriminately putting the AI label on their products to enable them to ride the wave, which is helping to cloud the waters around the market. Gartner calls this "AI washing."
"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," Jim Hare, research VP at Gartner, said in a statement.
Such confusion can be a source of frustration for channel partners as well as end users, according to Rob Enderle, principal analyst with the Enderle Group. A lot of AI-labeled products thrown into the market not only forces VARs, SIs, MSPs and others to sort through them, but can also keep customers from spending.
On the other hand, some confused customers will turn to the channel for help.
"Confusion can be an asset or a liability depending on whether it is causing customers to defer buying or the channel is able to provide unique help the vendor can't scale to provide," Enderle told Channelnomics. "[Partners need to] develop a visible competence and step in as the trusted information source for their customers to ensure they are part of the conversation and aren't cut out."
Industry analysts are expecting accelerating growth in the AI market. IDC's Worldwide Semiannual Cognitive Artificial Intelligence Systems Spending Guide predicts the market will grow this year by more than 59 percent over 2016 to $12.5 billion, reaching $46 billion by 2020. The development of faster CPUs and GPUs that can better handle parallel workloads, the development of deep learning techniques, the rise of use cases, like data analytics and autonomous vehicles, and the proliferation of data being generated have all combined to drive the demand for AI capabilities.
There are myriad opportunities for partners in the booming AI space, from reselling AI platforms and integrating their own AI platforms atop third-party infrastructure, to helping to develop AI clouds and bringing customers to AI platforms in public clouds, like Microsoft Azure and Google Cloud Platform.
However, a key will be for partners to learn not only about AI technologies and vendors but also how that knowledge can best be applied to address customer needs, Enderle said.
"The training and customisation for AI capability on a customer-by-customer basis should eventually largely be shared between the channel and the sourcing vendor with the vendor more heavily involved the larger the company is," he said.
"In addition, knowledge transfer to the client will likely primarily flow through the channel that sells the offerings. [Partners] should be working to understand their unique customer needs and engaging with customers to make it clear they know these needs best and are best able to meet them. This is to avoid these customers developing a belief that they should go directly to the vendors bypassing the channel."
In Gartner's 2017 AI development strategies survey of 83 IT and business leaders, more than half of respondents said that a top challenge to adopting AI was the lack of staff skills, which opens up opportunities for the channel.
According to Hare, partners should "use the term 'AI' wisely" in sales and marketing materials" and "be clear what differentiates your AI offering and what problem it solves".
Enderle echoed the advice to partners. He added that hype around AI is clouding the value of more traditional and proven approaches and suggested vendors use the simplest tools for the job, rather than emerging AI techniques.
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