Feature - 29 January 2020 (ChannelPro)


What AI skill sets do you need in the channel?

Customers want machine learning and AI, but do not yet understand the realities


Gartner says AI projects can require up to 35 different skillsets, from statisticians and storytellers to decision managers and data scientists. While channel companies will be looking to get ahead of the curve to take full advantage of the trend towards AI, there's no need to jump in feet first. Indeed, taking a more considered and methodical approach to growing your AI skills portfolio is likely a better strategy.

First steps are about learning customer needs, notes Barry Coombs, co-owner of VAR

Computerworld. Account managers should simply start asking questions, he says, even if they feel they don't understand AI.

He points out that AI and machine learning can be buzz phrases with little behind them.

"Some vendors are just doing a few SQL queries to look at data and calling it AI," he says.

By grasping the nettle early and having those conversations, the channel can use skills it already has to develop a strategy for achieving the right solutions, whether machine learning or otherwise.

"Right now the big thing is helping our customers – IT professionals – navigate that minefield," Coombs urges.

"Otherwise all you're really doing is selling a load of products for vendors."

Softer skills will initially be crucial – from understanding potential ethical issues to matching expectations with reality. Once that's in place, channel businesses can begin developing specific use cases for exploratory side projects – but they shouldn't wait too long.

"If you wait until machine learning goes mainstream, you'll end up too far behind," Coombs says.

"Start with understanding how those technologies are utilised and where you are going to

specialise."

Craig Lodzinski, lead for developing technologies at infrastructure and services provider Softcat agrees, saying the channel is a unique "checks-and-balances engine between customers and vendors – able to cut through the hype".

There's always the danger that some customers think they know what AI does, regardless of

commercial reality, so educating them will be key.

"HR will say: 'Can we do video interviews and then automatically select people based on the

characteristics of existing employees?' Which is a very complicated way of saying 'can I build a racism robot, please?'," Lodzinski says. . .

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