AI ‘efficiencies’ not a euphemism for job cuts

AI ‘efficiencies’ not a euphemism for job cuts

4 Min Read

by Ken Wieland

17 July 2019

LONDON -- A recent survey from Gartner Research showed that many organisations, unsurprisingly perhaps, use ‘efficiency’ targets to measure the success of AI projects. But what does that mean in practice? Should employees feel nervous about the axe falling on their jobs once bots get to work?  

Whit Andrews, vice president and distinguished analyst at Gartner -- and a specialist in use cases and business opportunities for AI and cognitive computing -- thinks efficiencies do not necessarily equate to job cuts. Attrition of this sort, he told AI Business, was not high on the corporate agenda of most organisations eyeing up AI implementations.

“In the current economic climate in western Europe and US, my experience from talking to clients is that they’re asking how I can achieve [with AI] the levels of customer service I’m expected to achieve with the people I’ve got,” he said. “Most organisations are going to look at AI and say that efficiency means I can accomplish more things than I could ever do before.”

In the healthcare sector, said Andrews, the drive to empower existing staff through AI and other advanced technologies is particularly apparent. “I’ve never spoken to any healthcare player which has characterised any decision they were going to make as being promising because it would allow them to have fewer workers,” he said. “I’ve only talked to them when they want to use technologies to make their workers more successful.”

Andrews acknowledges there will be some organisations not fully committed to the idea that their people are major assets, but he rarely encounters them. “I never talk to anybody who is saying I want to cut people,” he said. “I talk to people who say I’m trying to cut training time; trying to leverage the people I have; trying to find people faster; trying to reduce the necessary skills; and trying to scale up.” 

There are some nuances about what organisations mean by efficiencies, even if downsizing the workforce is not on the cards. “Using efficiency targets as a way of showing value is more prevalent in organisations who say they are conservative or mainstream in their [technology] adoption profiles,” said Andrews. “Companies who say they’re aggressive in adoption strategies were much more likely instead to say they were seeking improvements in customer engagement.”

Related: Interpretable Automation Is The Future Of AI

The Gartner view is that AI will result in net additions when it comes to jobs. While there will be a ‘displacement effect’, removing or reducing the need for certain types of jobs, there will also be a ‘productivity effect’ where there is increased demand for labour to carry out non-automated tasks. Gartner calculates that the productivity effect will more than compensate for the displacement effect in terms of hard numbers, although the nature of future employment -- if Gartner is correct -- will clearly change. Unskilled workers on this analysis look especially vulnerable, unless they can be trained up for new roles.  

Mind the AI

skills gap

For all the AI excitement about organisations doing more and scaling up, helped along by automated processes, revamping the workplace to achieve these goals is far from straightforward.  A worrying finding from the Gartner survey is that more than half of respondents said that lack of skills was one of the big challenges in adopting AI. Not really understanding AI use cases was something that 42% of respondents fretted about. A third of those canvassed reckoned the scope and quality of data was not up to scratch.

“Finding the right staff skills is a major concern whenever advanced technologies are involved,” said Jim Hare, a research vice president at Gartner. “Skill gaps can be addressed using service providers, partnering with universities, and establishing training programmes for existing employees. However, establishing a solid data management foundation is not something that you can improvise. Reliable data quality is critical for delivering accurate insights, building trust and reducing bias. Data readiness must be a top concern for all AI projects.”

Hare’s views were echoed by Rob Dalgety, industry specialist at Peltarion, a Sweden-based AI software specialist. He emphasised the need for ‘right partnerships’ that could remove some of the pain from AI implementation.

“Organisations could deploy an operational AI platform that takes away some of the core challenges in this area,” said Dalgety. “By giving AI projects a graphical interface and abstracting above the underlying complexity, using pre-built AI workflows and models with better integration into IT infrastructure, organisations can reduce the cost, skills and infrastructure required to run these projects, and move AI projects from concept to production much faster.”

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