Why Tech is Great for Employee Experience (And Why It's Not)
Автор: SabaSoftwareTV
Загружено: 2020-03-16
Просмотров: 55
Описание:
Technology has the potential to improve the employee experience. It collects data and packages it into a way that helps us understand where we're succeeding, and where there are areas for improvement.
In this video, RedThread Research co-founder Stacia Garr explains why technology can be good for the workplace, but why it can also be bad.
Get our Definitive Handbook for Creating Exceptional Experiences at Work, free to download here: http://bit.ly/2ovAsAM
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Transcript:
There's a real opportunity to use technology as an enabler of employee experience. For instance, you could use it to help employees get more information and insight into some of their own behaviors.
For instance, things like their frequency of emailing their team, or the way that they talk to their team through different communication channels. Getting information on that and kind of getting coaching on that represents a real opportunity for them to both improve their own employee experience and then also the employee experience of their teams.
There's also just technology that can enable you to understand better what the touchpoints are that are bothering people, that maybe an organization is falling down on the employee experience. Or, what kind of aspects or the continuous relationships – not just specific touchpoints, but the culmination of the relationship between the employee and the organization – we don't understand that as well.
This new technology that can take all that data and help you understand where the opportunities are to potentially improve the employee experience. I think there's a lot of opportunity within the space for technology.
One pitfall I would mention, though, is that anytime we're using any advanced algorithm or “artificial intelligence” as people say, it's important to understand what exactly that technology is and is not able to tell people about their behaviors and to understand also that when and how the technology has been tested and what data sets it may have been tested on and what potential bias may exist within those data sets. There's a lot of opportunity, but we need to make sure that we really understand what the technology is doing and what it’s telling us and whether or not we should be following the advice of it.
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