To succeed in the next decade,
companies will have to build high performing teams,
but how exactly do you measure
the quality of those relationships?
Are team members happy,
or are they just smiling through clenched teeth?
What if you could measure these kinds of social interactions
and power dynamics just as you would a balance sheet,
and how might data analytics and AI help?
Human AI is built on the idea of social physics.
It's the idea that you can look at human organizations
and characterize what's working
and what's not working using statistics.
With organizations you want
to do that more or less continuously.
As you might expect, silos are bad.
People working together across organizations, good,
and you can actually watch the patterns of communication
through phone, through face to face,
e-mail's not so important because people tend to ignore it,
but those are the things that show you
the flow of ideas in your organization,
and most organizations don't pay any attention to them.
Somebody else does the phone which is crazy.
There's your most valuable resource,
and you don't even know what's happening with it.
So the sorts of data that you need
to measure of course is how are you talking to yourselves.
Are you actually built into little silos
by district, by country, by goals?
Or are you actually trading ideas
about best practice and what works?
There's really too much data to pay attention to everything,
and how are you going to do it?
That's where some of these AI tools come in.
So for instance, we have ways of having
a computer listen to the conversations in real time,
and measure engagement about the topic.
You can also do other things like that,
like for instance, the patterns
of calls within your organization.
You don't have to look at the content,
just the, you know, does this group talk to that?
And that's something that we do
to be able to sort of diagnose,
essentially, sclerosis within organizations.
Are you talking to yourself,
or are you just in these little echo chambers?
Ideally you're measuring enough
about how things come out in an organization
as a function of these conversations
that you can capture things like power dynamics.
We know that people at the same level
in the organization tend to trade innovations
much more than they trade up, or down,
and obviously that's a problem.
It has to do with power dynamics.
It has to do with reputation,
strategic gaming of the system.
I think you need to set up situations
where that sort of up and down is valued,
things for instance where status is not so obvious,
but you also need to ask how does
the organization of work groups or teams of teams,
how is that working for the things you're trying to do,
and it needs to be very high frequency there.
It needs to be, you know daily,
and I think it's one of the big things
that every organization should be doing aggressively,
is making all of their management structure
really literate in how you use data,
collect data, use it to make decisions,
because if they don't know how to do it,
you're never going to be data driven.
So a few key takeaways are clear.
Break down silos, make sure your team members
are communicating up and down as well as side to side,
and use AI to track trends, measure progress,
and make changes to build better team relationships.
But doing this in practice is tricky.
How do you manage AI to get the most
out of the human beings on your team,
plus the digital technology at hand?
Who should review the data your AI is gathering?
Who'll make decisions about what you're seeing in the data,
and how do you make sure that AI
is working properly versus going off the rails?
In the next video, Sandy shares his insights
on this subject of leading in the age of AI.