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Are We Looking Forward of Backward? · Humanity Working

• 0:05
  SPEAKER_00: Hey Paul, I keep seeing Banny popping up.
• 0:08
  SPEAKER_00: Can you talk about what it is and if we should even be caring about it?
• 0:11
  SPEAKER_00: I think we should be caring about it.
• 0:14
  SPEAKER_01: Um probably the easiest way to talk about it is to talk about what preceded it.
• 0:20
  SPEAKER_01: So in the military, um for years and I'm possibly they they still do use it, um, there was a term that was often used to describe the new world in which we live, and it was VUCA.
• 0:33
  SPEAKER_01: Uh VUCA stood for volatile, uncertain, complex, and ambiguous.
• 0:38
  SPEAKER_01: Um and it was useful, right?
• 0:40
  SPEAKER_01: It's useful to be able to think about, you know, to be able to understand the what the theater of war might look like.
• 0:48
  SPEAKER_01: It's useful to understand what the world in which we exist might look like.
• 0:51
  SPEAKER_01: Um and because it was useful, like a lot of things that have come from the military that could have a use in the in business, it started to kind of wend its way into business because, you know, we think about most businesses there these days.
• 1:02
  SPEAKER_01: They're they're different than what they used to be, right?
• 1:04
  SPEAKER_01: And they could be a bit volatile, they could be a bit uncertain, they could be a bit complex, they could be a bit ambiguous.
• 1:10
  SPEAKER_01: Um way back, I think we spoke to uh to Charlie Gilkey um about the VUCA concept and how you go about managing for the uh for the VUCA concept.
• 1:19
  SPEAKER_01: If people want to go back and listen to that episode, um but around about 2020, I think it was, uh so probably most likely middle of the pandemic, um, this other term started to emerge, um, which was BANI.
• 1:36
  SPEAKER_01: And um again, four-letter acronym to kind of describe the world in which we live.
• 1:42
  SPEAKER_01: But I think a much more appropriate term for the world in which we live right now.
• 1:48
  SPEAKER_01: So banny stands for brittle, anxious, nonlinear, and incomprehensible.
• 1:53
  SPEAKER_01: So we'll briefly go through each of those different things.
• 1:55
  SPEAKER_01: So brittle, you can imagine that means that things that it's almost like uh an indication of fragility, right?
• 2:02
  SPEAKER_01: Things that seem can seem strong can can break fairly quickly.
• 2:05
  SPEAKER_01: We've seen that in a whole bunch of different scenarios.
• 2:07
  SPEAKER_01: The supply chain, which we've determined, we found out is very brittle.
• 2:11
  SPEAKER_01: Um, anxious, right?
• 2:12
  SPEAKER_01: So this is sort of talking about the psychological uh backdrop uh in which in which we live now and anxiety levels inside organizations are uh through the roof.
• 2:24
  SPEAKER_01: Um nonlinear.
• 2:26
  SPEAKER_01: Um so you know, back in the day, A led to B.
• 2:31
  SPEAKER_01: You did A, B obviously, uh obviously followed now.
• 2:35
  SPEAKER_01: Um due to the complexity and due to all the different factors, um, A doesn't necessarily easily uh lead to B.
• 2:44
  SPEAKER_01: Um and so it can be more difficult to make business decisions in that background.
• 2:49
  SPEAKER_01: And then the I is an interesting one, it's incomprehensible.
• 2:53
  SPEAKER_01: So the you know, it's almost an extension of what we said when we said things were complex.
• 3:00
  SPEAKER_01: The theory here is that things have got so complex that it's actually uh relatively difficult to even understand them fully.
• 3:07
  SPEAKER_01: Um and so I do think it's important because if you think about what that means as a manager, if you think about what that means as a leader, it means you are managing or leading in times that are inherently very uncertain.
• 3:22
  SPEAKER_01: And so it comes back to the discussion that we had, gosh, a couple of years ago with um with Matt Watkinson managing uncertainty.
• 3:30
  SPEAKER_00: Yeah.
• 3:30
  SPEAKER_01: Um, it's a reflection of the fact that uh the environments in which we are in, the business environments in which we are in, are very uncertain.
• 3:39
  SPEAKER_01: Doesn't mean we can't have strategies, but it means that our strategies have to reflect the banny nature in which we live.
• 3:45
  SPEAKER_00: Yeah, that's so that's so accurate.
• 3:47
  SPEAKER_00: In that kind of world, uh, how can leaders keep their decisions forward-looking instead of locked on yesterday's data, also?
• 3:55
  SPEAKER_01: Yeah.
• 3:55
  SPEAKER_01: Well, I mean, uh obviously if we're talking about the military, then what's that phrase in the military about you know about fighting the last war?
• 4:02
  SPEAKER_01: And and um it's a very human trait, right?
• 4:07
  SPEAKER_01: I mean, you don't you never want to repeat the errors that you've made previously, so you do look backwards to try to uh to understand the mistakes you made before um and correct for those.
• 4:21
  SPEAKER_01: But the challenge is that um if the world in which you sit is fundamentally different to the world in which you sat a year ago or two years ago, you might.
• 4:30
  SPEAKER_01: You might be learning the wrong lessons.
• 4:34
  SPEAKER_01: Um and so really the first thing um that leaders have to think about is is their own mindset, right?
• 4:44
  SPEAKER_01: Yes, look at their own behaviors, look at what happened inside the business before, but do so as a means of framing the context of as you do uh of what you do, not as a not as a concrete guide.
• 4:59
  SPEAKER_01: Right?
• 4:59
  SPEAKER_01: If you solved a problem previously by laying off 20% of your workforce, it doesn't mean that you're gonna solve today's problem by laying off 20% of your workplace.
• 5:08
  SPEAKER_01: You are probably um in a different uh in a different position.
• 5:12
  SPEAKER_01: The other thing I would say as well is that um while it's impossible to fully predict the future, um, there are some things that we do know, right?
• 5:20
  SPEAKER_01: We know the cost of robotics is coming down.
• 5:22
  SPEAKER_01: We know that um AI, automation, and robotics, they're starting to converge.
• 5:29
  SPEAKER_01: Um, and so we're gonna see more systems um that are capable of doing more complex uh tasks more effectively.
• 5:39
  SPEAKER_01: Right.
• 5:40
  SPEAKER_01: Um, but we also know that the need for really effective, really adaptable, really resilient human beings is only gonna go up because they're the added layer of value.
• 5:50
  SPEAKER_01: Once everybody, as we've talked about this before, but like if every organization has democratized access to very high level technology, yeah, the differentiator inside your organization is going to be the humans.
• 6:00
  SPEAKER_01: So you have to think about the value of your humans is as high or higher than it has ever been, and you should invest accordingly.
• 6:07
  SPEAKER_00: Yeah.
• 6:07
  SPEAKER_00: Wow, that's so powerful.
• 6:09
  SPEAKER_00: Um yeah, very interesting.
• 6:12
  SPEAKER_00: So, so let's look now like when big tech makes workforce moves, should everybody else copy them or are their circumstances too different?
• 6:22
  SPEAKER_01: Yeah, I uh I've been looking at this a lot, and I think we're gonna we're gonna probably do at least one or more additional episodes on this, and I'm gonna um write a couple of newsletters on this.
• 6:35
  SPEAKER_01: Um bunch of things that that I want to say about it, but we'll keep it really short here.
• 6:41
  SPEAKER_01: So the first thing is um if you are not big tech yourself, then why would you think that big tech is the model for you?
• 6:51
  SPEAKER_01: The reasons that people typically think about is they think, oh man, they must know stuff that we don't know, right?
• 6:56
  SPEAKER_01: Because they're in, they're making the investments in AI, they're making those investments in robotics, et cetera.
• 7:02
  SPEAKER_01: They must be looking forward and seeing a future that means that they don't need all of these categories of workers, and so they're eliminating them.
• 7:11
  SPEAKER_01: But in many cases, big tech is just like any other company, right?
• 7:17
  SPEAKER_01: When you make decisions, for example, about hiring and firing or strategic decisions, yeah, you might be looking forward, but a good proportion of you is course correcting from the previous decisions you made.
• 7:29
  SPEAKER_00: Right.
• 7:29
  SPEAKER_01: Right.
• 7:30
  SPEAKER_01: And so if you made massive investments in one area and it's not really panning out, you got to correct for that, right?
• 7:36
  SPEAKER_01: Or if you made massive investments based on a societal trend and that societal trend is corrected or reversed in some way, you got to correct for that.
• 7:43
  SPEAKER_01: So a classic example of this would be like we went through this pandemic.
• 7:47
  SPEAKER_01: Millions of people started moving from one part of the country to another.
• 7:50
  SPEAKER_01: Millions of people who had never been logged on in front of a computer and telecommuting the whole time all of a sudden were.
• 7:58
  SPEAKER_01: And we went from uh from not a standing start, but we went from a relatively small number to a massive number in a period of time.
• 8:05
  SPEAKER_01: Big tech invested massively to support that.
• 8:07
  SPEAKER_01: A lot of big tech also invested massively to support large-scale investments in massive high uh high-density compute clusters in order to support the medical effort associated with finding a cure for the pandemic.
• 8:19
  SPEAKER_01: Right.
• 8:19
  SPEAKER_01: So there was a huge amount of money that went in a various different set of places and a huge amount of hiring that happened in that period.
• 8:26
  SPEAKER_01: We ever exit we're exiting that period and they're course correcting, just like companies course corrected after the Second World War.
• 8:32
  SPEAKER_00: Yeah.
• 8:32
  SPEAKER_01: So following that is not always because they're ahead of you.
• 8:37
  SPEAKER_01: It's because they're cost correcting for the things that they did previously.
• 8:41
  SPEAKER_01: And they are different types of organizations, very different types of organizations.
• 8:45
  SPEAKER_01: So if you are not them, then they don't have to be your model.
• 8:49
  SPEAKER_00: Yeah, yeah.
• 8:50
  SPEAKER_00: It's also like when you talk about big tech, it it's interesting in in large-scale production.
• 8:56
  SPEAKER_00: For example, if you're building cars versus building a cloud-based software, you know, you're not producing a widget.
• 9:06
  unknown: Yeah.
• 9:06
  SPEAKER_00: You're producing uh an ethereal thing that's ever changing.
• 9:10
  SPEAKER_00: And so you're you're you're kind of funneling into a thing.
• 9:13
  SPEAKER_00: Why would it, why would it's very interesting when you think about it that way, too.
• 9:17
  SPEAKER_01: Yeah, look at Henry Mentzberg's work.
• 9:18
  SPEAKER_01: We'll throw the show notes, throw that in the show notes again.
• 9:21
  SPEAKER_01: Um Henry was on the pod previously, but he wrote, if you remember, he wrote that book, Understanding Organizations Finally.
• 9:27
  SPEAKER_01: Uh, and he has like seven different models of organizations.
• 9:32
  SPEAKER_01: And uh like we have this, you know, Harvard Business Review, I blame for this stuff, but it's we have this, oh, this company makes a ton of money, so do what this company does.
• 9:42
  SPEAKER_01: But if that company is a different, that's almost like saying, you know, George Clooney makes a ton of money, so I'm gonna start acting, right?
• 9:50
  SPEAKER_01: It doesn't work.
• 9:51
  SPEAKER_01: It doesn't work that way, you know.
• 9:52
  SPEAKER_00: Right.
• 9:52
  SPEAKER_00: Yeah.
• 9:53
  SPEAKER_00: All right.
• 9:54
  SPEAKER_00: So uh interesting stuff.
• 9:56
  SPEAKER_00: Um so you said that AI is shifting from thinking partner to doing partner.
• 10:00
  SPEAKER_00: What is that gonna mean for jobs in practice?
• 10:02
  SPEAKER_01: Yeah, so we alluded to this a little bit.
• 10:05
  SPEAKER_01: Um really, you know, when we start to think about agentic AI as a is a good example of of the direction that this is going, but I see this as that confluence of those three trends, the robotics, the AI, and the automation.
• 10:18
  SPEAKER_01: So um and if you if you look at that ARA world, ARA, automation, robotics, automation, that confluence that creates um a situation where we move from the idea of generative AI, human sitting here, having the AI help them think, right, and then the human sort of doing the doing to getting to a point where we can take functions and have the AI perform those functions more uh more or less end-to-end.
• 10:55
  SPEAKER_00: Yeah.
• 10:56
  SPEAKER_01: So I think what this means for jobs in practice is that um the tightly defined jobs, um AI is gonna be one of the ones that it's gonna be those things that it can come after really clearly.
• 11:12
  SPEAKER_01: So the humans need not just to be able to thrive amidst ambiguity, but they need to demonstrate that they can thrive amidst ambiguity.
• 11:20
  SPEAKER_01: Um when we spoke to to Keith, and I know we're gonna get onto this I think soon in another episode, but we spoke to Keith, he talked about that idea of, you know, not my job, and you don't want to have not my job as being your as being a thing that you say.
• 11:33
  SPEAKER_01: Yeah.
• 11:33
  SPEAKER_01: Um, we need to become not my job people, uh, we need to sorry, we need to be the anti not my job people.
• 11:39
  SPEAKER_01: Right?
• 11:40
  SPEAKER_01: Everything's your job.
• 11:41
  SPEAKER_00: Yeah.
• 11:41
  SPEAKER_01: Everything that you can do, you need to be able to throw your hand at.
• 11:44
  SPEAKER_01: And so that means super adaptable, super resilient humans.
• 11:49
  SPEAKER_00: Mm-hmm.
• 11:50
  SPEAKER_00: Amazing.
• 11:50
  SPEAKER_00: It keeps coming back to that after all of these years of research.
• 11:53
  SPEAKER_00: It still holds.
• 11:55
  SPEAKER_00: It's amazing.
• 11:55
  SPEAKER_00: Um so lastly, if I'm running a mid-sized firm, what should I ask before hiring, reskilling, laying people off?
• 12:02
  SPEAKER_00: What what what do I need to ask now?
• 12:04
  SPEAKER_01: Yeah.
• 12:04
  SPEAKER_01: So it it actually, funnily enough, you said it comes back to that.
• 12:07
  SPEAKER_01: It does come back to that, right?
• 12:08
  SPEAKER_01: I mean, I think that um now is the time uh for organizations to be specifically hiring for the trait of adaptability.
• 12:21
  SPEAKER_00: Yeah.
• 12:22
  SPEAKER_01: Now is the time for organizations to be not just assuming that the workplace will help people retain that adaptability.
• 12:33
  SPEAKER_01: So they need to be uh they need to be actively continuously skilling people.
• 12:40
  SPEAKER_01: So it's not really reskilling, it's continuous skill.
• 12:43
  SPEAKER_01: So they need to be doing continuous skilling for uh for people to make sure that they remain adaptable.
• 12:51
  SPEAKER_01: Um and in part that's done through the sort of stuff we do, the the learn do exercises and so on.
• 12:59
  SPEAKER_01: But it's also about embedding learning opportunities into people's uh into people's day-to-day work.
• 13:04
  SPEAKER_01: And then regarding laying people off, um it's the one the people that think about the people that you need to keep, and the people that you need to keep are the ones that are showing demonstrating that adaptability every single day.
• 13:18
  SPEAKER_00: Yeah.
• 13:19
  SPEAKER_00: All right.
• 13:20
  SPEAKER_00: All right, till the next one, Paul.
• 13:21
  SPEAKER_00: Thanks.
• 13:22
  SPEAKER_00: All right, cheers.
• 13:24
  SPEAKER_00: Humanity Working is produced and edited for Billion Minds by Matt Neo.
• 13:28
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• 13:32
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• 13:34
  SPEAKER_00: If you want to learn more about how Billion Minds gets companies ready for the future of work by building adaptable, resilient employees, look for them on LinkedIn or visit their website at billionminds.com.