Speaker 1: Most organizations measuring their AI progress and measuring the wrong theme.
Speaker 1: They're tracking how many people have a license, how many
Speaker 1: you have done the onboarding training, what percentage are using AI?
Speaker 1: In some form? And on paper it looks like progress,
Speaker 1: but in practice it often isn't because using AI and
Speaker 1: using AI well are two completely different things. One can
Speaker 1: actually make your team less productive because you get buried
Speaker 1: in mediocre output, drowning in AI slop, and none of
Speaker 1: it's moving the needle. So in this episode, Neo and
Speaker 1: I get into what actually separates high performing AI augmented
Speaker 1: teams from the average ones. We cover why adoption metrics
Speaker 1: are almost always vanity metrics, the difference between AI literacy
Speaker 1: and AI leverage, and why most teams nail the first
Speaker 1: and never get to the second. We also talk through
Speaker 1: what it looks like to truly re engineer your workflows,
Speaker 1: not just slot AI into the ones that you already have,
Speaker 1: and by the end you will have a much clearer
Speaker 1: picture of where your team sits and what it would
Speaker 1: take to close the gap. Welcome to how IAI with
Speaker 1: me Doctor Amantha Imber and Neo Applin, head of Inventium AI.
Speaker 1: Each episode we share one practical way to use AI
Speaker 1: better at work and in life. No fluff, no deech jargon,
Speaker 1: just things you can use straight away. So at inventium AI,
Speaker 1: we have worked with one hundreds and hundreds of different
Speaker 1: teams and many organizations around building AI capability, and I
Speaker 1: think there's still a really big difference between what a
Speaker 1: high performing AI augmented team looks like like and what
Speaker 1: an average team looks like. Neo, what are you noticing
Speaker 1: are the biggest difference is because most teams are using
Speaker 1: AI in some shape or form, but how are they
Speaker 1: really high performing ones different.
Speaker 2: They've embedded it within the workday core, so other people
Speaker 2: are using it a little bit, and the poor performing
Speaker 2: teams are actually over using it poorly. So maybe we'll
Speaker 2: start there. So the poor performing teams are the ones
Speaker 2: who are doing AI slop, So they're the ones who
Speaker 2: are looking really efficient. They're creating lots of documents and
Speaker 2: we're creating lots of packs of blah blah blah. They're
Speaker 2: just getting AI to do the lot, and unfortunately they're
Speaker 2: just getting buried in information. So a little bit of training,
Speaker 2: which is basically here's AI, you've got it now, go,
Speaker 2: is actually the worst part of training you can do
Speaker 2: showing people how to correctly use it. Then you're actually
Speaker 2: getting some benefits, but then generally it becomes I make
Speaker 2: a joke where it's my dad brought my grandma when
Speaker 2: you're still alive, a microwave and she for one thing only,
Speaker 2: which was defrosting soup. AI is being used the same
Speaker 2: people know how to use it that one way, and
Speaker 2: so therefore it's being used that one way every single time,
Speaker 2: every single week, so they're not opening up the breadth.
Speaker 2: The big changes that happen when you actually embed it
Speaker 2: in your work date where you're using it in different
Speaker 2: ways to be able to solve problems, to do things better,
Speaker 2: to do things faster, and that's where you get the benefits.
Speaker 2: But that's where everyone needs to be using it for
Speaker 2: similar kind of things and so that everyone is using
Speaker 2: AI to all get better, to get stronger, to get
Speaker 2: a better result. And that's where the higher performing teams work.
Speaker 2: It's not just about using the tool, it's using it
Speaker 2: well so that you're delivering for your team and for
Speaker 2: your customers and clients.
Speaker 1: And this is where I see leaders getting their metrics
Speaker 1: around AAI a little bit wrong. A little bit kind
Speaker 1: of vanity metrics as I would think of them, where
Speaker 1: I would say a lot of organizations that we encounter
Speaker 1: will say we've got an adoption target. So for example,
Speaker 1: we want you know, eighty percent of our workforce to
Speaker 1: have adopted AI, which basically means using AI or maybe
Speaker 1: you know, put through some internally created perhaps not that
Speaker 1: useful training in how to use AI, certainly using it responsibly.
Speaker 1: And if that is your target around how well you
Speaker 1: are going with AI that people are using it, that
Speaker 1: target is just so way off because you can easily
Speaker 1: actually get a whole lot worse when it comes to productivity.
Speaker 1: Let alone, just keep your productivity the same as Neo
Speaker 1: was saying, So Neo, can you share how with inventim
Speaker 1: AI's clients where we take quite a different approach because
Speaker 1: we see this in two parts in terms of first, yes,
Speaker 1: there absolutely is literacy, but then there's leverage. Can you
Speaker 1: talk a little bit about that.
Speaker 2: Yeah, literacy first off is what are these tools, how
Speaker 2: they are different, how do we get the best out
Speaker 2: of those things, and how do we avoid things like
Speaker 2: aislop And that is really important. That is your foundation.
Speaker 2: You need to have a solid foundation on what is
Speaker 2: this thing? And then how's it going to work? And
Speaker 2: some of these things are different for leadership teams, and
Speaker 2: we run leadership teams and boards through this, So like
Speaker 2: if you're setting policies, then you need to know what
Speaker 2: these tools are, how they work, so you can then
Speaker 2: create the policy. But also for knowledge workers, which is
Speaker 2: the bulk of our work, to know how and when
Speaker 2: to use it is really caught. The leverage comes in
Speaker 2: where it's how do I embed this in my day job?
Speaker 2: How do I speed up the things that I'm doing?
Speaker 2: And how do I get a better result? Because sometimes
Speaker 2: speeding up isn't the most important thing. Sometimes better result,
Speaker 2: but slower is better, or maybe a more customer centric
Speaker 2: approach to things is better. So how do I use
Speaker 2: this brand new tool. We've got to be able to
Speaker 2: get the benefit. And it's not about let's get the
Speaker 2: same process we've always had and then get AI to
Speaker 2: step into one of those steps. Yep, that's an interim step.
Speaker 2: That's where a lot of organizations go. It's more about
Speaker 2: now we've got this tool, now we understand what it is,
Speaker 2: how do we reinvent how we do our work, how
Speaker 2: we deliver our services, now we've got this amazing tool,
Speaker 2: and that's where the organizations really go better, and that's
Speaker 2: using the leverage of AI in a new way, because
Speaker 2: this is a new tool we've never had before.
Speaker 1: Can you give an example maybe of a team that
Speaker 1: you've worked with where you've really couldn't double down on going, okay,
Speaker 1: this is how you leave reach AI in things like
Speaker 1: you're doing every day.
Speaker 2: Here's a really simple and we see this a lot
Speaker 2: of places. Back in the day, managers look at a
Speaker 2: spreadsheet and they'd go, I need to get some insights
Speaker 2: out of this. But maybe managers weren't the pivot table experts.
Speaker 2: Maybe they weren't great with Excel formulas because you didn't
Speaker 2: need to be because you'd have that awesome person in
Speaker 2: your department, let's call it. You go to Susie and say, hey, look,
Speaker 2: can you please do some analysis on this spreadsheet for me?
Speaker 2: Now you could say, why don't we get Susie with
Speaker 2: AI so Susie can do it faster. That's one way
Speaker 2: to do it, and certainly a lot of people doing that.
Speaker 2: We're teaching a lot of Susie's and David's and Justin's
Speaker 2: to be able to do that as well, But wouldn't
Speaker 2: it be better if we could get AI to be
Speaker 2: there to be able to service the manager, so everyone
Speaker 2: then can self service rather than going to Susie. It's
Speaker 2: then I can ask questions of the AI, so either
Speaker 2: I can get that information out or even better, I
Speaker 2: can get the report that I need in the way
Speaker 2: that I need every single time. So it's not so
Speaker 2: much me just ask questions of it. It's I can
Speaker 2: just feed it a spreadsheet. It knows exactly what I'm
Speaker 2: after in a particular way I'm after because I've either
Speaker 2: built a workflow or built an agent or a GPT
Speaker 2: or a GEM or a project that then deliver me
Speaker 2: exactly what I need every single time. So it's an
Speaker 2: easy way to re envisage the way that we're doing things.
Speaker 2: But these are all embedded with so many different teams
Speaker 2: we've got, Like here's an example of like a hotel,
Speaker 2: you get lots of Q and A coming through to
Speaker 2: your concierge desk. You know what times a pool open
Speaker 2: and if I made a reservation, can I cancel it?
Speaker 2: And how of far and all those kind of things,
Speaker 2: So you could have to have that information in your
Speaker 2: brain or you can get an agent to be able
Speaker 2: to craft those emails for you for your customer, or
Speaker 2: you can even build a workflow where AI can help
Speaker 2: you to do that kind of thing automatically and it
Speaker 2: drops out the hard ones that you need human to process.
Speaker 2: So maybe it's like eighty twenty eighty percent has been
Speaker 2: automated and twenty percent has been taken care of by people.
Speaker 2: It's now what we've got with this tool, how do
Speaker 2: we solve that problem better and perhaps go closer to
Speaker 2: the source or closer to the person who needs to
Speaker 2: use it. And that's the real leverage.
Speaker 1: And what we're certainly finding and recommending to our clients
Speaker 1: is that, yes, everyone needs a certain amount of literacy
Speaker 1: about AI so that they're getting good output rather than
Speaker 1: just proliferating the organization with aislop, which is going to
Speaker 1: have really detrimental impacts on productivity, reputation, all sorts of things.
Speaker 1: But then what we're typically doing is we're working with
Speaker 1: AI champions and these are spread across different functional groups
Speaker 1: because you need to have that more advanced understanding of
Speaker 1: what AI is capable of in order to get those benefits,
Speaker 1: because most people are not like workflow architects where they
Speaker 1: know how to identify what are the different workflows that
Speaker 1: this individual or this group goes through daily or weekly,
Speaker 1: And then how do you even map out a workflow?
Speaker 1: Like most people don't have those skills, and that is
Speaker 1: a really critical skill set to have in order to
Speaker 1: really leave reach AI. If you can't do that, it's
Speaker 1: really hard to access the gains.
Speaker 2: And it's even harder to go mapping. It is one thing,
Speaker 2: one skill to then go how do I re envisage?
Speaker 2: That's a completely different skill as well, And that kind
Speaker 2: of takes a bit of a bit of a blue
Speaker 2: sky thinker, someone who understands the business and also can
Speaker 2: understand what the technology can do. And so yeah, we
Speaker 2: do definitely help these kind of people to build up
Speaker 2: those skills. We also build a bunch of tools to
Speaker 2: help them to help themselves with AI as well.
Speaker 1: So I would say, if you are listening and relating
Speaker 1: to having some sort of a metric in your organization
Speaker 1: or maybe even just for yourself, to go, yes, have
Speaker 1: I got people using AI? Have I given them licenses?
Speaker 1: So have I put them through some basic training? I
Speaker 1: would say it's really problematic if you are stopping there
Speaker 1: so I would challenge any listener to think about how
Speaker 1: can you really leaverage AI? How can you unpack your workflows?
Speaker 1: How can you really think deeply around where AI is
Speaker 1: best place to help you. Obviously, Neo and I are
Speaker 1: very biased because we do a lot of this training
Speaker 1: at Inventium AI, but by all means go anywhere, but
Speaker 1: make sure you are doing it to unlock the full
Speaker 1: benefits of AI whatever your job looks like.
Speaker 2: And just kind of leveraging that. I've read some studies
Speaker 2: a couple of days ago. The minimum viable dose for
Speaker 2: AI training is five hours minimum viable. Anything less than
Speaker 2: that you're not going to get any benefit in the workplace.
Speaker 2: But that is not about leverage. That is just about baseline.
Speaker 2: Can I use this tool? There are companies that spend
Speaker 2: up to believe it or not, eighty one hours on
Speaker 2: their teams, and what that's about is is what Amantha
Speaker 2: and I are talking about, which is using the skills
Speaker 2: in your workplace to re envisage your workflows and help
Speaker 2: your clients even better than you did before. That's where
Speaker 2: the benefit comes from.
Speaker 1: And Neo, I'm going to do a shameless plug because
Speaker 1: we do have a new program that We've just launched
Speaker 1: an inventium called the AI Agent boot Camp, which is
Speaker 1: designed to solve this very problem where we will be
Speaker 1: taking people through how do you become a workflow architect
Speaker 1: and how do you really double down on understanding where
Speaker 1: can AI help you augment or streamline those workflows get
Speaker 1: massive productivity savings but also get far better output at
Speaker 1: the end of the day.
Speaker 2: Yeah, it's a fun day. And also you bring one
Speaker 2: of your problems and we will help you solve those
Speaker 2: problems with agents during the day, so you've actually got
Speaker 2: us and a whole bunch of AI tools to be
Speaker 2: able to help you with that. So yeah, it's good
Speaker 2: fun day.
Speaker 1: And there is a link to that in the show notes.
Speaker 1: How IAI was hosted by me, Amantha Imber and Neo Applan.
Speaker 1: A big thank you to Martin Imba who does our
Speaker 1: sound editing, and Jim Rubio for production support, and thank
Speaker 1: you to John Kilby who composed the theme music.