AI's Dirty Little Secret: Why Your Data Isn't Enough to Win

Let's talk about the data that's the Let's talk about the data that's the foundation for AI. Um, foundation for AI. Um, foundation for AI. Um, not in a technical sense obviously, but not in a technical sense obviously, but not in a technical sense obviously, but in a practical sense of like do you have in a practical sense of like do you have in a practical sense of like do you have the foundations in place that are the foundations in place that are the foundations in place that are actually going to make AI work? We ran a

actually going to make AI work? We ran a actually going to make AI work? We ran a piece of research recently um with a piece of research recently um with a piece of research recently um with a thousand Australian business leaders and thousand Australian business leaders and thousand Australian business leaders and 44% said access to relevant business 44% said access to relevant business 44% said access to relevant business context and data was most important for context and data was most important for context and data was most important for AI agents to operate efficiently. And in AI agents to operate efficiently. And in AI agents to operate efficiently. And in my experience, this is where a lot of my experience, this is where a lot of my experience, this is where a lot of organizations can run into trouble. organizations can run into trouble. organizations can run into trouble. There's actually a distinction that's

There's actually a distinction that's There's actually a distinction that's worth making here. Data is what worth making here. Data is what worth making here. Data is what happened. Eg a deal closed, a customer happened. Eg a deal closed, a customer happened. Eg a deal closed, a customer churned, a campaign ran. Uh context is churned, a campaign ran. Uh context is churned, a campaign ran. Uh context is what that data means. So why the deal what that data means. So why the deal what that data means. So why the deal closed, what made the customer leave, closed, what made the customer leave, closed, what made the customer leave, why your what your team learned along why your what your team learned along why your what your team learned along the way. And that's where the gap lives.

the way. And that's where the gap lives. the way. And that's where the gap lives. Um Angard Zero's whole AI thesis is Um Angard Zero's whole AI thesis is Um Angard Zero's whole AI thesis is built on being a trusted system of built on being a trusted system of built on being a trusted system of record. and you've talked publicly about record. and you've talked publicly about record. and you've talked publicly about the shift from system of record to the shift from system of record to the shift from system of record to system of action. Um what has to be true system of action. Um what has to be true system of action. Um what has to be true inside an organization before that shift inside an organization before that shift inside an organization before that shift is possible. is possible.

is possible. >> Yeah, I think you mentioned obviously >> Yeah, I think you mentioned obviously >> Yeah, I think you mentioned obviously data is a critical foundation. Uh but I data is a critical foundation. Uh but I data is a critical foundation. Uh but I think for any system to become the think for any system to become the think for any system to become the system of action uh you also have system of action uh you also have system of action uh you also have context of the workflow to your point context of the workflow to your point context of the workflow to your point you know there's one thing to know what you know there's one thing to know what you know there's one thing to know what is in my bank statement. It's another is in my bank statement. It's another is in my bank statement. It's another thing to know how many times is that the thing to know how many times is that the thing to know how many times is that the same thing? same thing? same thing? >> How often does that need to then >> How often does that need to then >> How often does that need to then interact with different members of my interact with different members of my interact with different members of my team? what does my accountant and team? what does my accountant and team? what does my accountant and bookkeeper do with this information? Uh bookkeeper do with this information? Uh bookkeeper do with this information? Uh they have pattern recognition. And so, they have pattern recognition. And so, they have pattern recognition. And so, you know, the next thing you need to do

you know, the next thing you need to do you know, the next thing you need to do on that data is trust. And to get trust, on that data is trust. And to get trust, on that data is trust. And to get trust, we've talked about it just in that we've talked about it just in that we've talked about it just in that previous conversation. It's how do you previous conversation. It's how do you previous conversation. It's how do you have the right human in the loop at the have the right human in the loop at the have the right human in the loop at the right time? What level do you want to be right time? What level do you want to be right time? What level do you want to be automated? And what level do you want to automated? And what level do you want to automated? And what level do you want to go in your workflow go in your workflow go in your workflow to a human to cross-check something to to a human to cross-check something to to a human to cross-check something to then trigger the next action? So we then trigger the next action? So we then trigger the next action? So we think it's critical to build that trust think it's critical to build that trust think it's critical to build that trust uh with the human in the loop and to do uh with the human in the loop and to do uh with the human in the loop and to do that I think you know we have we have

that I think you know we have we have that I think you know we have we have the benefit but I think any company the benefit but I think any company the benefit but I think any company that's in this system of record space that's in this system of record space that's in this system of record space has this opportunity which is you have has this opportunity which is you have has this opportunity which is you have years and years of context of how that years and years of context of how that years and years of context of how that information is used how it gets information is used how it gets information is used how it gets manipulated how it gets used to make manipulated how it gets used to make manipulated how it gets used to make decisions to your point um and that's decisions to your point um and that's decisions to your point um and that's all the additional context you need to all the additional context you need to all the additional context you need to build on top of the raw data build on top of the raw data build on top of the raw data >> yeah absolutely Helen can you share what

>> yeah absolutely Helen can you share what >> yeah absolutely Helen can you share what the companies that you work with have the companies that you work with have the companies that you work with have had to manage uh in terms of data had to manage uh in terms of data had to manage uh in terms of data foundations and what advice you would foundations and what advice you would foundations and what advice you would have. have. have. >> Yeah, look I think this is a journey >> Yeah, look I think this is a journey >> Yeah, look I think this is a journey that was already happening. Data um you that was already happening. Data um you that was already happening. Data um you know machine learning is 15 years old, know machine learning is 15 years old, know machine learning is 15 years old, data lakes and so on were already um data lakes and so on were already um data lakes and so on were already um incredibly important and the engineering incredibly important and the engineering incredibly important and the engineering of those to give access. Um I think uh

of those to give access. Um I think uh of those to give access. Um I think uh the difference now is the autonomy of the difference now is the autonomy of the difference now is the autonomy of the agents working with that data that the agents working with that data that the agents working with that data that potentially is fully automated. No one potentially is fully automated. No one potentially is fully automated. No one is checking that it makes sense. Um I I is checking that it makes sense. Um I I is checking that it makes sense. Um I I think there's a lot of talk about the think there's a lot of talk about the think there's a lot of talk about the data being important, the context being data being important, the context being data being important, the context being important. I think on top of all that is

important. I think on top of all that is important. I think on top of all that is is judgment which at the moment is more is judgment which at the moment is more is judgment which at the moment is more the human piece. I think it will become the human piece. I think it will become the human piece. I think it will become an autonomous piece as well and then an autonomous piece as well and then an autonomous piece as well and then we'll need to check the judgments we'll need to check the judgments we'll need to check the judgments >> and have checks and balances on those >> and have checks and balances on those >> and have checks and balances on those judgments. But at the moment it is um it judgments. But at the moment it is um it judgments. But at the moment it is um it is where most of most organizations are is where most of most organizations are is where most of most organizations are still keeping the human judgment over uh still keeping the human judgment over uh still keeping the human judgment over uh those those calls. Um I think that will those those calls. Um I think that will those those calls. Um I think that will change and I think that's where it gets

change and I think that's where it gets change and I think that's where it gets very dangerous if you haven't got your very dangerous if you haven't got your very dangerous if you haven't got your data data data um engineering strong to the point of um engineering strong to the point of um engineering strong to the point of you know it's correct you know um the you know it's correct you know um the you know it's correct you know um the logic of of the entire organization logic of of the entire organization logic of of the entire organization uh and can leave things autonomously uh and can leave things autonomously uh and can leave things autonomously making the right judgments yeah it's making the right judgments yeah it's making the right judgments yeah it's much more critical now much more critical now much more critical now >> yeah I think so so the data in the >> yeah I think so so the data in the >> yeah I think so so the data in the context layer the human over the top context layer the human over the top context layer the human over the top making the judgments making the judgments making the judgments >> at the moment. Yeah. At the moment. >> at the moment. Yeah. At the moment. >> at the moment. Yeah. At the moment. Exactly. Yeah.