First of the questions which we have today which was actually submitted by tina and the question is who needs to have a hand in developing an ai strategy it's obviously c-suite or better as a bottom-up proposal what is the life cycle duration of such a strategy plan given the speed of change in this area so we're going to start with you simon for this one
Look this is a great question and um i think the the probably the common sense answer is probably going to be and certainly when i look at my own experience is strategy work is often one of these things that it starts at the top but it also starts at the bottom and it somehow meets in the middle and you know so you you do need to have a strong strategic awareness at a board and see cxo level of of the potential of ai and i think many corporations actually have that um but equally you need to have very solid practitioner level experience and somewhere in the middle it meets with sort of the you know what's real and what's what's actually worth doing now and i think most people's strategic story starts with sort of a conversation happening um and often it's a conversation around a particular problem or particular opportunity you know sort of a high value problem that we can use ai to drive automation or to drive up a customer experience or to improve some sort of operational efficiency you know you go looking for a good problem and you use that problem once solved to then build capability and to then mature the thinking and ensure the conversation at the both the cxo level but also at that operational sort of tool set level
I'll start off first with i think that this question is really highly specific to the particular business in the particular context um i'll be talking about sort of bottom-up and top-down sort of approaches that we apply and i think that's most relevant in another question that we've got coming up a little bit later um but i wouldn't just like to say i think um accessibility of the technology is really a major factor here so anybody with a bit of time a credit card a little bit of effort can really start having you know is really stuck in generating some knowledge here potentially in this space and and can begin to apply some of this to their their particular business but also begin to understand whether this particular technology is relevant to their particular business i think democratization of technology here is really quite relevant when it comes to dl
okay michael would you like to add to that
Yeah and i think um some really good points been touched on here so if you're developing a strategy for business um you know if you look at a bit of a swot analysis obviously the line of business needs to be a key part of this because you need to tackle this in line with business outcomes but as someone pointed out you do need a strategic awareness of what's going on but you've got to be careful here balancing um strategizing or you know paralysis viral analysis you're the perfect strategy in context with what you can actually get done so sometimes getting started and learning from that process especially if you're early on the journey is a really key thing understanding your technical your capabilities your data readiness and how this works for you sometimes those learnings can feed really powerfully into a strategy as well and that strategy can evolve over time so get started see how you go uh in line with specific business outcomes which is where everything should be aligned
Yeah um like everything at the moment i think that we should be focused on customer outcomes and customers experiences so if ai is driving towards those key business value outcomes you're going to be more successful because you're going to be able to measure those results in terms of you know how your customers feel about those services and offerings you're developing and like michael said this is an iterative process right ai is different in the fact that you know in things like auto ml which is machine learning of the model you know you are feeding back the results constantly into the machine is learning how to refine the model you've got so getting started with ai and having a sound um use case and then understand that you are going to be constantly changing that anyway and the and and the way you develop that over time the things you use it's going to be constantly changing that's the nature of the beast.