So how do you evaluate AI readiness within an organization? It's difficult. It's honestly very challenging to figure out if your company is gonna be ready to tackle something as big and hairy and scary as as AI is. And you can do this pretty methodically. You can look at this through three different lenses. The first being people, the second being process, and then the third being technology. And you have to look at this in that order. It's important. I'll get to why. You wanna start by looking at your people. Do you have your teams organized in a way that is intelligent? Do you have the structures of their job responsibilities and their job descriptions very clearly outlined? Do you know what your people are actually doing? And are they actually ready for a change like introducing AI into their business process? A lot of organizations, they are used to doing things the way that they've always done them, whether that's paper based or manual or, you know, maybe done in just an electronic fashion. Maybe you're a little bit more modern and tech savvy business owner. You have to take a hard look at your people and understand what's gonna be the lift from the change management side of things for this this project of implementing AI. Because implementing AI really is a project. It's it's no different than implementing any other system as you would have previously. And McKinsey has published a research paper that says that seventy percent of change management programs fail to ever go live, specifically system implementations, not because of the technology, but purely because of people adoption on the client side. So you have to take a hard look at your people, and you have to understand what are their current skill sets, what are their capabilities. You have to map out individual plans for each of your different departments of trying to level them up and get them ready for this change. So coming up with a plan and addressing each of the individual skill sets is gonna go a really long way. Also identifying within your people who the champions are gonna be is really important. It's not just gonna be you as the business owner or the CIO or IT director that's gonna be pushing this throughout the organization, you're gonna want people that are on your team that are excited about this technology that they can drive this through the rest of the organization. So identifying those champions up front, getting them really excited about the technology, showing them proof of concepts, and things like that, it's it's gonna go a long way as well. And you wanna look at how ready those people are as a measure of your evaluation if you're ready for AI or not. The second aspect is gonna be on the process. Within every single business, there is a process that you go through to perform something to be able to have an order come in and then collect cash in the end, or to be able to have a service performed and then cash collect At the end of the day, you can break down any business into a series of processes. And how well defined you have those processes, that's really gonna determine how ready you are to adopt AI. If you just have Janet who works at your company that she knows everything in the business and she essentially is your repository of processes, it's gonna be very difficult for you to apply AI to that scenario. So you wanna make sure that you have things documented across standard operating procedures of, you know, when this happens, then I need this to happen. So triggers and actions across your entire business. Getting that very clearly outlined from the offset, that's gonna enable you to springboard your AI usage because you're gonna be able to tell the AI exactly what you need to do along the entire way. And a recent MIT study came out that shows that ninety five percent of AI projects are failing to meet expectations of customers, which is pretty dramatic. So having these processes fairly clearly defined and written out is gonna be a huge enabler of success for you with these these AI implementations. And then the last thing that you wanna look at in all of this is the technology, and that's both the data and the system side of things that you wanna be analyzing. And starting with the data, if you're not capturing data in some sort of system of record or some area, like, if you're not you know, even if it's, putting things in filing cabinets, at least there's data there somewhere that you can capture capture and scan in with OCR and things like that. But if you're not capturing data on your business, then how do you expect AI to make decisions or perform processes based on data? It's just not gonna be possible. IBM estimates that bad data cost businesses three point one trillion dollars per year. So what you wanna do is you wanna look at those areas of where you have data in your business. Do you have clean data? Do you have reliable data? Like, what's the integrity of that data? If you can't trust it, then you're not gonna trust the AI then either. And so you wanna make sure you have a solid framework of your data and that you're collecting these things and putting it into a a manner that is reliable and accessible. Accessible. The other side of things is the system. You wanna have transactional systems that are accessible by modern AI applications, meaning that they need to be cloud enabled or AI enabled. So that way these AI tools can hook in and be able to understand what's going on within the system, perform actions on your behalf inside of the system, or do inquiry down into those systems to pull things out. If you have a system which is maybe just paper based, you're literally on a ledger, you know, writing out what your chart of accounts or your financial reports are, AI is not gonna be able to adjust that information or perform actions on your behalf. It's not gonna pick up a pen and and write down the ledger for you. You need to have a system that is cloud enabled or has modern accessibility into it. So looking at things like modern platforms like Microsoft or within, you know, any any modern application really is gonna have these API accessible features. And so you wanna look at it in this way. You wanna start with the people. You wanna go look at your process, and then you wanna look at the technology. And you can look at all three of these main categories as a score and create scorecards across each of how ready you are. And your goal should be able to max out that score. So you should have your people who are excited about the change. You have your power users or your champions identified. You have your organization really mapped out in terms of, like, your org chart, your job descriptions, what you guys do that is all clearly defined. And then within your processes, standard operating procedures across all of your different departments. So what are all those people actually doing, and how can you do this on repeat, those triggers and those actions across your entire business. And then lastly, all your technologies, so the data and the system side of things, how accessible is that data? Are you capturing all that data, ranking that within a scorecard? And then the same thing on the system side, how accessible are those systems? Are those systems about to go end of life, and are they no longer gonna be accessible by AI? You wanna be mapping those things out as well. All of this is really gonna give you a score on how ready you are for AI. And doing this now is gonna set you up for a massive scaling foundation for the future, scalable foundation. You're gonna be able to take advantage of these tools much faster. AI implementation is actually really fast when you have all of these things figured out. When you don't have all these things figured out, it's the same thing as previous system implementations where you gotta figure out all of those things so then you can add in the new system. So I'm excited for you to take advantage of it and follow for more tips.
