by Chris Pehura — C-SUITE DATA — 2024/03/15
Email us at contactus@csuitedata.com to see how we make your plans work.
When getting into AI, you can’t just buy the AI tools, get the data, and then expect it to work within your company. There’s more to AI than that. It’s not just plug-and-play. It’s not like other technologies where you could just bring it in and start using it. There is an evolution to AI. There are journeys that you, your company, and key people within your company need to take to better understand AI to adopt it effectively, and to have it perform for your company. AI has been around for decades. It’s been in pockets here and there at various companies. Companies were able to reach the AI step using their existing technologies effectively. Because if you don’t do that you’ll just end up with a souped-up version of the technology you’re currently using now. And it’s going to lose all the benefits of what it means to be AI.
You need to be good at using your existing tech. If you don’t your AI is going to act weird. Fixing that weirdness is requires a heck of a lot of work. You can’t get away from that work. Some people say you can use deep learning instead. That doesn’t save you. You still have a lot of work to do. You’re not ready to do AI.
There are a few things you have to ask yourself before you get started. You have to be ready for AI. Are you good at capturing information, complete information? Because your AI is expecting that. Any gaps in that information is going to make it act weird. What about tracking measures, details, things like that? Are you keeping track of the tracking measures? The AI doesn’t want any gaps in that either. You also need these measures because you have to fact-check the AI, confirm that the AI is doing the right stuff. And what about delegation? How do your people feel about delegating to another person? If people don’t like delegating are they going to like it if they delegate to an AI? Probably not. And what will happen when the AI starts acting weird and people start digging into it and then finding out all the dirty laundry, all the bad data that resulted in the AI acting weird? Things that people don’t like to share will generally come out. And then there are those personalities, the key personalities within your company. You know them. How will they respond to the AI? Because everyone’s going to be looking at them and making up their mind on the AI based on how they see them act. Are they going to be fearful? Are they going to advocate it? Are they going to have it as part of their major vision? Or are they going to see it as just an AI version of the existing tools and technologies and it’s just a fad?
You did your due diligence and you feel you’re ready to start adopting AI. You need a plan first. Something called a capability maturity model or a strategy map. Something that conveys improvements over a period-of-time in specific areas of the company.
For instance, culture. How is culture going to change and improve to support AI? How is AI going to influence the culture? Then there is organizational structure. How is it going to change during the AI adoption and successful use of AI? What about the core processes? How are they going to change? How is AI going to fit into all of those things and provide opportunities to be able to have the business change successfully?
You’ve nailed down your plan you know you’re true north for AI. It’s time to talk about the tech stuff that’s changing. Business Intelligence. That’s a broad term. When I say business intelligence, I’m talking about data, the management of data, the movement of data, the structuring of data, and the higher processing and performance of going through data. Business Intelligence is a bottleneck to AI when BI is not optimal. AI needs the data and needs the data in a timely fashion. That’s mandatory for AI to work. You want AI to be working off the most recent data. To help scope down and optimize your Business Intelligence use the portion you use to keep yourself up during production. Use that and the historical record portion. BI is really good at these things and AI needs these to be good too.
Once you’ve removed all the bottlenecks from your Business Intelligence for AI, you start looking at your Analytics. Analytics is an “analysis on the analysis”. It is a study of analysis so it should work hand in hand with AI. It just makes sense.
Analytics involves experts looking at the data, to find patterns in the data according to a set of rules and algorithms they use. There is always human involvement. Always human interpretation. Analytics can use the same sort of algorithms that AI can use. This makes Analytics a testing ground to see if the AI makes sense in its decision-making and recommended steps. Analytics and your experts will help confirm that they are indeed correct. When you are scoping down and optimizing your Analytics for AI, consider that Analytics is really good for planning ahead and planning ahead well. It also helps support keeping up with production, allowing Analytics to be a productivity multiplier for BI. Consider how Analytics supports BI and how planning ahead supports AI when you’re linking AI to your BI and analytics.
Once you have all the needed supports for AI to be successful in your company, it’s time to talk about AI and what AI actually does. AI is an accelerator. It helps speed things up. So you have to have a framework in place for AI to help speed up. You need the proper data in place. You need the proper guard rails. You need the proper people to help guide the AI so it’s going in the right direction. If you don’t, the AI is going to give you weird results. And it takes a lot of time to fix weird results. It’s better to address those issues with the BI and with the Analytics rather than the AI. It makes the AI more predictable. Use your experts and your analytics to test and measure the correctness of the AI. To confirm if the rules that the AI is operating by and the rules the experts are operating by, are valid based on the data. That is the power of AI.
Once you have the plan all figured out and you know how AI is going to fit in, you’re going to need to figure out how things are going to change to support AI and how AI is going to help support other things. It’s time to think about your employees. Think about the people who are going to be fueling your company using AI. AI is an accelerator. it helps them do what they’re currently doing. It helps them do it faster. So you have to make sure you know what they do and you have to make sure that they are incentivized to follow that track, to go that direction. What are the career tracks for each employee, the key people to support AI? You have to know them. You have to change responsibilities. You have to form new teams and communications. AI fails when it doesn’t have the proper employee support, the people who do the heavy lifting to make AI work.
Summing it up, when you want to adopt AI, you have to take a good look in the mirror and see if you’re ready for AI. As a business you have to ask yourself some key questions to really understand your culture and see if AI is going to be accepted by the key people within your organization. The worst thing you can do with AI is treat it as a souped-up version of the old technologies.
AI has a major impact on a lot of areas of the company. Your culture, Business Intelligence, your Analytics, your Project Management your processes, your organizational structure. There’s many avenues where AI can impact your company. Try to itemize them as much as you can and focus on each of them according to your priorities. Do your due diligence to make AI work.
There’s nothing magical about AI as long as you give it the data and its direction. It’ll get there in the fastest route possible. That is the true power of AI. That’s how we should best use AI. Not to replace workers. Instead, help them excel.
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