by Chris Pehura — C-SUITE DATA — 2024/03/14
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It’s been a long time coming. We are finally in the AI tech disruption, the tech revolution. AI, a brand new wave of technologies is going to be on the market that’s going to raise the level of competition, performance, and quality of products. But here’s the thing. AI is not the first tech disruption of this nature.
There were others. Looking back there was the mainframe; then client-server technologies; then the internet hit the scene; smart phones, which allowed people to collect large amounts of data which led to Big Data, and that Big Data helped establish Artificial Intelligence — and that’s our next disruption.
But despite what many say, the AI of today is the same as the AI of the 80s. Only the memory and processing power have improved but not the technologies. At least, not their fundamental nature. But here’s the challenge when you have a new wave of disruptions. They follow a similar pattern. If you look back at all the different waves you will find that people follow the same approach when they deal with a new technology. They ask — “do we do this? Are we ready to do this?” And when they do it they retrofit the technology in a way that removes the benefits of that technology. They force it to work the way that they’re used to seeing things work. It makes no fundamental sense to adopt the new technology when you’re just going to reinvent the wheel with a new technology. And this is going to happen with AI.
When adopting AI you’re going to have to look at these past waves, and how companies successfully adopted those new technologies and made them work within their businesses without losing the benefits of the technology. Talk to the people that were there. Pick their brains on it because what they did back then is still applicable now for what we’ll do with AI. And the core fundamental practices are all centered around business engineering and business re-engineering. That is a cornerstone of making Artificial Intelligence work within your company.
Business engineering is where you optimize the business, where you optimize the core processes of the business to help reduce costs and increase the efficiency of revenue generation. That’s in essence what business engineering is. AI has some strong parallels to business engineering because it too can do the same things. It optimizes solutions to reduce costs and increase revenue generation efficiencies. The thing is when incorporating AI, you can’t think of these core processes as steps or activities where people would need to meet and serve. You need to flip it. You need to think of people interacting together in a chain and how they’re collaborating. That way you will find that that’s where AI fits in. It fits in between how people interact and collaborate.
When you don’t think of core processes as being an interaction chain of people you’re going to run into problems when adopting AI. What the business is going to do is swap out their old technology and just plug in the AI — and turn on the process, keeping the rest of the process the same. And that’s not a successful implementation of AI. You’ll force the AI to work roughly the same way as the old technology. Maybe faster, but that’ll be it. You’ll lose a lot of the benefits.
AI is Artificial Intelligence and it works best when complemented with Human Intelligence, especially every step of the way. AI makes mistakes. You need a human there to course-correct those mistakes. So, it’s best to think of the core processes as an interaction chain of people and then complement each person in the chain or role in the chain with AI.
Once AI is adopted into a company I have some major concerns. There is always pressure to change the workers that interact with the AI. Some companies figure that the AI knows a lot about the business and that the AI needs to be supported by less knowledgeable people. Others see the AI as being very technical and so they hire more technical people to work with the AI. In both cases, the AI can make mistakes and neither tech nor less knowledgeable people will see that it made a mistake. You need someone very knowledgeable in the area that the AI is working with to tell you, intervene, and say that the AI made a mistake. It doesn’t matter what it is — executive management, legal, human resources, training. The AI makes a lot of mistakes and each worker should be given the expectation that when the AI gives you something they’re going to have to restructure it, reassemble it, and clean it up before it makes its way to the rest of the company.
It’s an extremely good idea to have several training sessions while you’re adopting AI in your company. The first set of training would be around your culture to see if your culture is ready for AI.
Is there cultural resistance using AI?
is there a fear of AI replacing jobs?
It’s a good idea to have a training session or a series of training sessions to help win people over so they will incorporate AI within their workplace. The second set of training would be around how to incorporate AI successfully within the core processes by seeing people as an interaction chain. Then the third set of training would be around after the AI has been successfully adopted where people would become more productive and effective using the AI tools. The common thread through all this training is the application of AI.
You should be talking about how to apply AI successfully within the company. And the best way to do that is by talking about the limitations of AI and how to best get around those limitations. This is the same conversation that business people have about using AI. And, this is the same conversation that technical people have about implementing and refining AI. Having that training gives a common language, a common way of thinking so that business can talk with the tech people to provide stellar AI solutions in house.
When doing something new, especially when it involves AI, it’s a good idea to bring in consultants. The thing is that you do not treat the consultants as another pair of hands to build your AI solutions. You use the consultant in an advisory capacity. You have them lead. You have them scope. You have them direct. You have them train. You don’t have them getting their hands dirty implementing stuff. You want your people to do that. You want your people to build the skill sets so that you don’t need to bring in that consultant or other consultants later on to maintain your AI solutions. You want to keep as much of your knowledge in house as possible.
It is very important to prepare yourselves before you engage with the consultants. You would need to determine three things about your business. One, what are the core processes you want impacted and who are the key people that influence the success of those processes. Two, how do you think the processes are going to change and which key people are going to change along with them. That includes their responsibilities and their career tracks. And three, how do you think AI is going to get you there. Making three points clear will ground consultants and prevent them from boiling the ocean.
To sum up, AI is part of a legacy, a strong legacy of tech evolution and tech revolution. All the waves since the mainframe has built us up to this point where we can now do things that were just science fiction.
But the thing is what worked back then for a successful implementation of new technologies still works today. The same practices around business engineering and re-engineering, how to work with consultants, and how to conduct training. These are cornerstones for successful AI implementation.
Will you learn from past mistakes? Because you are likely going to ask th
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