Exploring What AI Is And Is Not -- And Making It Work For Your Company

Post written by

Tyler Bowman

Director of Cloud Solutions at Velosio, accelerating growth of businesses through ERP, CRM, and cloud technology.

It’s easy to get overwhelmed by the term "artificial intelligence (AI)," let alone its ramifications for the future of — well, pretty much everything. It’s a big topic for my industry, which is resourcing and deploying business applications to and from the cloud --many of which employ AI. I’ll venture to say it’s a big topic for yours, as well.

My realm is the cloud. Though it may not be the final frontier, it certainly is my frontier for now. The term “AI” is commonplace in my everyday speech. I can’t even tell you how many times I say it in a day. It’s a lot.

As we go there, though, let’s consider a few important parameters: AI is only as good as its code writer(s). One size does not fit all. It’s not right for everybody. I recommend you venture into AI with specific end goals in mind. And stick to your plan.

In short, deploying AI techniques can be effective for your company, and you don't have to jump over the ethical shark or get overwhelmed by this frontier. You can navigate this. You just need to pinpoint where to start. And by start (insert MythBusters spoiler alert), I mean that there is no one-size-fits-all approach that requires huge disruption to your organization. Baby steps can result in positive gains, too.

AI Versus ML

When you consider AI for your company, understand this — AI and machine learning (ML) are two different things. That’s an important distinction. While AI and ML are frequently grouped together (they are often referenced as AI/ML), they are two different products and two different technologies. They go hand in hand like finance and enterprise resource planning (ERP), but they are technically two different entities.

If AI is the thinker, then ML is the doer. Imagine a dog park. It has a fence, and you want to identify all of the German shepherds (or your favorite dog breed) in the park. AI frames the concept, while ML is looking for the German shepherds. While your ML is out there looking for a particular dog, it’s refining its search and focusing in on the end goal your AI coding created. And the fence around the park is an important parameter. It’s important to place boundaries on your AI goals so that you can better control your ML.

Only As Good As You Make It

Once you decide on your focus, make sure your inputs are clear. If you’re not inputting clean and responsive data for your code, you’re not going to get anywhere. If you’re trying to do lead scoring and enterprise risk management (ERM) but you’re putting in ERP financial data, you’re not going to get what you want in the end. That leads to wasted energy and dollars.

According to the International Data Corporation (IDC), spending on cognitive and AI systems in 2022 could reach $77.6 billion. That's more than three times the $24 billion IDC forecasted for 2018. While those numbers are huge, stay focused on your AI goals. Start small. Know the difference between what AI can and cannot do for you (at the moment).

The Ethical Conundrum

While the ethical considerations of AI are real, they are manageable if you stay focused on your project. Again, if you start small and know the differences between what AI can and cannot do for you, your ethical headache and questions will likely remain manageable. Don’t start with the self-driving car, for example. Just begin with a self-starting engine. Begin at the beginning. Create a solution for your customer response shortcomings. If you go for the car, then you have to deal with who is responsible if the car crashes, and you don’t want that — not yet. Stay in your lane.

Forgetting One-Size-Fits-All

It comes down to this: You probably don’t need one solution for AI. You need a menu of solutions to address different issues.

If you deal with AI as a very specific tool for a specific problem set for a specific department, you’ll be better able to manage your investment. You don’t need to integrate 43 different departments all at once.

There’s a safer and more ethical way of creating AI and pulling those data streams into a flash report where you can integrate the findings and then determine how to use them with AI, but you don’t need ML and AI for all departments and data streams.

As an example, when you contact customer support and you get a chatbot, there’s no one on the other side. You are talking to a specific AI bot that uses ML and a specific data set to give you an answer based on your responses.

Starting With Specific End Goals In Mind

Searching a database for the answer to your customer’s question (as in the chatbot example) is another great way to incorporate AI. You’re searching through a specific set of parameters, kind of like you’re looking for all the German shepherds in the dog park. The dog park has a fence, so that’s an ethical way of doing things. Keep your parameters clear at the beginning, and stick to them all the way through the project. This is key.

If you deal with AI as a very specific tool for a specific problem set, then it can be effective for you. I believe that’s a safer and more ethical way of creating AI. You pull necessary data streams into a flash report where you can integrate your findings, but you don’t necessarily need both machine learning and AI to accomplish this.

I think what it comes down to is this: You don’t always need one AI solution — you may need a menu of solutions. And for the best results, you should approach them one at a time.

Forbes Business Development Council is an invitation-only community for sales and biz dev executives. Do I qualify?
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It’s easy to get overwhelmed by the term "artificial intelligence (AI)," let alone its ramifications for the future of — well, pretty much everything. It’s a big topic for my industry, which is resourcing and deploying business applications to and from the cloud --many of which employ AI. I’ll venture to say it’s a big topic for yours, as well.

My realm is the cloud. Though it may not be the final frontier, it certainly is my frontier for now. The term “AI” is commonplace in my everyday speech. I can’t even tell you how many times I say it in a day. It’s a lot.

As we go there, though, let’s consider a few important parameters: AI is only as good as its code writer(s). One size does not fit all. It’s not right for everybody. I recommend you venture into AI with specific end goals in mind. And stick to your plan.

In short, deploying AI techniques can be effective for your company, and you don't have to jump over the ethical shark or get overwhelmed by this frontier. You can navigate this. You just need to pinpoint where to start. And by start (insert MythBusters spoiler alert), I mean that there is no one-size-fits-all approach that requires huge disruption to your organization. Baby steps can result in positive gains, too.

AI Versus ML

When you consider AI for your company, understand this — AI and machine learning (ML) are two different things. That’s an important distinction. While AI and ML are frequently grouped together (they are often referenced as AI/ML), they are two different products and two different technologies. They go hand in hand like finance and enterprise resource planning (ERP), but they are technically two different entities.

If AI is the thinker, then ML is the doer. Imagine a dog park. It has a fence, and you want to identify all of the German shepherds (or your favorite dog breed) in the park. AI frames the concept, while ML is looking for the German shepherds. While your ML is out there looking for a particular dog, it’s refining its search and focusing in on the end goal your AI coding created. And the fence around the park is an important parameter. It’s important to place boundaries on your AI goals so that you can better control your ML.

Only As Good As You Make It

Once you decide on your focus, make sure your inputs are clear. If you’re not inputting clean and responsive data for your code, you’re not going to get anywhere. If you’re trying to do lead scoring and enterprise risk management (ERM) but you’re putting in ERP financial data, you’re not going to get what you want in the end. That leads to wasted energy and dollars.

According to the International Data Corporation (IDC), spending on cognitive and AI systems in 2022 could reach $77.6 billion. That's more than three times the $24 billion IDC forecasted for 2018. While those numbers are huge, stay focused on your AI goals. Start small. Know the difference between what AI can and cannot do for you (at the moment).

The Ethical Conundrum

While the ethical considerations of AI are real, they are manageable if you stay focused on your project. Again, if you start small and know the differences between what AI can and cannot do for you, your ethical headache and questions will likely remain manageable. Don’t start with the self-driving car, for example. Just begin with a self-starting engine. Begin at the beginning. Create a solution for your customer response shortcomings. If you go for the car, then you have to deal with who is responsible if the car crashes, and you don’t want that — not yet. Stay in your lane.

Forgetting One-Size-Fits-All

It comes down to this: You probably don’t need one solution for AI. You need a menu of solutions to address different issues.

If you deal with AI as a very specific tool for a specific problem set for a specific department, you’ll be better able to manage your investment. You don’t need to integrate 43 different departments all at once.

There’s a safer and more ethical way of creating AI and pulling those data streams into a flash report where you can integrate the findings and then determine how to use them with AI, but you don’t need ML and AI for all departments and data streams.

As an example, when you contact customer support and you get a chatbot, there’s no one on the other side. You are talking to a specific AI bot that uses ML and a specific data set to give you an answer based on your responses.

Starting With Specific End Goals In Mind

Searching a database for the answer to your customer’s question (as in the chatbot example) is another great way to incorporate AI. You’re searching through a specific set of parameters, kind of like you’re looking for all the German shepherds in the dog park. The dog park has a fence, so that’s an ethical way of doing things. Keep your parameters clear at the beginning, and stick to them all the way through the project. This is key.

If you deal with AI as a very specific tool for a specific problem set, then it can be effective for you. I believe that’s a safer and more ethical way of creating AI. You pull necessary data streams into a flash report where you can integrate your findings, but you don’t necessarily need both machine learning and AI to accomplish this.

I think what it comes down to is this: You don’t always need one AI solution — you may need a menu of solutions. And for the best results, you should approach them one at a time.

Forbes Business Development Council is an invitation-only community for sales and biz dev executives. Do I qualify?

Velosio, accelerating growth of businesses through ERP, CRM, and cloud technology. Read Tyler Bowman's full executive profil...">Director of Cloud Sales and Operations at Velosio, accelerating growth of businesses through ERP, CRM, and cloud technology. Read Tyler Bowman's full executive profil...