ElectrifAi
July 8, 2021

What’s the Best Ai Fit for Your Business?

If you have decided to pursue Artificial Intelligence (Ai) to optimize and grow your business, congratulations! You are on the right path to remain competitive in your industry.

The next step is to consider how you will implement Ai within your business. ElectrifAi’s CTO, Luming Wang, discusses this topic in The Consulting Ai Podcast from Emerj. Let’s review the main points covered and, if you want a deeper dive into the topic, listen to the podcast!

Action, Strategy, and Roadmap to Ai

What is the best course of action, strategy, and roadmap for you to take in your journey to Ai? One of the first things to decide is will you custom-build a machine learning model or use a pre-built off-the-shelf version that will allow you to get started using the benefits of Ai and machine learning right away?

At ElectrifAi, we talk with potential clients to decide if Ai and machine learning is the right solution to solve their business problems. We are very upfront and honest – if our solutions are not the right fit for you, we will let you know. After all, our success rate is ~95%!

If you choose to proceed with pre-built machine learning models, you can quickly pivot in another direction if the model isn’t producing the expected outcome. Building custom-made machine learning models from scratch, though? You would find that out months or years later.

Really understanding machine learning is the best way to not fail. How do you do that? By working with experienced data scientists who are domain experts. Machine learning is a powerful tool and you need to understand what it can solve and leverage the best practices in the industry.

For example, domain experts know what problems are prevalent in the industry and can quickly find patterns in the data. The machine learning model uses those patterns to output recommendations that stakeholders can act on to solve business problems.

Determine Business Problem

To find out if our pre-built machine learning models are right for your business, we must first get to the root of the challenges the business faces. We are experienced at quickly understanding your priorities and pain points.

We see tons of use cases that give us the experience necessary to solve problems. Sometimes we even know the final answer already! For XYZ problem, we can help you solve it with machine learning. Here is your investment amount, here is the uplift, and here is the return on investment (ROI).

The potential client leverages our experience to know what success looks like, how long it will take, and what return they can get for using pre-built machine learning models versus building the models from scratch.

Before diving into solutions, however, first determining the business problem is key to being able to get that amazing ROI. And we are really good investigators.

Delivering Value

ElectrifAi pre-built machine learning models deliver better value than building models from scratch as we have the models ready to go! We don’t need client data to get the model working. Client data just fine-tunes the model for specific data sets.

Now, we understand that some use cases must be customized as even in the same industry companies can have different requirements. And if the client does need the model to solve a specific problem, we have a process for that!

Introducing the Machine Learning Model Factory. This factory is not a physical factory, of course, but it helps to explain the way our models work. The following diagrams give a great explanation:

Electrif Ai Pre built Models Graphic

Electrif Ai ML Model Factory Graphic

Matching Problems to Specific Machine Learning Models

We look at each client’s problems and see what kind of classification we can investigate. With the largest library of machine learning models available, there is a good chance we already have what you need and can begin right away.

There are a few things that need to happen first to get the best match.

One is to test the model with the client data. Even pre-trained models that do not need client data to work, you still have to test to ensure the assumption is correct. Yes, this model will work to solve your problem. Or no, let’s look at another option.

Pre-structured models are based on business objectives. For example, whether you are seeking to maximize your revenue or profit, sometimes those two objectives are aligned… but sometimes they are not. The client must tell us what their specific business objective is for the solution to be successful.

Plug and Play

Plug and Play (PnP), or things that can happen out of the box, depends on the specific use case and client expectation.

Take our Churn Mitigation (or reducing the amount of customers that leave the company) machine learning model as an example.

Churn Mitigation can be a PnP model that builds on the common data set. A business will have customer profiling data, customer behavior data, past purchase history, etc. And leveraging that data you can build a PnP model.

For most small companies, they don’t have complicated use cases and that model works really well as a PnP model. But mid-sized or larger companies will have specific scenarios that need to be customized. It really depends on the expected uplift and use cases.

Conclusion

There is a lot that goes into preparing Ai and machine learning implementation. Determine your business problem to ensure the correct models are used and your business will reap the rewards. Delivering value is easy if you have a partner like ElectrifAi who has years of experience providing business results that matter.

If pre-built machine learning models meet your business requirements, then you should buy! Our models are proven to speed up your time-to-value, reduce your risk, and lower your costs.

Want to learn more? Luming discusses more use cases in the podcast so listen to find out even more about how ElectrifAi can help you with your machine learning journey!

Contact us directly to request a custom demo.

You have data, we have solutions!

Find out what ElectrifAi has to offer by filling out the information below.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.