Preparing your business for AI

AI Readiness Check

So you want to start integrating AI and Machine Learning into your company’s products or workflow. Well you’ve come to the right place. In this article I’m going to break down some of the key questions to ask yourself to help you decide whether or not your company is even AI/ML ready in the first place. Hopefully this should save you not only time, but also money when you’re to start this journey, you won’t have pay someone to tell you the things I’ll be sharing in this article for free!

Define your problem and data

For any machine learning solution we have to start with the problem being solved and the data that is available. The problem and data are heavily inter-related. Data can often guide you to solutions you didn’t realize you could create and the problem you’re looking to solve will often dictate what kind of data is necessary. Picking one or the other should be your first priority.

I have a lot of data, what do I do with it?

If your company has a ton of data that has been collected for years, you’re probably wondering what is even possible with Machine Learning/AI, how can I effectively employ it?

If this is the case, you can start to ask what kind of problems does your data solve. Take for example you’re a house construction company. You’ve built 10,000 homes and on each you’ve collected house details like sq. footage, # of rooms, etc. as well as demographic data on where you’re building each home. Well with that kind of data you can very easily begin to imagine a product like a pricing sales engine that takes in all of that data and spits out what you should be pricing each home at to maximize revenue.

This is also an excellent time to bring on a ML/AI firm to help evaluate your data and work with you to figure out which custom solution best fits your business needs and objectives!

I have a problem I want to tackle, how do I set myself up?

In this scenario you have an exact product or solution in mind you’d like to build. Maybe that’s a database that supports natural english language search (semantic search), or maybe it’s that real estate pricing engine we were discussing previously.

Whatever the solution is, you must now walk backwards to figure out what data you need to solve that problem. A general good rule of thumb is, the more data the better.

If you want to build a recommendation engine for example, try to think about what data points would be most relevant to your outcome. Machine learning excels at finding hidden relationships between data points, but generally speaking, it’s a system that’s able to take what we intuitively know and generalize it across inputs. In plain english, the data is most obvious to us in terms of importance (number of rooms in a house dictating price), will most likely be highly important to the end solution.

Once you’ve figured out what data you need, the next step is just to start collecting! Collect as much data as humanely possible and take some time to setup your databases correctly to ensure it’s as easy to use for machine learning applications as possible. (Article coming soon on how to do this properly!)

Using a ML/AI Firm

Figuring out how to effectively deploy machine learning solutions can be tricky. Particularly when you factor in best practices around storing and collecting data, the custom nature of each solution, and the impact these solutions can have on your business.

I often recommend to business owners who are looking to break into this space, to actually just go ahead and hire an ML/AI consulting agency to do this initial setup and exploration. A lot of people believe that only after you properly setup your data, or have formulated what you want to build is the best time to bring on an agency like this, but I believe bringing in professionals as early as possible is the best way to set yourself up for long term success.

AI/ML agencies will generally do an initial exploration phase where they work with your business on figuring out exactly what kind of data is available and how to employ it most effectively. This usually comes with a data analysis component, which is a precursor the full ML/AI pipeline. These types of engagements generally cost around 25–50k to do and take place over a few months.

Conclusion + Shameless Plug

AI and Machine Learning are here and they are disrupting industries and businesses every day. It is in the best interest of every business owner to think about how to prepare themselves for this new wave of technology. This often starts with something as simple as ramping up the amount of data your business collects around its products/offerings. But I challenge you to really start to question where you can leverage this powerful technology within your business.

My agency, PressW, has been helping businesses answer these questions and begin to build their ML/AI futures from the day we opened our doors. We have helped businesses from healthcare, oil and gas, to construction, find unique ML solutions within their business that have been responsible for huge revenue and efficiency improvements within their business. We’re happy to sit down with you for a few hours (free of charge!) and discuss your business, your problems, and see if ML/AI is a right fit for you. If you or anyone you know would stand to benefit, please don’t hesitate to reach out to tarun@pressw.co!