The endless hype about artificial intelligence has led many business leaders to believe that machine learning is highly complicated and uncanny. Terms such as “neural networks” and “machines that can mimic the human brain” may seem incomprehensible, but conceptually machine learning is simple.
My favorite definition: Machine learning (ML) is using data to answer questions.
Now, why is it important for management teams to understand AI? Because AI/ML is going to transform every industry over the next 3 to 7 years. An industry is a collection of companies serving their customers. This means we will see disruption at an unprecedented scale, and the companies who adapt to these changes will define the future of their sector.
This does not mean management teams need to understand machine learning programming and low-level technical details – this is a role for other members of the company. However, because management team members operate at the higher levels of an organization, they are the ones who make key business decisions that affect the company as a whole.
If your company is to adapt and thrive in this new era of AI, you need to be in a position to make informed decisions about data, technology, strategy, talent, etc. Check out my blog post titled, “3 Questions To Ask Yourself Before Adopting AI In Your Company” to help guide you in your initial phases of AI adoption.
Start by looking at the data you already have. As a business leader, you don’t have to query databases and go into technical details. All you should do is keep it at a high level at first and simply assess what data you are currently collecting about your customers, about your operations, about your team, about your marketing, etc. Now ask yourself:
How are we harnessing this data to drive better business decisions?
Are we using this data to make predictions about future trends or behaviors? If so, what actions are we taking to use these predictions to add business value?
Are humans heavily involved in this process?
Could we create a system that automates these processes by leveraging the power of AI and machine learning? Think of this system as a production pipeline with specific sequential steps:
- Data is collected (see examples of data above)
- This data is cleaned and prepared
- This clean data is “fed into” a machine learning algorithm
- This ML algorithm learns the patterns and relationships within this data through a process called “training”
- This training process completes, and the ML algorithm is ready to make predictions
- We “feed” new data into this trained machine learning algorithm
- This ML algorithm makes a prediction using this new piece of data
- We use this prediction to take a specific action that drives value for our company and/or our customers
By simply asking these questions and thinking conceptually about a machine learning ‘pipeline’, you are already developing an understanding of how artificial intelligence can be used in your business. This understanding will help you make important strategic decisions for your company as you enter this new era of AI.
If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at firstname.lastname@example.org and I will be happy to help you.
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