If You’re Not Leveraging or Considering AI in Some Fashion, You’re Already Behind.

It’s been said that data is the new gold. If you believe this critical concept like I do, then you should believe that artificial intelligence (AI), and machine learning in particular, will have a tremendous impact on the way we work. Though not new, machine learning is the next step in the evolution of data analytics and every company should already be looking at where they can take advantage of this transformational technology. If you aren’t doing that already, you’re behind.

Data Analytics too often is about looking backwards at what happened, while machine learning is about looking at the same historical data but with an eye into the future. It uses patterns in the data to help provide insight and recommendations and it can help automate tasks where historical patterns are a good prediction of the future. Machine learning is just one application of artificial intelligence, but it’s the most accessible and relevant as it applies to most enterprises. There is a lot of debate about how robots powered by AI might replace our jobs, but we’re still too early in the AI evolution to be worried about full scale replacement. Many firms are already taking advantage, augmenting their employees work and freeing up time for more knowledge based activities. It’s a long way before large sets of jobs are replaced, but AI can and should be used to augment and improve processes, while also enabling personalization into how each employee or consumer works or shops.

Machine learning also opens up the world of prescriptive analytics, which takes predictions a step further by suggesting actions based upon the predicted event. Just because you know something will happen doesn’t mean you’ll take the most meaningful action. Scale also becomes more attainable with AI. The augmented work, insights and predictions open a world of doing much more with less.

If you’re still figuring out how to get started, then don’t worry. Foundationally, it’s much easier today to start than it was just one year ago. The cloud has accelerated machine learning adoption and accessibility and it’s a perfect environment for machine learning. You can get a lot of compute for specific periods of time for model training, while the number and quality of the model’s available increases daily. All the major cloud vendors now make it easier to tap into their models no matter where your data resides, while also providing open API’s for integrating the results into usable forms. There are also a multitude of vendors available that can help you start with a small project as you begin your learning exercise. Like other new technologies, it’s smart to start small in the form of testing, learning, trying and discovering.

When looking at where to begin, think about what business problems could be solved by automating a routine task. Look at problems where understanding historical trends can improve decision making. As you approach the project, keep the Agile methodology in mind; identify the business problem, pick a short-term win that can be accomplished in 4-6 weeks. Take the Minimal Viable Product (MVP) approach and don’t try boiling the ocean on this. You need to try it, learn, iterate, and go through it again.

Some examples of where to look are:

·       Repetitive tasks – Are there tasks that staff perform that are routine that also generate transactions or other data sets? Do you get requests that are repetitive and where history is a good indicator of how to process these requests? Is there data movement between systems that are routine?

·       Providing insights – Are there key processes that are event based, where you might improve the outcome the next time if you had historical data that provided insights into the quality of your decisions?

·       Reducing Noise – Operationally, do you have notifications that generate more data than you’re able to easily sift through? Machine learning can help you get through the noise and clutter.

·       Improving the user experience – Are there processes in your operations where your employees are required to go through multiple steps to get help or generate requests? There are many use cases where machine learning can help employees get what they need quicker, easier, and cheaper.

·       Personalization – Leveraging user preferences and habits, the user and employee experience can be personalized to provide a better and engaged experience. What temperature do you like it in your workspace? Which conference room do you tend to use (or what is the most convenient). What food do you like the most, giving you alerts when it’s available in a nearby café?

·       What’s in your data? – Look at where you have a lot of data. Just looking at the systems or devices that are generating a lot of data can give you ideas for where to look. The more data you have to train and test the model is important, so start with what drives your current data analytic requirements and you’ll likely get ideas from there.

One last note about your data. I’ve written many times about data quality and the importance of data governance, and the topic of machine learning is a great example of why that’s critical. Hopefully you have a good data governance program in place and your data is somewhat clean. Data quality will be key to a successful AI pilot. Having said that, you also don’t need perfection to start. Just by starting an AI project will give you insights into your data and hopefully get you on a journey of organizing and improving the quality once you understand what you really have.

If you haven’t thought about starting an AI program, then you better start soon. I bet your competitors are already on the journey and you’ll soon be left behind.

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