Machine Learning and Artificial Intelligence is here. Is your organisation prepared to take advantage of it?

Every industry goes through technological advances every few years that affects all the constituents sooner or later. Less frequently, there is a change that affects all industries. Introduction of computers, mobile devices and the internet have reshaped every industry in the world.

New technology starts as an experiment followed by years of research. Early adopters struggle through viability issues but reap the most benefits as competitive advantage. Stragglers lose their edge and sometimes do not survive. Machine Learning and Artificial Intelligence are well on their way to become an integral part of everyday lives and business. Here are a few things you can do to position your company well to turn this possible threat into a massive opportunity.

Identify key decisions in your organisation that can be made based on data

Every piece of data that you come across will open new possibilities of insights and improvements. This can lead to effort and resources being spent on Machine Learning where returns are minimal or non-existent. While starting out, it is better to approach from the top and focus on the problems that will have the biggest impact on the organisation. What are the decisions that your executives make every day? Where would there be a great improvement based on decisions made on a lot of data? Pricing, Resource allocation, Cost Management, Hiring, Security, Sales and Marketing. Machine learning can be applied to all of these areas.

Look for existing data and understand it

You know there is a massive amount of data in your organisation. Only your organisation has it. It’s your collective experience waiting to actualise your potential. You have been putting off collating and organising it for years. Well, now is the time to dust off those shelves and hard drives. Focus your efforts on getting data relevant to your goals.

Data often exists in silos within organisations. Problems such as resource allocation are best solved with data from all parts of the organisation. Bring together data in a single place like a data lake.

Don’t discard unclean and unformatted data

Don’t be afraid of dirty data. Data everywhere has missing gaps, broken formats and accuracy problems. Data Science tools offer us ways to deal with data that is incomplete, partially accurate and unusable in classical analytics. Data that may not be reliable or complete enough for reporting actuals may have insights for Machine Learning.

Remember, data does not mean only digital numerical data. Machine learning can be applied to text, pictures, videos, audios and any new format that can be digitised. If there is a lot of print data, scan and digitise it. If there is a lot of Engineering data in those instruments, get it into your servers and organise it. More the data, farther back it goes, the better will be your machine learning.

Start gathering focused data and structure it

Don’t have existing data relevant to your goals? It is never too late to start. Start putting together internal and external data today. Even if you don’t plan on using Machine Learning right away, having the right data will put you in a great position to do so when you are ready. Data gathering is a time consuming process. Starting early allows you to time for checking errors and correcting course.

Gather feedback and responses

Machine Learning requires validation of decisions made. Feedback completes the loop for a learning system and generates more data to learn and validate the Machine Learnt model. This is why companies at the forefront of AI like Google and Facebook ask for continual feedback in their products.

Internally, predictions you get from your Machine Learning system need to be captured. you should get suggestions from Machine Learning and capture the accuracy/impact of the decision made by the system. These predictions made by Machine Learning should be plugged back into the data. Strong validation processes will also check if your Machine Learning model continues to perform well over time.

Build a Machine Learning team with a Machine Learning attitude

Start with a Machine Learning Champion. While it’s a role big enough to warrant a new hire, sometimes it is better to have an existing influential person lead your Machine learning efforts. Depending on your need and extent of the Machine Learning tasks, you may build a team by hiring or augment with Consultants.

Google made a shift from mobile-first to AI-first in 2016. Leveraging Machine Learning for Business needs you to change your approach to problem solving across the organisation. Spread awareness about Machine Learning across your organisation and garner participation in your AI initiatives from as many teams as possible.
strong>Start small but get started

There are many reasons to delay your entry. It requires a huge amount of data. It requires fresh and rare talent. Machine Learning can be an expensive affair if not done in a focused manner. But the opportunity loss could be huge if you do not start early. You may lose the competitive edge. Not starting at all is not a good option. A sweeping technological change of this magnitude could impact multiple industries and can often change the entire landscape of an industry. Ask Kodak why they did not pursue digital photography.

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