Machine Learning (ML) is a game changer for businesses of all kinds. It is critical for executives to understand more about how it could transform them. To that end, this article focuses on the two key points of why and when Machine Learning should be used.
There are three reasons why a company should use ML:
First, to create economic value: new ML based products can be developed or existing ones greatly enhanced. New intelligent or self-tracking cameras and scanners, recommendation systems for websites or apps, are just a few examples of these ML applications creating value. In numerous cases ML enables the findings of hidden patterns, that even the most trained human eye could not notice. One example can be found in the computational biology field, where ML algorithms frequently outperform doctors. For cancer treatment, there are ML models which recommend which drugs a patient should take based on numerous health features such as their blood types, tests results, scans and more that no doctor could consider as a whole. Another one is recent work done by academia and the major tech companies in the field of computer vision, where state of the art techniques are better at reading road signs than humans.
Second, ML can be used to reduce costs by optimising processes. This is referred to most frequently as automation. Many business processes are redundant or tedious and could be streamlined through using computers: filling in forms, handling client’s requests, filtering information, improving the knowledge transfer, automatically check the quality of products, and more.
McKinsey&Co recently reported: “In about 60% of occupations, at least 30% of activities are automatable”. Below, we show a graphic illustration from the US bureau of Labor Statistics, showing today’s automation potential thanks to AI.
Moreover, in many industries considerable waste is produced daily which could be reduced thanks to ML models predicting more accurately the demand and adapting the production and inventory levels accordingly.
Third, ML can increase revenues by enabling a more throughout market understanding. We see these applications as being mainly applied in marketing and finance. Customer segmentation, pricing or portfolio optimisation are some examples of such applications.
When taking companies from their inputs side (so as entities which buy assets), being able to better understand the value of the goods acquired allows to gain bargaining power or reduce risk. The bargaining power can come from the anticipation of the:
- supplier’s prices
- competitions willingness to pay
- market demand.
ML models can predict this kind of variations efficiently when provided with the right data. For example, in the energy sector or in heavily energy dependant industries, being able to optimize the production scheduling with price forecasts can allow considerable cost reductions.
Now, when taking companies from their outputs side (so as entities which sell assets), being able to better understand the willingness to pay of their customers will allow companies to capture value by adjusting their prices, packaging and quantities produced. ML allows to efficiently target customers and understand which product features bring them or make them think they acquire more value when buying the product or service.
These were three reasons and some examples that were based on a microeconomics perspective. However, another point should be mentioned before moving on to the “when” part of this article.
ML hits all industries in a disruptive fashion. Due to ML applications’ great return on investment and their high level of scalability, they make the competitive advantage they bring crucial especially for early adopters who will be able to capture an important share of the newly available value.
When should business apply ML?
As we just learned the reasons to use ML, it follows to need to understand when it is valuable to use it. Specific requirements need to be met in order to undertake the development of ML projects. First and most importantly, having the idea of the right ML applications to invest in is a necessity. Second, the right data needs to be available, whether it comes from internal company data or from public sources. And third, making sure the business impact of the project will be positive for the company is needed as for any investment.
Ideation of ML projects can be difficult when lacking the expertise. It requires considerable knowledge of the existing techniques and the kinds of data that can be used effectively to build various models. On top of that, business expertise is equally critical to come up with high value ideas solving pain points or creating new value for a business.
Several options are offered to companies:
- Read ML books and papers.
- Read articles that describe case studies such as the ones we publish frequently (http://visium.ch/blog/) in order to make connections and see what could apply to them.
- Contact us, and have us identify their highest-value opportunities in ML.
ML could not exist without data as explained in the previous article of this series. This data needs to be available in sufficient quantity and quality.
For quantity, the data on which the artificial neural network will be trained needs to be representative of the whole problem for the model to perform well. An ML based quality control image classifier cannot be expected to predict if a piece is defective or not just by showing it one way a piece could be broken. The model needs to have learnt a lot of the possible ways a piece could be defective. The more relevant the data the model will be trained on, the more accurate the model will be able to predict.
Regarding quality, the data labels needs to be correct. This same model will not learn differentiating faulty from working pieces if it is told that working pieces are faulty and vise versa. The data also needs to be as precise as possible. If the images of the pieces to classify are blurry, it will be hard for the model to understand which features make a broken piece broken.
The data can come either from businesses: internal knowledge base, CRM data or client feedbacks, ERP or production data (sensors, inventories, scanners, monitoring systems, etc).
But the data can also come from external open source datasets which are extremely helpful to increase the performances of ML models. There exists many of them but one of the most famous ones is a handwritten digits dataset which allows to train very performant models that are able to read more accurately than humans.
Oftentimes the data can be sensitive. This issue can be handled in multiple ways. The data can be anonymised or encrypted and private servers can be used. This is the case for Visium, which stores its clients’ data on own private servers in Switzerland.
ML’s value needs to be estimated in order to take the strategic decision to use or not to use it. When integrated in businesses, the value Machine Learning applications bring is actually most of the time easily measurable.
ML models are trained and then tested. The testing phase gives an accuracy (or other indications) which says that given new data, it will predict or classify it properly with this measured accuracy.
In the case of economic value creating and increase in customer/provider understanding ML applications, one way to estimate their value is to compute the extent to which the price and quantities are expected to be more favorable and what it will bring versus the cost of the investment.
In the case of cost reduction ML applications it is often simpler as it is enough to compare the cost structure with or without the application (without the application being most of the time the current cost structure). For example, having this model will allow the production line to have its pieces checked with a 95% accuracy at a cost of running a computer and maybe 0.1 second of processing per check. This can be compared to the current cost structure and a precise data driven conclusion can be made.
It should now be clearer why ML can bring value to your business and when ML could and should be used. We hope to have helped you understand that becoming an early adopter will greatly benefit your company and will spare you the risk of being left behind by the competition. Feel free to contact us for a free preliminary consultation about your business needs and potential application of Machine Learning.