What is the opportunity cost of machine learning? An opportunity cost can be defined as forfeiting all benefits associated with a route which is given up when a decision is made.
We say that, when an option is chosen from alternatives, the opportunity “cost” is the cost contracted by not enjoying the benefit associated with the best alternative. It is the loss of potential gain from other alternatives when one alternative is chosen. Machine Learning and Artificial Intelligence are a relatively new process. Hand in hand with data science, they will shape the future of every business and every life (McKinsey, 2018).
There is an opportunity cost behind everything. In our personal day-to-day life decisions as well as in our business day-to-day decisions. As a series of trade-offs, they affect both the bottom and top line of enterprise.
To put things into context, think about Blockbuster.
After years of being the incumbent in the movie rental market, it failed to innovate and to transform its business digitally. It left its role as a market leader to Netflix (after failing to acquire it when it had the opportunity) (HBS, 2017).
Blockbuster’s opportunity cost of not innovating? Bankruptcy and approximately $6 Billion.
As well as the opportunity cost for a business, it is important to observe the scalability and innovation that Machine Learning and Artificial Intelligence can have in a business – try to see the whole picture (PMLR, 2017).
AI, ML & Data Science are innovative practices that are fundamental for any business to keep their competitive edge and to gain more market share. They provide both an opportunity to scale up and optimise your practices.
Adding Machine Learning and Data Science practices into a business process is a choice that every business might have to face in the future and that holds a strong opportunity cost.
But what is the opportunity cost of not starting to add Machine Learning into a process?
Note: This blog aims to spark a reflection on businesses in general or more specifically, your business/idea and not to give a comprehensive evaluation of ML impact. Hence, we have identified 3 problems/scenarios that can give you a better understanding of the factors that can influence your Machine Learning Application.
According to a 2019 TechRepublic study, 53% of companies reported that they don’t have a clear understanding of how AI or ML could benefit their businesses.
Information asymmetry can lead to huge market disequilibrium as players who know more will then eventually become monopolists (ScienceDirect, 2014).
Companies understand that ML will be necessary for their survival, yet they fear high costs that can lead to shrinking budgets or worse, as they do not fully understand it and need to outsource it.
This fear is rational, as there is a level of trust involved when implementing ML solutions into a business (Springer,2019). ML is only truly effective when provided with huge amounts of data, so making sure that your data is backed by a solid security infrastructure is the key to overcoming this fear.
Machine Learning frameworks exist on this specific purpose: to overcome information asymmetries that naturally generate in the market and at the same time to reduce the shrinking budget fear. Step by steps phases are set in place to help businesses and data scientists in order to establish goals and give results in a timely and trustworthy manner, in order to keep the process going and the company innovating.
As big data doesn’t mean quality data – ML can work initially with a smaller amount of data in order to find insights and assess the relevance of the findings.
Competitiveness & Competitive Advantage
ML is becoming more and more scalable for business processes, translating in an exponential growth in accessibility to every business no matter the size or scope.
By gaining more competitive advantage businesses can increase their market share either globally or within their niche market. The sooner a business will move the better it will be and the more competitive it will become. Conversely, if it decides to prioritise different investments, it can risk to miss the opportunity and make the wrong choice – increasing its opportunity cost.
An easy solution to prevent this issue and make the most of the available time is to understand what goals need to be achieved with ML. This is crucial in order to obtain meaningful results. Without specific objective setting – it is like firing a weapon in the dark – you don’t know the results nor what you are looking for either. As data scientists, we aim to help you to set these goals in order to give you the possibility to better understand your aim and how to obtain results.
The Risk/Reward Trade-off
Although businesses are now starting to apply Data Science and Machine Learning practices more and more, these integrations are still often part of R&D project developments.
Many companies do not have an R&D department or any budget allocated to applying innovation in their processes. There is always a risk-reward trade-off in investing in ML and the opportunity cost of it can be a limit to further developments. Yet, on a survey conducted by MIT, out of 3000 executives – 85% believe that ML and AI will impact their businesses (MIT, 2018).
Moreover, according to a study conducted by Deloitte, the median return on investment from the use of cognitive technologies is around 20%.
The issue is that many firms believe that Machine Learning will require a huge amount of investments and an uncertain return on the latter and a consequent shrink in their budget and arcane technologies, available only to Data Scientists. The reality is that this is simply not true. Most of the tools needed to create and adopt can be found in any office, without any relevant sunk cost affecting the investment (CIO, 2019).
As an innovation agency, we focus on value and deliver through short, iterative sprints to maximise the return on investments. This often starts by helping businesses understand how technology can help and how their data can play a big part in their processes.