In recent times, machine learning has emerged as one of the biggest trends of our time. Study after study points to its massive potential and expected future growth curve. Worldwide spending on cognitive and Artificial Intelligence systems will reach USD 77.6B in 2022 as per data from IDC. According to Gartner, AI is likely to create USD 3.9 trillion worth of business value in 2022.
Despite all the doomsday forecasts about AI taking over our jobs, it seems as if every company worth its salt is quickly jumping on the machine learning bandwagon to bring in efficiencies and create new capabilities that can help improve their bottom line. There are also efforts in place to upskill and reskill the current workforce to bring it up to speed. But while all the dire predictions may have been off the mark, the fact is that AI and machine learning will have an impact on the way that business operations can be optimized. And not many companies understand exactly what this impact is likely to be.
For any machine learning or AI tools to work its magic, the key is to a) understand what machine learning can and cannot do b) ensure that your business is prepared from an analytics point of view to use the machine learning tools for optimization of processes For businesses to accrue the true benefits of machine learning, they need to ensure that they have first put their house in order. Automation applied to an efficient operation will magnify the efficiency. However, machine learning cannot fix a broken process. Automation applied to an inefficient operation will magnify the inefficiency.
So, if you’re looking at machine learning as some magic potion that can transform your business and unlock all growth opportunities, with all things remaining constant, then you are in for some disappointment.
Instead, focus on the things that AI and ML do well. First, it will enable you to analyze your data and gain unprecedented value from it. It will help you make better, well-informed decisions with real-time actionable insights. It will enable you to automate several processes. It will allow for massive personalization at scale. It will help you gain a much better understanding of your customers, suppliers, and the market at large. Machine learning can give you an ‘intelligent eye’ that can detect anomalies which are invisible to us humans. This can help optimize industrial production, for example.
Even before you venture into using your first machine learning tools deployment though, here are a few things to do first to ensure that your company is truly ML-ready.
The Right Set of Data
Scan your business thoroughly and find a set of processes or tasks that seem to be carried out regularly or frequently. Study how you are making your decisions. What data is powering those decisions? Also, who is making which decisions? How easy or difficult is it to find that data that empowers each decision? Remember that this is the very data that will feed into your machine learning algorithms. So, ensure that your processes make sense and are logical.
Instead of shuttling between different tools to deal with different aspects of the machine learning applications, using a single comprehensive tool with can help streamline the process considerably.
Start Small and Simple
For your first project, don’t focus on optimising an entire process such as logistics. Instead, try to start solving specific use case cases such as shelf-life analysis, route optimization or demand forecast. Similarly, ‘how do I make my customers happier?’ is a vague place to start. Instead, focus on simple problems with well-defined problem statements. For instance, start with a fairly logical process that you would like to automate.
In several cases, machine learning may not be a complete solution, but it might still be able to augment an existing solution to make it more efficient or more effective. Always keep the problem at the centre, not the technology. Focus on specific use -cases of processes that can be optimised using the right tools.
The most important point to remember is that AI/ML needs to be part of a well-thought out long-term strategy that is likely to take time before it is formulated properly. By focusing on getting your data in order, starting small, and inculcating the required patience and flexibility, organizations can look at accruing maximum benefits of this technology.
The author is Co-Founder and CEO of Mate Labs, a B2B AI company which was the only organization that was selected to represent India at Google Demo Day Asia 2019.