With the world around us likely becoming data-centric, many businesses may not be able to leverage the use of it to the fullest potential. Machine learning can possibly help small to midsize enterprises (SMEs) make informed data-driven decisions and improve business operations.

Machine learning can potentially increase the productivity of the SMEs

Concepts of machine learning (ML)and artificial intelligence —a subset of ML— may no longer be very foreign to us. With its potential to boost the capabilities of enterprises and organizations, ML could be seeing widespread adoption across numerous areas of business and social life. 

According to a report by Fortune Business Insights, the global machine learning sector is expected to grow from US$15.50 billion in 2021 to US$152.24 billion in 2028, with an annual growth rate of 38.6%. The report also states that small and mid-sized enterprises are projected to increase their tech spending on AI and ML technology deployment.

What is machine learning?

Machine learning (ML) is technology that analyses data and distills it into classifications and groups to generate predictions that would otherwise be difficult to extract via traditional modes of analysis. In other words, machine learning is a set of techniques and technology that identify patterns in historical data to make predictions.

This differs from traditional methods —also called predictive analytics— based on a step-by-step, predefined process for converting input into output data. Some examples of ML in the real world are speech recognition, image recognition, virtual personal assistant etc.

How can the implementation of ML help SMEs?

With the world around us getting more and more digitized, the possibility of data leaks and cyber threats could also be increasing. This could mean that SMEs may be actively looking to invest in machine learning technology to safeguard them against such threats by identifying patterns of malicious activities. According to the 2022 AI Index Report, private investment in AI was estimated to be over US$93.5 billion in 2021, more than twice what it was in 2020. This section will look at how the implementation of machine learning can help SMEs.

1. Aids in data-driven decision making

Machine learning can potentially help SMEs make stronger decisions based on data rather than assumptions. ML may also help small businesses by forecasting their future growth using historical data. It can do so by analyzing the patterns learned from existing data to predict the behaviour of a system, thereby helping them base their decisions on the results obtained to improve their systems. 

For example, an eCommerce business can make inferences from consumer data such as online transactions, dates and times of purchases, as well as the modes of payment used to get insights about customer behaviour. They can then decide which customers they want to target again through marketing, and if many customers use online payments, then perhaps improve their digital payment experience etc. 

2. Helps automate mundane tasks 

SMEs may improve the process of attending to mundane tasks by automating them, like responding to repeat customer queries. They can use automated chatbots that operate 24 hours a day, seven days a week, without recruiting and training dedicated customer care personnel. This may help businesses not just by cutting the resource cost, it could also help them attend to customers at any time of the day. 

Automation can also help SMEs in many other areas like HR, accounting etc. For instance, companies can automate their email marketing tasks by setting the parameters of when and whom to send the emails, and set a system up to run this task automatically. When a receiver opens the email, the automated systems can also send follow-up emails. 

3. Can help improve cybersecurity 

According to an article by Forbes, AI and ML systems are being employed by enterprises to provide intelligence to their security systems. This can help SMEs prevent cyberattacks and respond to suspicious behaviour, which can further aid cybersecurity teams to be more proactive in preventing threats and responding to active attacks in real-time. As a result, using ML to identify data discrepancies and outliers can enable organizations to safeguard their confidential and critical data. For example, many email monitoring tools use ML to detect malware/viruses sent via phishing emails, compare them with regular emails, and report them to the concerned IT departments.  

Challenges of using machine learning for SMEs

Although the use of machine learning can help companies in many ways, there may still be areas for improvement. In this section, we will look at some of the challenges posed by the adoption of ML by businesses.

1. Insufficient resources available to SMEs

According to a report by ResearchGate, many SMEs face difficulty implementing ML technology compared to bigger businesses due to a lack of ML resources. The article also states that insufficient machine learning know-how in SMEs for identifying use cases —ways in which a user interacts with a product or system— of ML and implementing them could also pose barriers to its adoption.

2. Lack of historical data to run ML algorithms

Many businesses founded recently may not have a lot of data to run ML algorithms on. Moreover, other external factors like natural calamities and the COVID-19 pandemic can interfere with consistently storing and recording data. This may result in incorrect results if past information is not accurate. According to Jim Hare, Research Vice President at Gartner: “Disruptions such as the COVID-19 pandemic is causing historical data that reflects past conditions to quickly become obsolete, which is breaking many production AI and machine learning (ML) models.”

To conclude

Machine learning can potentially play an essential role for SMEs in the future and may see wider adoption among businesses of different spheres. ML can also possible change business environments and help boost their output and productivity. It would be interesting to see the newer challenges that ML could solve for businesses in the future with time and advancements in technology.

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