The Why and How of Integrating Machine Learning in Business

machine learning in business

Advancements in automated computing technology are opening up new opportunities in the business world. Machine learning is at the forefront of the revolution. According to a Markets and Markets report, the machine learning market was worth $1.41 billion in 2017. Growing at an annual rate of 44.1%, the report predicts that by 2022, it will be worth $8.81 billion.

Due to its potential to increase productivity, speed and efficiency, it has already found widespread acceptance in the enterprise sector. In a 2018 survey carried out by MIT Technology Review and Google Cloud, 60% of respondents said they had already implemented machine learning in business processes.

These statistics clearly indicate the massive potential of machine learning and its popularity in business. In what aspects of the business can operators integrate the technology? What benefits can they expect to gain? How can they devise an effective implementation strategy? Let us consider the answers to these and other questions that you may need to weigh before determining whether or not you need to integrate machine learning into your business.


Popular Applications of Machine Learning in Business

Machine learning has the capacity to enhance numerous aspects of a business. These practical applications are based on the inherent properties of machine learning systems. Basically, the technology enables computers to learn from available data and interactions without requiring explicit programming. Such computing systems can then turn raw data into useful insights that a business can use to solve complex challenges.

Take a look at some of the popular ways in which businesses are embracing the tech:

Enhancing Network Security

Threats of information theft and malicious attacks make it imperative for businesses to adopt advanced cybersecurity measures. Most threat control measures become obsolete as viruses evolve at more dynamic rates. Machine learning algorithms hold an advantage in this regard as they analyze existing data to predict possible future threats.

Making Predictions and Recommendations

Interactions with customers produce a constant stream of useful data that businesses can leverage to personalize customer experience. By reviewing customer habits and identifying purchase habits, it can make product recommendations that they are more likely to buy.

They also make it easier to streamline marketing efforts based on data, improving predictive models. Thus marketers can better target their campaigns and create more exposure for better outcomes.

Personalized Customer Service

Advanced algorithms leverage customer service data to enhance interactions. They offer each customer the most relevant information and make better decisions based on data. This enhances customer engagement and satisfaction leading to higher retention rates. And since chatbots, which use such algorithms, are available round the clock, they offer immediate solutions that keep customers happy.

Streamlining Recruitment Processes

Using automated computing systems for the recruitment process offers countless benefits for businesses. First, they thoroughly assess every applicant, matching credentials to the job description. Moreover, they eliminate biases from the process.

Business Accounting

Accounting involves carrying out predictable, repetitive tasks that are heavy on data. By its very nature, this aspect of business perfectly lends itself to automated machine learning solutions. Automating these sensitive processes enables businesses to streamline expense management, bank reconciliation and invoicing among other tasks.


Benefits of Machine Learning Integration

The integration of machine learning into the above aspects of business offers countless positive impacts for enterprises. Let us briefly consider some of the foremost benefits:

High Return on Investment (ROI)

Automation improves overall efficiency and thus increases higher ROI. For instance, machine learning-based predictive marketing efforts yield better results than the traditional approach.

Moreover, making data-based purchase recommendations can increase sales and reduce churn. To illustrate, a McKinsey Global Institute report cited by Forbes revealed that Netflix saved $1 billion in 2017 by making personalized recommendations using machine learning.

Lowering Operational Costs

Automation of various business processes can yield significant cost-savings. One way is by eliminating the costs associated with human labor in carrying out various tasks. For example, by replacing human customer service assistants with chatbots, it significantly cuts down remuneration costs. The same applies when it comes to automating repetitive and mundane tasks.

Another avenue for cost reduction is through the automation of the recruitment process. It cuts down the amount of time and human resources associated with the process. And due to a high efficiency rate, it also reduces the wastage that results from an unsuccessful recruitment exercise.

Reducing Errors and Inaccuracies

When humans manually perform highly repetitive tasks, there are high chances of error and inaccuracies. On the other hand, machine learning-based approaches are completely error-free and reliable.

Enhancing Productivity

Human effort expended towards manual data entry tasks is extremely time-consuming and painstaking. Automating such processes frees personnel, redirecting them to more worthwhile and productive pursuits. It also speeds up the process and enhances productivity. Furthermore, individuals in data-intensive departments such as accounting can use the technology to develop deeper insights and increase value.


Practical Considerations When Developing a Machine Learning Adoption Strategy

Many business operators acknowledge the fact that technology holds great transformative potential. But the biggest challenge is how to actually make use of it in their day-to-day operations. To make your integration process effective, consider the following pointers:

Define the Problem

A common mistake that companies often make is adopting machine learning simply to get onboard the emerging tech hype. But for the technology to make a real impact and solve existing challenges, it’s important to identify the exact business processes you wish to improve. Avoid hype traps at all costs and set well-defined target areas for practical application.

Scope of the Opportunity

After isolating the problem, the next step is to work out a solution framework. Identify your objectives, stakeholders involved, constraints and the current state of the process in which you want to apply machine learning. With all the information at your disposal, it will be possible to figure out the best approach to use in applying the solution to specific workflows. Right from the onset, strive to attain clarity on how you will execute the output from your model and the possible effects on customers and employees. Otherwise, without effective integration, all you will have is a series of numbers.

Create Relevant Data Fabric

One of the most important steps in deploying a machine learning solution for your business is creating a data ecosystem that would facilitate integration across the enterprise. Without gathering relevant data and organizing it efficiently, a business cannot fully monetize the opportunity. Setting this foundation will eliminate the tendency to create a ‘garbage-in-garbage-out’ scenario, which would in fact be detrimental to a business.

Bring in the Right Talent

Proper machine learning integration revolves around creating the right team of personnel for its implementation. Focus on creating a cross-functional team including but not limited to a data engineer, domain expert and statistician. While you can outsource most of such talent, it is equally important to create an internal pipeline to ensure successful implementation across the enterprise.

Include Human Intelligence Into Your Machine Learning Adoption Strategy

Admittedly, artificial intelligence is advancing at an unprecedented rate. But it’s important not to forget the value of the human brain in innovation and creativity. Consider machine learning tools as enablers for expanding capability rather than replacements for the human workforce. Aim to compliment employees by placing the right tools in the right hands so as to effectively augment the technology in business applications.


Get On Board the Machine Learning Business Revolution

In the early days, implementing machine learning was intimidating, especially for small businesses due to the costs involved. But in recent times, technological advancements and high competition in the market has led to significant price drops. The number of open source frameworks is also on the rise. Consequently, business operators no longer have to train their own models from scratch. Rather, they can use pre-trained models to build custom solutions. With barriers to entry becoming ever lower, businesses have every good reason to embrace the highly versatile technology.

Though machine learning may not replace or perfectly replicate human intuition, it holds great potential for enhancing business efficiency. Leveraging the exciting opportunity could mean the difference between gaining a competitive edge and becoming redundant.

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Janica San Juan

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