What is Machine Learning and why Businesses Should Invest

Machine learning is no longer just for medium-to-large enterprises. Small businesses can utilize machine learning to innovate and compete at the highest level.

by Andrew S., April 14, 2020

For decades, artificial intelligence has been the industry buzzword. In the past ten years though, it has deeply affected the business landscape at a medium-to-large enterprise scope with a wave of new applications and uses. With improvements, smaller businesses can innovate and get into the game.

Defined, ‘Machine Learning’ is an application of artificial intelligence that allows software more precise, predictive and extrapolated results without the need for definitive programming.  

Overall, the global machine learning market is approximately worth around $1.6 billion in 2017, expecting to grow to more than $21 billon by 2025. Some experts predict that by the middle of the century, many intellectual tasks performed by humans will be replaced by some sort of artificial intelligence technology.

Today, industries such as Finance, Healthcare, Marketing, Sales, Transportation, Government, Oil & Gas, Manufacturing, Bioinformatics, and other industries use some level of machine learning technology. These applications have created a market feeling of opportunity in addition to fear. Corporations, committees, and even governments have taken action to take a united stand to decide how AI today should be positioned to affect the society of tomorrow.

One example of this partnership and committee is the Global Partnership for Artificial Intelligence, founded by French president Emmanuel Macron and Canadian Prime Minister Justin Trudeau.

Here are Vivus’ questions and thoughts on Machine Learning and the future:

  1. What are the biggest misconceptions businesses have in relation to ML?
  2. What are the barriers businesses have when it comes to the adoption of this technology and how can they be overcome?
  3. How can your business bridge the data science gap?
  4. How will businesses succeed if they put more resources into these emerging technologies and training for them?


Let’s start with the first.

What are the biggest misconceptions businesses have in relation to ML?

The two largest misconceptions about machine learning are one, that companies often think that machine learning is intensively complex and require doctorates to get value out of its implementation; and second, that machine learning is a cure-all for all business problems.

As it relates to complexity, relatively simple algorithms can be applied to business data to provide simple predictions or classifications which can easily grow the bottom line of companies.

Too far on the other side, however, is that machine learning can replace human ingenuity (at least at this point). Today, it cannot. The middle ground is to understand the true capabilities of different, well-understood algorithms and to match them with the right data to provide real customer value.

What are the barriers businesses have when it comes to the adoption of this technology and how can they be overcome?

Machine learning can seem like a wild and hapless territory to most business decision-makers-- and to some degree, it is. It can be unpredictable, experimental, and a shot in the dark. Here are a few questions to ask yourself or your team when thinking of applying machine learning.

What is the right type of data required to solve a problem and do you have access to it? Providing time and resources for exploration to that real and correct data can be siphoned to solve a particular problem. Be also the way that different industries, businesses, and departments limit access to certain internal and customer data. Take this into account when utilizing machine learning.

Another technical challenge includes the speed and automation of data access, especially when it comes to real-time data. For best results, machine learning should be exposed to and incorporate constant real-time data. Machine learning models should not be trained on a fixed set of data, therefore information technologists and architects need to set their algorithms up for constant re-trained to adapt to changing data behavior and systems they are interacting with.  

As the industry grows and applications are found, more barriers will be conquered and others will arise.

How can your business bridge the data science gap?

With profits and opportunities, more and more data scientists are emerging to fulfill demand. Time and sources are not infinite resources and the level of skill needed today is high and ever-growing. On top of that, demand is vastly outpacing supply.

One trend arising is the cross-training of other areas of expertise with data science, such as senior business analysts, software engineers, and those involved in forecasting. Even light training in machine learning can be applied outside of the IT department.

One way companies and organizations can access this limited pool of resources is by working with academia, through Universities and co-piloting apprenticeship programs to secure new talent.

Lastly, one more organization can mitigate this talent pool is by investing in technology that automates and simplify data pipelines, standardizing real-time data, making it legible if not programmable by non-experts, and allow cross-department collaboration. This will allow machine learning processes a lower level of entry for those unskilled in the craft.

How will businesses succeed if they put more resources into these emerging technologies and training for them?

Businesses who invest in machine learning emerging technology benefit in two ways-- by getting ahead of the competition or future-proofing their organization through innovation.

By utilizing artificial intelligence and machine learning, companies allow more effective resource utilization as well as better service to their users and consumers. One tactical example of this is without machine learnings, many organizations struggle with the process of sifting through large data pools because they rely on outdated technology and the need for on-premise, in-person systems. 

Guessing the future, one way or another, all industries, companies, departments, and teams will need to adopt some form of machine learning simply because it may become a market expectation. Consumers and users may expect their products, services, apps, and digital goods to anticipate what they want or to provide recommendations and predictive analysis personalized to them. 

In conclusion, the field of machine learning and artificial intelligence is still in its infancy, but we expect it to be a hot-button topic for the rest of our generation. Businesses large and small can benefit from its adoption.


Interested in learning more about machine learning? Check out this post on How Machine Learning Can Save Your Business Time and Money. 

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