AI Trends in 2020

The year 2019 can be regarded as the time of awe-inspiring technological trends. No prior period has the wave of digital transformation like artificial intelligence, robotic process automation (RPA) and other emerging technologies created a heightened awareness.

While artificial intelligence and its related software was the main focus, the year also saw the use of pre-built machine learning technology solutions. Machine Learning as a Service (MLaaS) emerged as one of the latest trends. The solution is much like the Software as a Service (SaaS) or Infrastructure as a Service (IaaS).

Companies found no more need to build their own machine learning models. Software vendors sold and supplied the MLaaS solutions for businesses to implant in their operations or business applications. Business enterprises can derive the benefits of the pre-built AI solutions either via subscription or the pay-as-you-use service.

Upcoming AI trends in 2020

The business community will experience a major makeover in 2019 with the upcoming trends:

MLaaS will make its debut at Wall Street

Machine learning as a service will be fully harnessed and become an important part of the mainstream financial sector. Many key vendors will offer a host of cloud platforms and infrastructure micro-services.

Amazon Web Services (AWS), Microsoft Azure, and Google’s Cloud Platform are among the leading cloud computing providers. They earned their prominent positions as MLaaS providers because of excellent AI research work.

The MLaaS space is highly competitive with each cloud provider wanting to boost revenues and garner market share. The providers are bent on displaying growth in their respective cloud businesses and showing positive quarterly and yearly sales figures through their earnings reports.

Although cloud computing has become a common practice for some years now, the business has plenty of room to grow. With regards to MLaaS as a part of the digital transformation, Transparency Market Research estimates the space to rake in $19.86 billion revenue by 2025. The cloud computing providers will begin to flex their muscles and prove their mettle through the sales growth from the MLaaS service. 


The time of reckoning will be on Q2 2020 when the acknowledged leaders in the cloud computing market will reveal their actual revenues from their MLaaS products. The financial results would serve the basis of startups and businesses migrating to the cloud in selecting the cloud computing provider.

Beyond 2020, almost all IT leaders will have to implement some type of machine learning for the purpose of improving several business functions. Some of the critical technologies in business include natural language processing (NLP), natural language generation (NLG), and computer vision to name a few.

When AI solutions are in place, businesses can easily automate tasks that do not need humans. AI can be deployed to support and assist employees in their daily work. Once these machine learning technologies are assimilated into business processes, the use of MLaaS will expand.

Hence, it is vital for cloud computing providers to have an easy-to-implement and easy-to-use MLaaS to win the nod of customers. Further, software companies desiring to use intelligent applications and build their products are likely to choose the best AI platform available. 

The choices might redound to the Google Machine Learning Engine, Amazon SageMaker, Microsoft Azure Machine learning Studio, or IBM Watson Studio. However, compatibility might be an issue since these solutions do not work across vendors. It will all depend on which platform is suited for a business to build the app that would complement the other cloud computing components needed to run the business.

The success is sequential. It begins from revenue growth which leads to growth in market share then the stock price growth which is the ultimate objective of every enterprise. That’s precisely the reason why the resulting figures from the use of MLaaS need to be published.

Small businesses will excel with robotic process automation

No solution can be as trendier in 2019 than the robotic process automation software. Apart from RPA’s ability to automate dreary and mundane but time-consuming tasks, supervised automation can also assist employees in their work. Companies can save on man-hours while ensuring processes are conducted properly.

The RPA market is flourishing for quite some time now and is often included in the basket AI and machine learning tools. When segregated from the main basket, RPA as a standalone market can deliver stellar growth. Based on a report by Forrester, the RPA market in 2016 was worth $250 million and by 2021, is estimated to top $2.1 billion.

Previously, only enterprise-sized businesses are leveraging these digital workforce solutions. These companies have the liquidity to purchase and implement the software but would need to automate a large number of processes. Thus, you will see more of the small businesses implementing these tools next year.


Small businesses will be the target market of RPA vendors aside from enterprise companies, and other smaller companies that will leverage the advantages of a digital workforce. Small businesses are projected to comprise about 30% of the entire RPA market.

By employing a digital workforce to perform tasks, small businesses are geared for rapid scaling versus hiring a human workforce to perform the same tasks. This would prove to be more economical in the long run.

The window of opportunity opens up for RPA vendors to peddle to smaller but growing businesses. Enterprises that are implementing automation solutions will continue to do so. When the realization of ROI is quicker and obvious, small businesses will begin to see the tangible benefit of using an RPA software.

Machine Learning Data Catalogs (MLDCs) will be the norm

Many organizations today are data-driven. They understand that big data is a critical aspect of digital transformation and leveraging data can improve business decision-making. Companies are empowering their employees to access huge data. Businesses do these using self-service business intelligence (self-service BI) applications.

Self-service BI tools are helpful for exploring and analyzing deftly organized data that has previously been sanitized and prepared by data analysts, data scientists or an IT team. Only the data set connected to the BI tool can be found by the end-user.

If employees are unable to find or locate the needed data, they can be forced to discontinue leveraging data to make decisions. A company can’t depend on master data management software tool or data preparation software.  Both are complex tools and difficult for daily users to navigate.

However, a tech-savvy employee can easily find the data set within these tools or might have come across them unintentionally. These tools are where all business data is stored including sensitive data that are not shareable with employees. These vital issues and concerns reinforce the need of companies to have machine learning data catalogs (MLDCs) in 2020.


Businesses have no other recourse but to increase implementations of MLDCs in order to handle the growth of big data. End users will find the catalogs very useful when organizing and governing data. It also allows them self-service access to data. The use and implementation of MLDCs are seen to increase by at least 50% year over year. With machine learning data catalogs, businesses can organize all data in an intuitive manner. End users along with non-technical employees will have easy access to data. MLDCs also have security features similar to dynamic data masking to help safeguard sensitive data that are off-limits to employees.

The outlook in 2020

Expect the excitement over AI to further intensify. When the business world transformed into a data-driven environment in 2019, the importance of data management was magnified. Businesses will have to embrace the emerging technologies that will drive business growth in the years ahead.

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