There is a push to open data sources for the benefit of machine learning developers. AI is only as good as the data that it has to learn from, so when building embedded AI applications or training machine and deep learning models, one needs as much data as possible. Enterprise companies, like Amazon and Google, among others, do not have a problem accessing mammoth data sets, because their everyday businesses are so large that they create a seemingly endless supply of data. However, small businesses or independent developers do not have that luxury; therefore, they will take advantage of open-source data sets, often made available by those same enterprise companies.


Similarly, businesses will begin to share their data with the software they work with instead of trying to hoard their own data in secrecy. As embedded AI becomes the norm, companies will have the option to share data with the vendor to increase the machine learning capabilities, and have the technology learn not just from the business’ data, but also from the data of the vendors’ entire customer base. Businesses using AI-enabled software will begin to realize that the benefits of data sharing outweigh the risks, which primarily center around data security.


Artificial intelligence (AI) continues to be a major driver of digital transformation in, with the rapidly advancing technology affecting business strategy and operations, customer interactions and the workforce itself. While these are all general and broad impacts of AI, they will continue to be important for businesses trying to keep up with rapid technological advancements.


Embedded deep learning will become the focus of software product teams in the coming year, as buyers will begin to inquire about the machine learning as a service capabilities of the tools they are purchasing. Many vendors are already including machine learning in products to enhance and automate certain functionalities, and build marketing campaigns around those AI enhancements. As embedded AI becomes more standard in solutions, there will be less of an emphasis around the glitz and glamour of machine learning and more of a focus on how the embedded AI is contributing to a business’ overall digital transformation.

Adoption of AI in businesses will be driven by digital platform providers, the same way that those enterprise service providers drove adoption of the cloud.

Businesses and software vendors will also more frequently open up data to partnership opportunities in the form of data swaps. This will be particularly helpful for AI and general automation. Software vendors will begin to trade valuable data to best improve the embedded AI within its products. This will most likely happen across software categories, because the race to have the smartest and most intelligent application will be fiercely competitive. Any edge a vendor can get will be crucial. This will also benefit businesses outside the software space who begin to implement AI into general businesses processes.

Adoption of AI in businesses will be driven by digital platform providers, the same way that those enterprise service providers drove adoption of the cloud. Amazon Web Services (AWS), Google Cloud Platform and Microsoft Azure have created a number of machine and deep learning API’s and microservices that will make it easy for businesses to deploy AI for business operations and automation purposes. These solutions will have the same advantages as the vendors’ other service offerings; they will be cost effective, easy to setup and quick to deploy, making them attractive options for companies that do not have highly skilled, in-house developers. This machine learning as a service (MLaaS) type of deployment will become much more mainstream.

Finally, robotics process automation (RPA) will make its emergence in the workplace. This technology is still in its infancy, but it will begin to have an impact on business process management. RPA creates intelligent robots that access the software a business already uses, and creates automation for mundane tasks, like data entry. The benefit of RPA systems is that they are very easy to build, setup and train. These solutions can eliminate human error and help IT teams focus on bigger and more important implementations, instead of wasting time and energy improving and correcting the minor, but necessary aspects of industry. Look out for more updates on RPA throughout the coming year/s.

Look for each of these trends to emerge as a focal point for AI in the coming year and have an impact on business modernization and digital transformation. Small businesses and enterprise companies alike will be adopting and embracing these intelligent trends, because the benefits will be so important that they will be unavoidable.