5 trends that will impact machine learning projects in 2019

Register now

Machine learning adoption has been growing at a rapid pace, and there is no end in sight. A forecast by International Data Corporation shows that spending on artificial intelligence and ML will grow from twelve billion dollars in 2017 to about fifty-eight billion dollars in 2021.

In addition, Deloitte Global shows that the number of ML pilots and implementations will double in 2018 compared to 2017, and double again by 2020.

Yet, as you’ll see in my predictions for machine learning in 2019, the path to deriving real business ROI from AI and ML initiatives is still far from being an achievable feat for most companies.

Frustration among business leaders will continue to grow.

For many companies, ownership of machine learning initiatives lies with data science teams. Despite being well versed in choosing, building and validating training algorithms and turning them into models to solve a business problem, data scientists are not familiar with what it takes to deploy and manage those models in production – an aspect that is typically owned by the operations teams.

As a result, it often takes much longer than anticipated for companies to see the benefits of machine learning. This leaves business leaders unsure of when they’ll accomplish their machine learning goals, leading to mounting frustration.

Additional areas of business will be brought in to share the responsibilities of delivering machine learning.

As the pressure for companies to differentiate themselves from their competitors and frustration among business leaders increases, other functions will be pulled in to help bring machine learning initiatives to fruition. Those functions include operations and business analysts, who can help pick up the responsibility from data scientists once they are done building machine learning models.

2019 is the year when machine learning and compliance will collide.

As more and more companies look to utilize machine learning in their businesses or scale the current machine learning projects they have, we’ll see compliance become a major point of discussion – both from a government regulatory standpoint and specific company policies. This is especially important given the high-profile discussions on data privacy and control taking place around the world.

There will be an increased focus on explainability and governance.

With more machine learning models being put into production every day and an increased focus on compliance, companies will have to account for why models made certain suggestions or predictions. In addition, organizations will have to understand exactly where any issues with their machine learning models stem from and why those issues led to certain outcomes. Therefore, we’ll see companies begin to adopt model governance in order to provide detailed fault analysis.

Small companies will leapfrog bigger companies in machine learning adoption.

Although large companies are always concerned about new technologies and innovating, startups and smaller companies are much more agile and more willing to take risks in order to grow their businesses. We’ll see smaller companies utilize and/or scale machine learning before larger companies even take the leap.

In order to see the benefits and a return on investment from your AI and ML initiatives, you must consider how these forecasts will impact your business – whether you are a small or large organization. By doing so, you will be able to avoid any issues that may arise and accelerate your path to success.

For reprint and licensing requests for this article, click here.
Machine learning Artificial intelligence Data strategy