Despite extraordinary advancements in the field, machine learning (ML) and deep learning have seen slow adoption in the enterprise. It’s reported that nearly 80% of enterprises fail to scale AI deployments across the organization. However, in 2022 AI will evolve to better deliver on its promise. There will be a new wave of technological advancements that will help companies overcome the common challenges. In addition, with a move to AI-first, AI won’t only be used to streamline processes, but will also be used to rethink business strategies.

ML Ops Will Relieve Data Scientists from Tedious Tasks

Today, models are typically built using a sterile environment, and when they are put into production, they underperform due to insufficient data processing resources and the inability to access the needed data to keep models comprehensive and accurate. As a result, AI developers are often burdened with operational issues. Next year, many labor-intensive, repetitive, and tedious tasks, such as preparing and labeling data, will begin to be automated so AI developers can focus on what they do best – perfecting algorithms. 

There Will Be More Off-the-Shelf AI Solutions

Previously, only big players like Google and Facebook had the deep pockets to make AI/ML models a reality. Next year, there will be more off-the-shelf technology including no-code machine learning that will make AI more cost-effective for smaller companies. Ready-made data flows will be available for simple data analysis projects like predicting retail profits and implementing dynamic pricing.

More Women Will Move to AI

The World Economic Forum’s 2020 Global Gender Gap Report shows just 26% of professionals in data and AI are women. There has been a global push to involve more women in AI. Take for example the Women in AI project, which is publicizing the careers of women in the AI field to provide inspiring role models. Not only for the advancement of women, but also to ensure that the models themselves show diversity, it’s important for women to be active in the AI industry. 

Bring Your Own Compute and Storage (BYOCS) Will Become a Reality

AI developers will have the ability to choose the best of breed compute and cloud solution per machine learning model on the fly, to prevent vendor lock-in while also giving them the luxury to try new innovative technologies without risking their whole AI/ML environment. AI developers will have access to a full marketplace of options to provide more development choices. Selecting the best platform based on performance/cost considerations will lower the overall cost of AI. The ability to choose different cloud storage vendors, as well as CPUs, GPUs, and specialized AI chips will accelerate innovation, limit risk, and speed up time to market.  

Shift to AI-First Approach

In 2021, most companies were still in the experimentation or POC phase of AI. Next year more enterprises will deploy machine learning models than ever before, as AI tools and become more available, affordable, and easier to use. An AI-first approach strategy will make AI the core of the company, where companies will learn faster about customer preferences and innovative ways to create competitive advantage. In addition, more products and services will have AI built-in, where AI is embedded in the core of everything from architecture to operations.

With the new online economy, in 2022 companies will race to use AI insights to become more competitive by being truly data-driven. This year’s challenges of integrating, cleaning, and processing data will continue, but at the same time, there will be a flood of more generic open-source solutions that can replace manual tasks freeing up data scientists for more strategic tasks.   

As the new online economy demands more data analysis for companies to be more efficient and provide a better customer experience, there will be more enabling technologies available to ease the transition to an AI economy. Next year companies will learn how to be stronger, faster, and better with more mature AI technology.