Four Predictions for AI and ML in 2022

As 76% of enterprises (according to a study reported by Forbes) prioritized Artificial intelligence (AI) and Machine Learning (ML) over other IT initiatives last year, Brian Bartell, VP of AI and Machine Learning at Conga, expects this trend will only continue into 2022 with four related predictions. 

Artificial intelligence (AI) and machine learning (ML) are no longer emerging technologies. While some consider these technologies ‘new,’ AI and ML have been providing real, practical value to organizations for many years. 

Industry data demonstrates the prioritization of AI and ML among business leaders. 76% of enterprises prioritized AI and ML over other IT initiatives in 2021. The majority (83%) also increased their budgets year-over-year, proving that business leaders are already seeing the benefits of AI and ML in operations shifting their priorities accordingly. And, likely, they will only continue to do so as the value is further proven. 

As AI and ML rise in popularity, the following are four trends expected to impact business leaders in 2022: 

1. The Democratization of AI and ML 

This trend will continue to impact enterprises, meaning AI and ML will no longer be the responsibility of one employee in the IT department. Still, it will be available to engineers, product managers, customer support representatives, sales engineers, and other allied professionals to solve everyday business problems.  

By becoming more accessible, ML will emerge as a standard tool to solve specific complex computational problems faced by many. AI and ML will also continue to impact multiple industries, as they realize benefits such as personalizing customer experiences, allowing enhanced insight into customer behavior and improving risk analysis. As more people understand ML and deliver it themselves, the applications will be more successful across an entire organization.

Furthermore, there will continue to be creative and new ML-enabled applications in every industry, healthcare, financial services, insurance, telecommunications, or transportation. But, the source of those applications will be the domain specialists — the people who live those details every day. With specialists understanding ML and delivering it themselves, those applications will be more successful.

2. Data Access at the Forefront of Security Concerns

The rise of cybersecurity and stringent data access requirements have forced data availability to be a driving concern for CIOs. As a result, there will be a continued focus on improving data access to drive better-supervised models without compromising confidentiality and security requirements. Model development is an iterative activity, where the faulty predictions from one iteration inform the modeling decisions in the successive iteration. ML engineers cannot identify the necessary enhancements to the model architectures without access to the source data. Consequently, organizations will go to lengths to ensure data is high-quality, secure and available for use. 

See More: Here’s Why Machine Learning will Thrive in 2022

3. More Complicated DevOps

DevOps has become increasingly more complicated with the addition of ML to the stack. With DevOps combining software development with IT operations, the integration of ML has muddied the waters. With “MLOps” as the new moniker, there is nowhere near a ‘best practice’ for adding this technology in the stack. 

“MLOps” also presents challenges due to the complexity of its data. While DevOps focuses primarily on software and code (and processes around that code), an ML model is created from software and data. As a result, different models can be made for the data changes, even if the software doesn’t change. This can add a layer of complexity to managing all artifacts within a model as this data can be large and dynamic.

DevOps and ML are inherently different, but the synergy of the two can ensure success for an organization. Therefore, IT leaders will be tasked with determining the most effective way that DevOps and ML can intersect.

4. The Need for Improved Platform Support

IT leaders will call for better platform support in 2022 and beyond. As a result of the continued democratization of ML and moving technology into many hands across an organization, there will need to be better platform support and a framework in place to ensure the success of the “ML everywhere” approach that companies are executing on. With this influx of ML solution practitioners, platforms will need to support heterogeneous ML technologies and commit to creating a true diversity of skill sets. 

Throughout 2022, businesses will likely leverage AI and ML more frequently to make data-driven decisions and experience transformational value. However, true transformation is not a single problem and solution but rather, it is like a wheel. The only way to solve complex issues is by focusing on one spoke at a time. Tools like AI and ML can help support each spoke of this wheel to help move companies in the right direction.

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