AI Predictions for 2021

Artificial intelligence and machine learning continue to make waves in the industry and 2021 looks to further that trend. Experts in the field see humans and machines working together to eliminate biases in algorithms while making work easier to manage through the automation of simple tasks and more.

Here IT leaders reflect on the changes that 2020 has brought and the key changes they see shaping up for 2021.  

The rise of explainable AI/machine learning. Expect developers and business users to demand more insight and reasoning into AI and machine learning algorithms and how they are applied. Wide-scale adoption of these solutions will occur after we build trust in the underlying technology, which can only happen if the drivers for a given prediction are explained to the end user. For example, in the context of machine learning in recruiting—why a given candidate is recommended for a particular role is important both to allow the hiring manager to make an informed decision and also to expose the risk of unintentional (or malicious) bias in hiring practices.

Because most AI/ML models are somewhat of a black box, users and developers don’t have visibility into why the models make the decisions they do. More insight into this process will ensure people understand the factors on which the model is basing its decision, and it will also help developers prevent an adversary from exploiting it.—Jim Stratton, CTO, Workday.

Convergence of AI and BI will boost data insights. AI has been part of every corporate discussion over the past 5 years. And yet, challenges persist in democratizing advanced AI insights across large sections of employees. As new AI-powered BI products emerge, silos will be broken and every user will be able to leverage data analytics and find insights easily. Simple interfaces, personalized insights, and engaging data experiences will become the hallmarks of data analytics in 2021 and beyond.—Dhiren Patel, MachEye’s chief product officer and head of customer success.

AI will improve decision making. Business Intelligence is shifting to a new paradigm of advanced data analytics with the integration of Natural Language, Natural Search, AI/ML, Augmented Analytics, Automated Data Preparation, and Automated Data Catalogs. This will transform business decision-making processes with higher-quality real-time insights.—Ramesh Panuganty, CEO of MachEye.

Artificial Intelligence becomes less artificial in 2021. Even with a vaccine for COVID-19 on the horizon, how people work and interact has fundamentally shifted. In the new year, remote work will continue, social distancing requirements will remain, and supply chains will continue to face disruption. This new way of life demands a new way for companies to continue operations effectively across the value chain – from the product to the plant to the end user. The use of artificial intelligence (AI) will be the standard for addressing these challenges. However, without considering how humans will interact with and leverage these new autonomous systems, AI will fail. In 2021, enterprises will take a human-centered approach to AI initiatives, understanding user needs and values, then adapting AI designs and models accordingly, which will in turn, improve adoption.

Enterprises must put the same focus on people and culture as the technology itself for AI to be successful. Organizational change management (OCM) teams will be critical for driving digital transformation and AI forward by bringing people along for the change journey and setting the organization up for measurable results. Proper change management is the most important – yet overlooked – aspect of any digital transformation initiative.—George Young, global managing director of Kalypso.

AI teaches data to speak human.
AI-driven active metadata creation will become more popular as we address data management-- shifting the emphasis from data storage and cataloging to a true human data discovery experience. The same shift— which has already taken place on the consumer side as we saw in e-commerce wherein the primary navigation has shifted from manual catalog filtering to automated content recommendations — will transform enterprise data discovery experience, enabling new data consumers to self-service access large, varied and complex data sets for the first time, unlocking citizen data scientist innovation potential.—Cognite’s president of product marketing, Petteri Vainikka.

Data security will be a crucial ingredient to add on the enterprise path to AI. For many companies, the COVID-19 pandemic fast-tracked digital transformation, AI, and machine learning timelines – well beyond the level of innovation that would have happened under normal circumstances. For the coming year, companies will continue on the path to AI, likely encountering the age-old balance between the freedom to innovate and the control required by the business. As a result, having an enterprise-wide data security strategy will become even more critical. And with the rise of hybrid cloud and multi cloud, companies will need to adopt a comprehensive data security strategy that accounts for data wherever it resides – whether at rest, in motion or in use.—Rick Farnell, president and CEO of Protegrity.

Artificial intelligence is getting smarter and faster, but it’s obvious that the need for human oversight isn’t going away anytime soon. Improvements in artificial intelligence will transform the human experience like never before, from billing automation to legal documentation. Leveraging huge stores of data and past records for reference, automation will begin to do the lion’s share of the work with human oversight to correct errors and misinterpretations. For scanning applications, we’re seeing the advancement of AI for document classification and extraction in every major ECM application, including Microsoft’s Project Cortex and SharePoint Syntex, providing customers with seamless end-to-end solutions.—Technology evangelist Scott Francis of Fujitsu.

Addressing bias in AI algorithms will be a top priority causing guidelines to be rolled out for machine learning support of ethnicity for facial recognition.  Enterprises are becoming increasingly concerned about demographic bias in AI algorithms (race, age, gender) and its effect on their brand and potential to raise legal issues. Evaluating how vendors address demographic bias will become a top priority when selecting identity proofing solutions in 2021. According to Gartner, more than 95% of RFPs for document-centric identity proofing (comparing a government-issued ID to a selfie) will contain clear requirements regarding minimizing demographic bias by 2022, an increase from fewer than 15% today. Organizations will increasingly need to have clear answers to organizations who want to know how a vendor’s AI “black box” was built, where the data originated from and how representative the training data is to the broader population being served.  

As organizations continue to adopt biometric-based facial recognition technology for identity verification, the industry must address the inherent bias in systems. The topic of AI, data and ethnicity is not new, but it must come to a head in 2021. According to researchers at MIT who analyzed imagery datasets used to develop facial recognition technologies, 77% of images were male and 83% were white, signaling to one of the main reasons why systematic bias exists in facial recognition technology. In 2021, guidelines will be introduced to offset this systematic bias. Until that happens, organizations using facial recognition technology should be asking their technology providers how their algorithms are trained and ensure that their vendor is not training algorithms on purchased data sets.—Robert Prigge, CEO of Jumio.