When we look back on 2020, we’ll likely regard it as the year that digital transformation reached escape velocity. Technology took center stage as businesses scrambled to serve customers, ensure business continuity, and support employees during the throes of a global pandemic. As a result, technology evaluations and implementations that previously took months or years to complete were compressed down to weeks, if not days, in many cases. This pandemic taught us just how important technology is to businesses today and for their future success.
As part of this process we’ve also realized that planning and preparedness pays off. Companies are now investing in technology not only for digital transformation but for crisis preparedness, because if there's one thing we've learned it's that a global crisis can happen at any time, and with little warning.
As the world focuses on recovery in 2021, the investments businesses made as a result of the pandemic have helped lay the groundwork for a year of rapid technology adoption in enterprises. Companies are looking to upgrade their digital core technologies to give them the agility they need to move forward. I expect machine learning (ML), next-generation analytics, and employee experience technology to make huge leaps this year, in the following ways:
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. Machine learning in the enterprise is being designed and developed to augment individuals in their roles, such as recommending the most qualified candidates for a role, flagging an anomalous or high-risk financial transaction, or suggesting the next step in your career path. However, wide-scale adoption of these solutions will only happen 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—understanding why a given candidate is recommended for a particular role is important to allow the hiring manager to make an informed decision and also to expose the risk of bias in hiring practices. Similarly, in the finance world, without describing why a particular transaction was categorized as potentially anomalous, we may actually cause the financial analyst to spend more time auditing transactions rather than less.
This year, I expect developers and business users will demand more insight into the algorithms and how they are applied. More visibility into this process will ensure people understand the factors on which a given model is basing its recommendation.
In 2021, IT leaders will increasingly seek automation to reduce friction and increase productivity with processes and tasks that flow between enterprise systems. Pressured to do more with less—and faster—we’ll see automation technologies experience greater traction as CIOs work to quickly re-align their enterprise systems to new processes, ways of working, and revenue opportunities; ensure the ongoing health and safety of their workforce; fill critical talent gaps; and ultimately extend the capacity of those workforces. In fact, Workday’s recent organizational agility study shows that nearly two-thirds (64%) of companies report progress deploying AI, ML, and automation technologies designed to streamline workflows and help increase capacity of the existing workforce. The focus for IT is shifting from how to customize and rewire a single application to orchestrating a holistic and efficient solution experience.
The investments businesses made as a result of the pandemic have helped lay the groundwork for a year of rapid technology adoption.
The seamless and personalized consumer experiences we enjoy in our personal lives are increasingly coming to the workplace. The confluence of advanced front-end user interface technology, open systems with connecting workflows, intelligent multimedia content, and embedded ML will bring about a deeper level of integration, context-guided experiences, and personalization across systems and devices. Equally important is a burgeoning focus on design thinking for workplace experiences to ensure employee interactions are, and remain, frictionless and intuitive. With so many employees continuing to work remotely, businesses are accelerating investments in making their enterprise applications as compelling, collaborative, personalized, and efficient as possible.
When I speak with our customers, nearly every conversation revolves around data—how they can make the most of their data to drive business decisions and agility. With so many clouds, applications, and services in the enterprise ecosystem, critical data is sitting in silos across the organization. For example, if you wanted to see how your Atlanta sales office is performing, you’d likely need data from your CRM, finance, marketing, and HR systems to get a full picture—not an easy task.
The next generation of applications will give us greater ability to aggregate analytics and insights from across systems. And, these next-gen tools will then surface those insights to the right people at the right time to help them solve business needs—no SQL required. With recent advancements to connectors and metadata connectors, it’s getting increasingly easier to bring disparate data sets together and put the data to work, which bodes well for making decisions faster, and with more confidence.
In the early days of AI and ML development, companies focused on building proprietary ML algorithms for a wide range of functions—optical character recognition, natural language processing, and speech-to-text, to name a few. As the building blocks of AI and ML become commoditized, we’re starting to see the move toward higher-value services that leverage those underlying technologies.
This evolution mirrors that of the major public cloud providers as they moved from infrastructure to platform to applications. Examples of this higher value include enabling companies to experiment more with AI and ML innovations to improve products, while building trust in their models and predictions. Businesses will be able to leverage best-in-class algorithms to quickly build ensemble applications and focus their ML on what’s truly unique to their business—without requiring a full data science team.
While there’s no telling what’s ahead of us in 2021, businesses are focused on preparing for the next normal by devising new ways to empower their greatest asset—their people—with technology.