Introducing Workday’s AI Maturity Model: The Four Waves of the Intelligent Business

There is much discussion about the impact intelligent technologies will have on the world. We’re already seeing how they’re transforming medicine and healthcare, and powering a new era of driverless vehicles. While there is no shortage of hype, it is widely agreed that intelligent technologies will shape life and work in ways we still can’t imagine.

Many businesses are looking to the intelligent future with equal measures of excitement and trepidation. At the crux of the discussion is the term artificial intelligence (AI), which describes the wide field of making machines exhibit human-like intelligence. For many, this term can conjure images of science fiction and a takeover by robots. While there’s reason to proceed carefully with AI, there are also many ways it will continue to positively impact our lives.

At Workday, we believe intelligent technologies will lead to a better and more productive future. As these changes unfold, organizations must take a people-centric approach to supporting their workforces, which includes reskilling and helping employees adapt to newly shaped roles as a result of AI. We also believe that intelligent systems must be built in an ethical way, taking great care to avoid unintended biases, and to protect personal data. As we continue to invest, we are guided by these principles. 

In fact, Workday has been honing its AI knowledge and experience for years. Having partnered with our customers from day one to build applications and solutions that help their businesses thrive, we began making investments and infusing our platform and applications with intelligence in 2014 when we acquired Identified, a predictive analytics company. Through our early investments, we have learned a lot about solving our customers’ business problems with intelligent technologies, giving us a solid foundation on which to build and help solve their challenges for years to come.

Workday’s AI Maturity Model

Our customers have told us that amidst all the hype, there’s complexity around understanding all the technologies at play and their impact on operations and employee experiences, in a way that helps them move forward and embrace new opportunities.

To help our customers navigate this evolving landscape, and gain a framework for their own intelligence strategies, we have developed a four-stage maturity model. These four stages outline how Workday believes intelligence will impact the enterprise—moving from making today’s operations more efficient, to reorganizing operations around the unique possibilities intelligent technologies will offer in the near future. Along with each stage, we have also highlighted some strategies for helping the workforce adapt to changes that intelligence will inevitably bring to their roles and work.

Workday's AI Maturity Model

Automating

Automating is where the journey begins for many businesses. While automation has been around for a while, only recently has the scalability of machine learning systems allowed it to be cost-effectively brought to places where it was never feasible.

Automation presents opportunities for employees to focus on more value-add work.

Automation technologies also offer new possibilities for the employee experience, making it easier to interact with complex systems through a natural interface. For example, at this stage companies may begin using chatbots, which are powered by natural language processing technologies and able to understand intent, to transform the end user experience.

Also at this stage of the journey, some employees’ work is beginning to change, especially jobs that include repetitive, data-based transactional work. In many cases, this work is the least fulfilling part of their jobs, and automation presents opportunities for employees to focus on more value-add work. It is important that companies recognize these changes and incorporate learning opportunities to support shifting roles.

Informing

At the next stage, intelligent predictions and insights are widely used to inform decision-making and planning. Leaders, managers, and frontline employees all become adept at using intelligence to augment their daily decision-making, infusing it throughout everything they do.

Also at this stage, machine learning techniques use colossal amounts of data to underpin predictions and create simulations, opening up entirely new possibilities for data-driven decision-making. They make these predictions and simulations so cost-effective and accessible that they can be more widely used than ever before.

Natural language technologies also play a big role in bringing advanced analytics to every employee by democratizing access to data and empowering employees to make augmented decisions. Instead of having to learn complex query languages and data interrogation tools, at this stage natural language interfaces allow employees to ask simple questions to achieve complex data science. For example, “How does the weather impact sales in our stores?” or “Which managers have the best track record for developing strong employees?”

Intelligent analytics and predictions are being used to incorporate larger and more diverse data sets from which to make decisions. Cloud computing power, along with intelligent algorithms and advanced analytics platforms that can fully harness it, allow data from multiple systems to be combined to generate predictions and spot patterns that previously would have remained hidden.

At this stage, companies should be investing in developing data and intelligence literacy skills across the employee base. As a business deploys augmented decision-making, and as every employee starts to become skilled at using data as part of his or her daily work, the organization starts to transform from within.

The focus begins to switch from “How can we make what we’re doing today more effective and efficient?” to “How can we transform our operations to drive tomorrow’s business?”

Discovering

As processes are automated and as data-driven decision-making becomes widespread across the business, a tipping point is reached and the promise of AI is being fully realized as employees’ productivity skyrockets due to a shift to more high-value tasks.

At the discovering stage, intelligence has been deployed, the use of smart applications is widespread, and day-to-day operations and decision-making have been made frictionless. The focus now begins to switch from “How can we make what we’re doing today more effective and efficient?” to “How can we transform our operations to drive tomorrow’s business?”

As business transformation becomes more noticeable, workforce reskilling programs should be well underway in order to ensure that workers at risk of role displacement or change are involved in learning and redeployment opportunities. The very technologies that are causing this shift can also help employees adapt. For example, personalized learning paths driven by smart recommendations can help employees close their skills gaps.

Transforming

At the final stage, the business looks very different because every facet of the organization has been reshaped around intelligence, from the core business model and how employees are engaged, to the composition of the workforce itself and the skills that employees possess.

Through thoughtful and proactive planning in previous stages, workers in impacted roles have been successfully reskilled and redeployed to more impactful jobs.

As the organization faces new “intelligence-native” competitors in the market, the transformation it has undergone to be a truly intelligent enterprise has set it up to be successful in the future.

It’s a long and complex road but as our customers continue their intelligence journeys, Workday will be investing in our platform and our applications to help them transform their people and businesses. In the next post in this series, we’ll share our AI product vision and how we’re continuing to evolve as our customers’ priorities shift.