How AI Is Shaping the Future of Work
2020년 12월 1일
If there’s one thing that 2020 has taught employers, it’s that agility and adaptability are essential for dealing with disruption. For many organizations, that means leveraging artificial intelligence (AI) to do everything from optimizing retail operations, to streamlining supply chains and creating faster, more personalized customer service. But there is one area in particular where organizations are less bullish: how to leverage AI in the workplace to transform their people’s experience, and drive success.
This doesn’t just mean using AI to automate mundane tasks that bog people down. It means using AI to help them be better at their jobs and grow in their careers. This, in turn, helps organizations uncover better insights about their business and their people, such as making helpful predictions to support productivity, restoring stability—especially in times like these—and creating long-term resiliency.
But today, only 17% of organizations use AI-based solutions in their HR function, and only another 30% plan to do so by 2022, according to the Gartner Artificial Intelligence Survey. And yet, AI has the potential to increase HR scalability, recognize patterns in people’s behavior and offer personalized support where and when needed. For example, AI can surface prescriptive recommendations in areas like recruiting, learning and development, boosting engagement and retention and others. But turning this AI potential into reality doesn’t come without its challenges, from ensuring ethical and unbiased use, to implementing practical, day-to-day applications.
Enter: The Cornerstone Innovation Lab for AI
Today, we announced the Cornerstone Innovation Lab for AI, a new center of excellence within Cornerstone, bringing together data scientists, machine learning engineers and other experts from across the company to innovate practical and ethical ways to apply AI technology to the workplace. Through research and collaboration, the Lab aims to tackle the toughest AI questions that organizations are concerned about, such as how to preserve the human elements of work while relying on automation, and how to operationalize sensitive people data—all while preserving ethics and eliminating bias.
The ultimate goal? Use AI to elevate people’s experience at work into a better, more personalized and rewarding one.
The Challenges of Applying AI to the Workplace—and How to Overcome Them
There’s plenty of opportunity to use AI in the workplace. From helping with HR’s recruiting activities, like filtering candidate applications and automating interview scheduling, to offering employees personalized learning recommendations to support career growth. But as more organizations consider practical use cases like these, there’s a central challenge that stands in the way: effective analysis of people data. And that’s one of the major topics that our new Lab is exploring.
The format of HR data is uniquely diverse.
It takes both AI algorithms and a rich breadth of HR data to derive effective, data-driven insights from the workplace. HR data tends to fall into two, diverse categories. The first is structured data, which arises from quantifiable events like how often employees engage with their training curriculums, the data candidates submit during their hiring process, employee attrition data, and career path data as employees advance and grow within their organization.
Then, there is textual, unstructured data, which comes from resumes, job profiles, performance reviews and descriptions of training courses. Collectively, structured and unstructured data forms a large and varied gamut of HR data. It takes a rich collection of AI algorithms to feed off this data to generate valuable insights into the workplace.
Other major challenges include privacy and security.
Workplace data has powerful implications, but it is also some of the most sensitive data at any company. In addition to shielding it from external bad actors, organizations also need to consider protecting it from exposure internally, too.
I asked Cornerstone’s Vice President and Chief Analytics Architect Asif Qamar to explain it:
"As we analyze the data, we should not, even ourselves [at Cornerstone], become aware who this person is. We have to deal with data completely stripped of personally identifiable information."
Looking beyond big data to cross-functional use cases.
Many companies today claim they have AI solutions, but in actuality, they are just business intelligence tools (a.k.a. big data). Their use case streamlines one specific operation using a finite data set.
To truly be AI, a solution must be applicable to a variety of flexible and changing situations, leverage data from across the organization and be able to provide predictive and intelligent decisions and recommendations. When this type of AI-driven insight is culled from a comprehensive window into the workplace, it transcends usefulness that traditionally derived analytics enables with business intelligence tools.
AI Will Humanize Work—and Improve People Experience
As our new Lab continues to explore these and the myriad other unique facets of AI in HR, we’re already seeing some success in applying best practices to our own AI engine.
Recruiting. A component of Cornerstone’s AI engine, the Cornerstone Skills Graph, can analyze a job applicant’s resume and capture their skills even when they aren’t explicitly mentioned—a major innovation that recruiting teams can tap into.
Asif explains it like this:
"What our AI engine can do is infer things not mentioned in a resume. It’s studying the resumes of hundreds of millions of people and seeing the relationship between skills. It’s learning to understand those relationships in order to make accurate predictions."
Learning and development. By analyzing how people engage with existing learning content—what topics they choose, how often they view it and how well they retain information—our AI engine can identify their personal learning preferences and provide methods to optimize their learning experience.
Career development. And because Cornerstone’s AI engine is designed to be cross-functional, it has the capacity to extend its prescriptions beyond a single use case. For example, the Cornerstone Skills Graph not only analyzes learning behaviors, but also career trajectories. This makes it possible for the system to offer recommendations that empower employees to use their newly acquired skills to propel their careers forward.
Here’s Asif again, to explain:
"We have data from thousands of employees who have followed well-trodden paths. This makes it possible to make a probabilistic model for where others want to go. When we can infer that, we can make recommendations not simply based on what you have been learning recently, but also what will help you with career growth."
The result is not only a better learning experience, but also a more personalized, holistic work culture designed around development.
Humans Are Still More Essential Than Ever
When AI is implemented successfully, the possibilities to transform (read: personalize, humanize and improve) people's experience at work become virtually limitless. But there’s one important caveat as Asif shares here:
"At the end of the day, the interpretation of data is human. AI can surface interesting things for observation, but it cannot replace people. It is a decision support system to bring efficiencies and optimizations for the HR team and employees."
In the pursuit of these transformative use-cases for AI in the workplace, it’s important not to overlook ethics and bias. After all, AI systems can pick up on and learn from existing patterns of, say, conscious or unconscious bias in hiring, at an organization. This is another ongoing area of focus for our data scientists.
Fundamentally, AI will continue to make potentially biased predictions if the data sets are inherently biased. Two important ways to address this are to ensure the AI team is diverse and conduct adverse impact studies, something we are making a point of doing at Cornerstone.
Employee-centric AI Is Key
As organizations increasingly implement AI across their businesses, they must keep this goal in mind: leverage AI to improve the experience for their people in real, practical ways. Moving forward, our Lab will continue to educate business and IT leaders—as well as employees —about the role AI can have in the workplace. We’ll explore how AI can restore workforce stability, how it can support diversity initiatives, ethical ways to apply AI, and more. We look forward to sharing these topics with you in future.