Cornerstones New Innovation Lab for AI Will Transform the People Experience at Work

How AI Will Transform People Experience

If there’s one thing that 2020 has taught employers, it’s that agility and adaptability are essential to deal with disruption. For many organisations, that means leveraging artificial intelligence (AI) to do everything from optimising retail operations, to streamlining supply chains and even creating faster, more personalised customer service. But there is one area in particular where organisations are less bullish on: how to leverage AI in the workplace to transform their people’s experience.

Today, only 17% of organisations 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, recognise patterns in people’s behaviour and offer personalised support where and when needed. This doesn’t just mean using AI to automate mundane tasks that bog people down. AI can help them be better at their jobs and grow in their careers. In turn, it helps organisations uncover better insights about their business and their people, make helpful predictions, increase diversity, boost productivity and effectively respond to rapid change.

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 our Innovation Lab for AI, a new centre of excellence within Cornerstone, bringing together data scientists 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 organisations are concerned about, such as how to preserve the human elements of work while relying on automation, and how to operationalise 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 personalised 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 personalised learning recommendations to support career growth. But as more organisations consider practical use cases like these, there’s a central obstacle that stands in the way: their people’s data. And that’s one of the major topics that our new Lab is exploring.

The first challenge is the format of HR data.

To be effective, AI tools need data to power algorithms and models—and the more structured that data is, the better. But while some HR data is qualitative and numerical (i.e. retention rate, hiring rate, etc), much of HR is unstructured—think CVs, performance reviews and notes from exit interviews. This type of data needs more than number-crunching capabilities alone—it calls for natural language processing to accurately collect and analyse data in the form of written or spoken words.

Other major challenges include privacy and security.
Workplace data has powerful implications, but it’s also some of the most sensitive data at any company. In addition to shielding it from external bad actors, organisations 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 analyse 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 solutions today are not AI tools but in fact business intelligence (a.k.a. big data) tools: 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 organisation and be able to provide predictive and intelligent decisions and recommendations.

The Cornerstone Innovation Lab for AI looks into new opportunities to fully unlock AI’s capabilities for cross-functionality, moving beyond the process-driven and the transactional in order to enable organisations to be truly transformative and accelerate the growth of their people and their business.

AI Will Humanise Work—and Improve People Experience

As our new Lab continues to explore these and the myriad other challenges around AI, we’re already seeing some success in applying best practices to our own AI engine.

Recruiting. Cornerstone’s AI engine, the Cornerstone Skills Graph, can analyse a job applicant’s CV and capture a candidate’s 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 analysing 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 next steps to continue on their learning journey.

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 analyses learning behaviours, 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 personalised, holistic work culture designed around development.

Humans Are Still More Essential Than Ever

When AI is implemented successfully, the possibilities to transform (read: personalise, humanise and improve) people 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.”

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, prominent sexism or racism in hiring, at an organisation. This is another ongoing area of focus for our data scientists. Fundamentally, while AI can reveal biases, it can’t get rid of them—that comes down to people.

As organisations increasingly implement AI across their businesses, they must have one goal in mind: improving the experience for their people in real, practical and ethical 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 look forward to sharing more with you in future.