Automated Talent Management: 5 Myths About Artificial Intelligence and HR

Eightfold
Eightfold

Myths, fears, and unfair expectations still persist in discussions about artificial intelligence and automated talent management. It is a technology that is transforming many aspects of business, but it is neither a panacea for nor a threat to those business operations.

Below, we will explore five of the most common myths surrounding artificial intelligence and its applications in HR.

Myth: AI Will Replace Recruiters

AI-based recruiting tools are very good at screening candidates for their strengths and at predicting someone’s chance for success in a given role. But such tools cannot replace the work a recruiter does.

“It goes without saying that recruitment is a people-first function,” writes Rebecca Skilbeck, the head of customer insights and market research at PageUp. “Candidates want to speak to a recruiter or hiring manager and form an authentic connection, which they won’t be able to get from interacting with a machine.”

What we see happening instead is AI working hand-in-hand with recruiters to find candidates more efficiently and more effectively. AI’s role in recruitment will be to speed up the manual aspects of that work. 

That frees recruiters to invest more energies in relationship-building.

two coworkers review a blueprint; artificial intelligence, automated talent management concept

Myth: AI Recruitment Tools = Chatbots

Yes, AI-powered chatbots are becoming more and more a part of the recruitment process. But AI has so many more applications than simple candidate-facing conversation.

If you have access to data on members of a candidate pool (which you almost certainly do), AI has an uncanny ability to build profiles of these candidates. It can deduce what motivates people in their work, what their capacities for leadership are, and what their general demeanor is like when working with colleagues.

These insights are useful in several ways. Josh Bersin, founder of The Josh Bersin Academy and a member of Eightfold’s Advisory Board, points out how companies with the most impactful hiring policies (i.e. they see the highest ROI on each hire) spend significant time assessing personality traits among candidates, like ambition, passion, and a sense of purpose. 

In other words, there is a strong correlation between the presence of such traits and how valuable a person will be for those companies. That’s why it’s important to invest in recruiting tools that give you a comprehensive view of each candidate.

AI can also help you preempt the need for recruitment by understanding how engaged current employees are in their work. As Node founder and CEO Falon Fatemi writes, AI-based monitoring tools can help companies get a sense of how accountable a given employee feels about their work.

“[U]sing AI, companies can acutely understand employee participation and engagement during company-wide events such as All Hands meetings where leaders discuss the company’s mission and overarching objectives,” Fatemi writes. “How do employees’ tone of voice, body language, or use of words shed light on accountability?”

When you have this level of insight into employee engagement, you can begin to anticipate turnover and perhaps intervene before you lose a valuable member of your team.

Myth: Automated Talent Management Tech Will Set Unrealistic Goals for Workers

Historically, leaps forward in workplace technology create leaps forward in workplace productivity. 

Case in point: Imagine doing your current job in the 1980s, before email or conference calls or shareable spreadsheets. Your productivity is astronomically higher today than it would have been then. AI is having similar impacts on workplace productivity.

There is a fear, however, that AI will allow managers to track an individual person’s performance to such a degree that productivity goals will come to dominate all aspects of work.

In most work, though, this isn’t likely to be the case. As IBM researchers Nigel Guenole, Ph.D. and Sheri Feinzig, Ph.D. write, AI systems must be designed to empower workers rather than to oversee workers. In this context, the systems that win out in the software marketplace would be those that augment work, give people autonomy over decisions, and leave plenty of room for human override so that teams aren’t simply responding to algorithmic instructions.

Further, Guenole and Feinzig argue that AI be deployed transparently. “For managers to feel comfortable working with an AI recommendation, it is important that there is clarity and transparency about why AI recommendations are made,” they write. 

“This should include making clear to managers and employees what the recommendation aimed to achieve, what data were used to make the recommendation, which variables influenced the recommendation most, as well as identifying all the variables on which the recommendations were based, and the expected accuracy of the recommendation.”

Another important aspect of this conversation is how AI-facilitated work will be increasingly less task-based, and this is for financial reasons, says Martin Fleming, chief economist and former vice president at IBM.

“Our research shows that as technology reduces the cost of some tasks because they can be done in part by AI, the value workers bring to the remaining tasks increases,” Fleming writes at Harvard Business Review. “Those tasks tend to require grounding in intellectual skill and insight—something AI isn’t as good at as people.”

The consequence of this is a paradigm shift, one in which work is measured not in terms of tasks completed but in terms of value created for the organization.

And, again, this is work that can only be augmented by automated talent management tech, which is great at analyzing data and providing insights, but isn’t quite able to assess business value in a complete, human-centric sense. That still requires the intellectual skill and human insight that Fleming speaks of.

As this paradigm takes hold in businesses large and small, those that emerge as the most successful will be the businesses that understand value in human terms, not in strict numeric terms.

three coworkers share ideas on a whiteboard; artificial intelligence, automated talent management concept

Myth: You Need Bona Fide Data Scientists to Use AI Technology

Analyzing data about candidates and employees can sound intimidating, but it’s something anyone with the right software can do. 

In general, artificial intelligence technology has matured to the point that it’s sneakily commonplace at work and elsewhere.

As one example, accounting software company Intuit has designed a tool for small businesses called Cash Flow Planner that uses AI to predict day-to-day cash flow as far as 90 days into the future. “With this full view at hand, the planner enables small businesses to get ahead of challenges before they lead to negative consequences,” Alex Chriss, EVP and general manager of Intuit’s Small Business and Self-Employed Group, writes at TechCrunch.

That said, familiarity with the core concepts behind AI are becoming increasingly common requirements for employees, just as Internet navigation skills and Excel skills did once upon a time.

“Everyone involved in HR, from leaders to employees, ought to have a basic knowledge of how AI works,” tech reporter Emily Wong writes at Tech Wire Asia. “They can begin with understanding how AI and automation technology is being used across industries, such as chatbots in customer service, or RPA software in finance.”

Myth: Only HR Teams at Large Organizations Benefit From AI Tools

As Chriss at Intuit above illustrates, AI has a place in all businesses, even among freelancers and self-employed workers.

More and more small businesses are understanding this, too. Brian Westfall, senior HR and talent management analyst at Capterra, reports that in 2019 that more than 4 in 10 small businesses in the U.S. were “already using AI or machine learning, or plan to use it in the next one to two years.” Expect that figure to keep rising.

The technology has all kinds of applications for small businesses, and a point of focus right now should be its use in HR.

“Finding qualified and competitive candidates is challenging when a firm’s circle of influence is geographically limited,” KuppingerCole analyst Anne Bailey writes. 

“A factor often contributing to success in small firms is the ability to hire for organizational fit, thus building tightly knit teams to deliver agile service. To increase the chances of attracting highly qualified candidates, small businesses should focus on using AI systems to support recruiting and hiring for organizational fit.”

Images by: Javier Sierra, This Is Engineering, Javier Trueba

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