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Ensure a Positive Candidate Experience When Hiring Contingent Talent Remotely

As digitization, coupled with the global pandemic, propels contingent hiring online and with more individuals relying on employer reviewer sites to evaluate businesses, delivering a positive[...]

March 10, 2021

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How AI Design Thinking Can Inspire a New Recruitment Mindset

It’s virtually impossible to have a conversation about the future of business without centering on the topic of hyper-intelligent technology. It’s a subject that generates mixed emotions -- a struggle in which the excitement of discovery is tempered by a fear that machines will replace human talent. There are many reasons why people worry about a coming singularity. Here’s a thought that I haven’t heard discussed, though. Perhaps people fret because they’re noticing that digital processes are becoming organic, intuitive and agile, while some human processes seem staid and mechanical. This certainly seems to be the case in talent acquisition, where traditional hiring models have become outdated and linear. So why not break the mold and mirror the proven design theories that drive tech? I was recently reflecting on the best practices for developing and managing AI systems, and I realized that recruiters could change the game by adopting similar approaches.

Science Fiction is Science Fact

Clichés aside, science fiction is becoming science fact. This shift in perspective manifests itself in the way we’ve come to think about evolving technologies. Virtual reality is now augmented reality. Artificial intelligence, likewise, has lost its sense of artifice. Machine learning has become, to our modern sensibilities, a more accurate and material definition.

As we’ve been discussing, the robot age is dawning. I sincerely believe that by embracing automation and investing in it wisely, we will discover ways to forge powerful opportunities that secure our future prosperity and wellbeing. This is, after all, the promise of exponential technology. Design thinking, augmented reality, machine learning and the Internet of Things (IoT) aren’t just advancing -- they will soon integrate themselves into every aspect of our professional and personal lives. Even in the staffing industry, these advances will reshape the way we operate. Artificial intelligence (AI), for example, is rapidly making inroads with human resources. We’re seeing a surge in developers launching chatbots, akin to Amazon’s Alexa, that facilitate informed decision-making for recruiters and talent.

Groundbreakers like Google and Amazon haven’t just disrupted entire industries, they’ve completely altered the way we think. In a business context, Big Data has reinvented the methods we use in sales, marketing, customer service, and hiring.

Recruitment marketing is the current gold standard in talent acquisition. The gig economy has influenced this by contributing to a massive decoupling of consumers and buyers. The candidates we prospect are becoming more like consumers; we’re selling and they’re buying. Talent are beginning to view employers as clients, and those organizations are looking at workers as service providers. Given all these shifts, it’s easy to see how marketing insights from Think with Google, the company’s marketing research service, can be applied to enhance the efforts of clever recruiters. It’s something we explored this January.

Last June, we took another cue from Internet leaders by emphasizing the importance of mobility and fluidity to overcome inefficiencies with manual processes in hiring. Candidate outreach, sourcing, engagement and interaction have become mobile experiences. People apply for jobs on their smartphones. Recruiters and prospects communicate through texts and social apps. The concept of “conversational commerce” is gaining steam around the world. We’re using video technologies to showcase employment cultures, conduct interviews and assist with onboarding. And soon, we’ll be tapping into virtual reality to enhance those efforts.

So the question for me remains, “Are human processes keeping pace with technology processes?” In some ways, it appears as though simple systems -- computing actions of past machines -- reflect the ways we continue to handle talent acquisition. In an article for VentureBeat, Will Murphy of Talla, an AI company, breaks down the conceptual transformation from simple systems to AI.

If you entered A and B into the input, C would come out. If you don’t get C, you know you have a defect that needs to be addressed. With simple systems, you can use the same set of test cases over and over again and expect the same outputs each time.

Intelligent agents and other dynamic AI-based systems turn this concept on its head as self-learning software continually adapts its outputs based on inputs from various interactions with other systems and people. Some systems today have gotten pretty complex (especially in the enterprise), but introducing more AI-based algorithms will accelerate complexity beyond where we’ve been in the past. We’ll have systems that go from being difficult to decipher to being indecipherable. And with intelligent agents, we’re massively increasing the number of potential inputs (sometimes, the input could be any combination of words in an entire language), which again dramatically increases the number of potential ways to interpret the input and provide a wider array of outputs.

This is how I see the state of the staffing industry. The new economic models have given rise to different classifications of talent -- independent freelancers, contractors and entrepreneurs. They’ve also laid the foundations for the Human Cloud, where crowd-based sourcing and hiring strategies are reinventing the future of work. So, to Murphy’s point, we have more inputs, more information, limitless talent pools, independent recruiter networks and few geographic boundaries. With all that comes accelerated complexities. Constant analysis and learning are required. Yet, in so many instances, I see industry organizations still relying on simple systems for recruiting: A plus B equals C. That’s not sustainable. However, by following the same techniques that AI designers employ, we can replicate those lessons to enhance talent acquisition.

How AI Design Thinking Can Inspire a New Recruitment Mindset

Domain Focus

Murphy: “Limiting your domain can help limit complexity.” Murphy points out that successful AI design begins with simplifying and focusing on things over which you have control -- a logical set of tasks for the customer and a logical set of knowledge around domain expertise. That’s smart advice for recruiting, as well.

  • Focus on candidate skills sets you know you excel at sourcing and placing.
  • Focus on the types of customers, industries or locations where you have the greatest success.
  • Keep learning so you can expand into other “domains.”

Learning Feedback Loops

Murphy: “Every interaction is a chance to learn.” AI systems, by nature, constantly learn. Software programmers implement “feedback loops” that examine interactions from systems and users to self-correct, learn and provide information to optimize processes. Continuous improvement should also play a crucial role in talent acquisition.

  • Measure analytics and discover what processes or techniques are responsible for successes.
  • ”If it ain’t broke, don’t fix it,” is a poor adage to follow. You can’t know whether something is broken if you’re not looking. And when it does break, you end up in crisis response mode. Actively search for weaknesses or issues that could lead to failure. Regularly try to detect abnormalities or potential obstacles. Relying on honest input from candidates and hiring managers can only help, even if the message is critical.
  • Always consider how the data you collect can be used to adjust current strategies and plan for future needs.
  • Identify trends and develop methods for predicting demands or fluctuations.
  • Begin to optimize at a high level, and don’t over-optimize too soon -- you could fall into the innovation pitfall of creating a solution that may not be needed.
  • Analyze larger, more general sets of data to start. Look for challenges you can help clients solve. As your interactions with candidates, hiring managers and job platforms increase, you can refine your processes over time, based on actual feedback and usage from clients or talent.
  • Check the quality of the data and feedback you collect to ensure that it can be used for training.

Context Counts as Much as Data

Murphy: “Context adds intelligence.” The seeming miracle of AI lies in its ability to deduce, anticipate and change course. That requires context. Data alone can only tell one side of a story. As Mark Twain famously quipped, “There are lies, damned lies and statistics.” Without context, figures alone fail to paint a complete portrait.

Let’s say you’ve got an amazing position to fill for a hugely popular company. The pay rate is great, the culture is incredible and the requirements fall right into your recruiting sweet spot. And yet, candidates aren’t responding. Employment experts and analysts have found that subtle context clues and wording in job descriptions can unintentionally deter candidates from applying to a position. For example, researchers from the American Psychological Association conducted a study of 4,000 job descriptions that revealed a subconscious gender bias toward men. The job postings that contained overly masculine phrasing were also those positions that recruiting professionals found hard to fill when sourcing women candidates.

One challenging description read: “We are a dominant engineering firm that boasts many leading clients. We are determined to stand apart from the competition.” However, more women began to apply when the researchers reworded the description to say: “We are a community of engineers who have effective relationships with many satisfied clients. We are committed to understanding the engineer sector intimately.”

Smart Failover Reactions

Murphy: “Expect the unexpected.” Humans are unpredictable. For AI systems, as Murphy notes, “combining unpredictable humans with unpredictable machines exacerbates the issue.” The same holds true for talent acquisition processes. Regardless of technology, big data and optimized metrics, hiring remains a fairly subjective process. The machine, in this instance, could represent several elements: hiring manager biases, compelling or lackluster job postings, communication gaps, interview scheduling, onboarding processes and so forth. AI best practices involve smart failover experiences that “can ask for clarity or clearly communicate confusion to the user.” Modern recruiting should strive for the same.

  • Always stay engaged with clients and hiring managers. Solicit as much feedback as you can.
  • Interact regularly with candidates, keeping them informed of the process at all steps,
  • Keep the client informed of candidate interest levels, concerns, questions, needs, etc.
  • Plan for the unexpected. It’s best to avoid selecting a single applicant for a position you’re filling. Have a dedicated pool of potential candidates ready to submit. Even better, keep in contact with proven performers from past assignments. Providing them with a steady stream of opportunities allows you to continue making placements and money. This also increases the chances of client satisfaction, which will be rewarded.

Create User Value

AI, like all successful innovations, creates value by solving issues. It’s not the marvel of its inner workings or newness of the technology that appeals to users -- it’s that AI makes their lives easier. Exceptional talent acquisition accomplishes the same for all parties involved: talent, hiring managers, MSPs and staffing agencies. We’re dealing with a bigger world now, full of complex dynamics. There are more inputs and outputs. There are more nuances to address. We can learn much from the development of technology -- the triumphs, the shortcoming, the lessons. And by incorporating these discoveries into modern human processes, we can stay relevant and vital in the digital age.

Sunil Bagai
Sunil Bagai
Sunil is a Silicon Valley thought leader, speaker, motivator, and the visionary behind the groundbreaking Crowdstaffing ecosystem. Blending vision, technology, and business skills, he is transforming the talent acquisition landscape and the very nature of work. Prior to launching Crowdstaffing, Sunil honed his skills and experience as a business leader for companies such as IBM, EMC, and Symantec. "We need to think exponentially to mindfully architect the future of humanity, civilization, and work. When we collaborate and work together, everyone prospers."
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