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You won’t hear that robots are going to take your job here (or in our latest webinar, Man + Machine, with Allegis Global Solutions and HiringSolved). Refreshing … and reassuring, right? What you will hear are the answers to some of the most pressing questions asked during the webinar, from the speakers themselves: Steve Parker, AGS; Jeremy Roberts, HiringSolved; and Josh Zywien, SmashFly. Take note!


Who are artificial intelligence (AI) tech leaders in the industry?

Parker: AI stacks are just now beginning to form, and over the next 6 months you’ll really begin to see the utilization in the industry.  I know Allegis and SmashFly are both partnering and working with a few companies on this front right now (one of which is HiringSolved!), so stay tuned.


How do I get started with this technology? Is it a change in our ATS?

Josh: I think one of the biggest misconceptions with AI is that it’s some sort of highly technical advancement that requires a lot of heavy lifting to implement. Often times, the opposite is true. There’s some required work upfront to ensure a tight connection with your CRM, ATS, and other recruiting technologies, but the beauty of AI rests in its ability to start with basic guidance and data input, absorb new data and learn over time, and ultimately leverage algorithms to automate a lot of unnecessary manual activities.

In short, you don’t have to change your ATS. Likely, it’s in recruiting or sourcing technology you would adopt to complement and work with your ATS to hire smarter.

Jeremy: Start simple. Look closely at your processes to determine what steps you could eliminate and what efficiency you could increase with automation. From an individual contributor level, this may include using Chrome extensions and other tools. From an enterprise level, this could be an additional technology to complement your current ATS or CRM, or it could mean implementing a new CRM or ATS.


In a low-risk, conservative organization, how do I convince decision makers to adopt newer technology such as AI?

Josh: I’d start by evaluating the risks associated with your current process — like regulation compliance, sourcing inefficiency, and limited access to quality talent— and mapping the value of AI technology to those risks. If AI technologies are implemented and used correctly, they can actually decrease risk in an organization by automating processes that may be inherently biased or flawed. The risk of continuing with a broken strategy is often much higher than leveraging new technology to eradicate those issues.

Parker: Another way to think of utilizing technology is actually for risk avoidance. For example, utilizing a chatbot to deliver content from a single and accessible repository may communicate a more consistent and controlled message to potential hires. Also, I tend to look at the cost or impact of not doing something new, along with the business case of why. Consider what happens to your organization when competition starts testing new technology that could change the experience and process both internally and externally.  

Jeremy: It’s all about saving money and increasing efficiency. Using AI should add new efficiencies and make your team faster and smarter. Break down your process, really honing in on where you could reduce spend and automate tasks to free up time. Where could you get smarter? Once you find those gaps, you can map opportunities in new technology to meet or exceed expectations and results.


Do you have examples or metrics of companies using AI/new tech in their talent acquisition strategy?

Parker: From what I see in the space, companies are starting to baseline their traditional metrics and looking for improvement when applying AI to the inefficient components of the talent acquisition lifecycle.

This all depends on where you choose to utilize AI in your process. If utilized at the top of the funnel, look for larger, more quality talent pools, better candidate flow and increased qualified interviews.  If utilized to rediscover applicant data or screen resumes, then look for increased recruiter efficiency, improved time-to-fill, lower cost per hire and better candidate quality.

Jeremy: From 2008 to 2016, everyone sought to collect as much data as possible. The new challenge is having the right candidates at the right time, ready to engage in a conversation. And to do this, you need the right data. AI can help a recruiting team cut through the clutter, so they are constantly nurturing the talent pools they seek. Smart talent acquisition practitioners are constantly evaluating their own data, prioritizing who they want to nurture and how, and testing technology to accomplish this.


How do candidates feel about automating the recruitment process? Are people receptive to it, or do they desire more one-on-one interactions with recruiters/company?

Parker: From our own surveys, we found that candidate prefer a mobile-driven, seamless and automated process. However, much of this depends on how you design your workflows and experiences. For example, the recruiting workflow that a senior level executive leader follows could vary greatly from an individual contributor’s path.

Jeremy: Candidates seem to appreciate automation in the recruitment process. We’ve all heard about how bad the candidate experience is for large organizations. Automation can provide feedback to candidates, which is appreciated regardless of if it comes from a machine or not. It’s better to know where one stands than to wait on human interaction that never happens. 

Josh: Also, just because the delivery of the communication touchpoint is automated, doesn’t mean it has to sound automated. Have you ever read a hysterical out of office response? Or a great message when a URL is broken or timed out? Automation can be clever, funny and welcomed.


How do you see the AI landscape as it relates to hourly, entry-level hiring? (i.e. retail, food service, etc.)

Josh: I think hourly hiring is a natural starting point for AI. Think about the sheer volume of applications large retailers or restaurant chains tend to receive — particularly during busy, seasonal periods. Or the high turnover that many hourly employers fight. If AI could detect hiring needs or gaps before they became an issue, automatically develop pipelines of pre-qualified candidates, and then trigger targeted messaging to the ones most likely to respond, how would that improve the efficiency of hourly hiring? Throw in the potential for AI-based screening, interview scheduling, and employee onboarding and you suddenly have a pretty powerful — and far more efficient — recruiting strategy.

Jeremy: I agree with Josh that hourly jobs are a great place to start with AI. A conversational interface can be very effective in the initial screening phase to help people know if they are a fit for the organization’s openings. Entry-level hourly roles are often more transactional; for example, a person needs hourly work, an organization has an opening, and the match is made very quickly. More senior-level jobs require negotiation, and talent acquisition professionals may need to spend more time “selling.”


How do HiringSolved, SmashFly and Allegis Global Solutions work together to make organizations more effective?

Parker: There are a few ways! One is helping clients understand how to navigate this new technology landscape, specifically AI/machine learning, primarily to provide guidance on where and how to utilize specific functions. Really, we work together to form a holistic solution that provides additional value to our customers, like the recent integration of HiringSolved and SmashFly. Our combined teams really provide the best two worlds: recruitment marketing technology (SmashFly and HiringSolved) and Recruitment Process Outsourcing (AGS).  Together, we’re able to provide a complete look across the talent acquisition lifecycle for our customers.


Since “Emotional Intelligence” can be measured and given an EQ score, do you think robots can one day be programmed for empathy?

Josh: This is a tricky one. And it’s probably the area of AI I’m most skeptical about. A few weeks ago, I watched an excellent report on 60 Minutes that dove into the development of AI, and there was one quote that stood out: “Artificial intelligence or super intelligence, if we get there, is not necessarily going to be benevolent.”

As we covered in the webinar, AI today is excellent at automating manual processes, processing and learning from large data sets, and identifying behavioral patterns and trends. But it’s not currently a replacement for human intuition and empathy. The optimal scenario is to combine the best aspects of AI and machine learning with the best aspects of human cognition and intuition.


Can you give specific examples of how AI can reduce unconscious bias, and what to be aware of when implementing it?

Jeremy: Say a recruiting team gets an initiative that they need to hire 2,000 people in ‘X’ role by ‘X’ date, with a focus on hiring women. Technologies like HiringSolved and SmashFly could work together to make intelligent guesses about which candidates might be women, stack rank them into a pipeline, and enable your team to filter those candidates based on skills, experience, etc. This won’t eliminate profiles or resumes of qualified people, but it can bring more women into the top of the funnel with higher accuracy and without bias or compliance issues.

Regarding what to be aware of, our CEO Shon Burton has said, “It’s tricky with technology because AI learns from our choices to some degree. If you have a lot of bias in your recruiting and hiring processes, and you push that data through a neural network or AI-based system, then you’re using data that’s inherently biased. As a result, you’ll simply be training AI to be biased.”

Josh: Great example. It’s important to point out that that process example isn’t consciously removing qualified men from the role. Instead, it’s giving more qualified women — or anyone else — a chance to be seen when bias might have eliminated them before. There’s a really great article recently published by SmashFly and HiringSolved in ERE SourceCon for more thinking around unconscious bias.

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