AI Accelerates Human Innovation: Robots aren’t Replacements

Three out of every four U.S. tech industry CEOs believe automation and machine learning are likely to replace at least 5% of their manufacturing, technology, sales, and marketing workforce by 2019. With so much innovation expected in less than 2 years, we have a great deal to learn about automation and how it affects the workforce. The KPMG study that reported these findings also mentions over half of the executives expect their organization’s headcount to grow by at least 6%.

CEOs may believe at least 5% of their current workforce will be replaced by #AI Click To Tweet

Robots, Automation and AI: vive la différence?

Most would read those two statistics and see a contradiction of sorts. What it’s revealing is that robots aren’t “taking our jobs”, but may in fact be taking the parts of our jobs that can be automated, making room for the tasks that need a human’s touch to move forward. Take for example the following situations familiar to many organizations and how they display the synergy between human and machine.

  • Nurturing and sales workflows built by humans and run by software.
  • Unenrolling from email lists happens with a click.
  • Archiving and sorting emails according to importance or keyword.
  • Calendaring software automatically making appointments.
  • Reminders are sent at specific times of day to staff.
  • Scheduling is done automatically through software.
  • Long-tail social ads are curated by software and updated automatically.

The list goes on. What this level of automation allows (even in this microenvironment) is for people to spend their time more creatively. Instead of being bogged down with the administration or operation of a task, the team can move into a deeper and more strategic focus on their work.

Automation vs AI: Much Ado About Nothing?

It’s worth noting that many recruitment bloggers, pundits and analysts don’t truly understand the difference between automation and AI (which is why here, even we are blurring the lines a little bit). At Talent Tech Labs, the community is working hard to figure out how discerning we should be in calling out these differences.

At a basic level: automating process through computerization, for example creating automation that fires off different work flows are labor saving and even intelligent. Taking it one step further, using algorithms to find correlations and other relationships like making matches or triggering a response is also intelligent.

However, in these examples of using technology to mimic intelligence we are not using Artificial Intelligence, but instead, building intelligence around what are already known systems, known behaviors and generally known outcomes. Not bad stuff, yes automation, yes delivering intelligence, but not Artificial Intelligence.

It’s easy to understand why so many struggle to understand this. We’ve even struggled to define an appropriate “line in the sand” between automation and AI.

Identifying True AI

When you get into the realm of setting up a machine to interpret known systems and then through learning from enough of those exchanges how different outcomes are called for, that’s when Artificial Intelligence is actually being applied.

Machine Learning is hand in hand with AI. Running those vast amounts of data from the know system realms, and then being able to instruct a different outcome by learning new relationships is the crux of it. (I believe) I’m no expert on the finer points of this all.  But it’s clear that while the earlier 2 examples are automating, they are not teaching the machine and hence I chafe at referring to them as AI.

In AI: think Watson learning to play game board games, or Jeopardy, think Siri or Aria being able to eventually learn from patterns and offer created responses that are not programed responses. Taking facial and voice inputs (unstructured data) and being able to learn from seeing enough of it to predict an output that is learned by being exposed to huge chunks of experiences.

AI and Big Data

AI poses a most likely use case behind the purpose of amassing Big Data. Several prominent HR Technology voices have piped up in defense of Big Data, with the chief concern being that many don’t actually understand Big Data as a concept (which is largely true). Artificial Intelligence certainly has similar issues in a market geared toward solving people problems; one not inherently equipped to understand Big Data or Artificial Intelligence. But the two misunderstood ends of the spectrum need to be clarified so that HR and talent acquisition & management specifically can help shape the outcomes that will drive corporate efficiencies while managing the human consequences, both good and bad, at the man / machine interface.

As a matter of fact, much of the data that HR collects and encounters are unstructured or difficult to map. While a number of emerging products have tried to solve this issue, the one hurdle many have not been able to clear is the sheer amount of processing power and then analysis in getting the data to be actionable. This emerging need will allow Talent Acquisition professionals and more broadly, HR Professionals to move from wondering what to do with data to using it intelligently to positively impact their biggest challenges ranging from sourcing techniques to candidate experience to better managing employee referrals to retention, promotability and so much more.

In the past decade, we’ve gone from having no data, to having data, to having, in some cases, data overload. From healthcare to finance, organizations in nearly every industry are transitioning from merely storing data to using it intelligently. Lonsdale founded ‘investment management technology’ firm Addepar in 2009 to fix multi-trillion dollar segments of global finance by restructuring its data, in order to prime it for the artificial intelligence wave of the future.

Duggan’s article quoted above goes on to posits how the chasm between Talent Acquisition and Talent Management can be served through:

  • Data driven analytics and accurately reallocating recruitment marketing spend.
  • How people work and what motivates them to assess engagement platforms.
  • Finding meaningful data about the best hires and why they work out within the organization.

Fear and AI

With everything that is happening on the Big Data front in HR, it’s natural to assume that people, especially those who work in the general “workforce arena,” are apprehensive of what automation can do, how powerful it seems (when put in the hands of people smarter than themselves) and whether or not it might mean the end of certain jobs. The path from data availability translating into job loss still has a long way to go. But the fact is “Big Data” is the enabler that will allow machines to learn and become intelligent.

Iteration and machine learning are new things that investors and corporations are underwriting with increasing intensity. It is really significant to HR technology where so much of the data is behavioral and unstructured; an ideal application is Big Data fueling AI. As it evolves AI will eventually not only assist in helping with currently frustrating or mundane tasks but can open the door to envisioning what was previously unforeseeable, in essence forecasting what the business may encounter in the future.

At scale, CEOs may believe that at least 5% of their current workforce will be replaced by AI; however, this stat doesn’t take into account the people who will manage these systems and use the findings they reveal to make changes within the workforce.

Artificial intelligence is primed and ready to infiltrate the workforce. Big companies like Accenture, SAP, and Deloitte are trading in their traditional performance management ratings and rankings systems for technologies that bring transparency to data around the work employees do. This is creating huge opportunities for these businesses to leverage frequent touch points and check-ins with employees to get a holistic picture of what’s driving work. As this data surfaces, so does our ability to apply machine learning to compare trends across departments, workers, or organizations as a whole.

Data signals are showing organizations whether their employees are performing at peak productivity, in danger of being poached by a rival organization, or how they want to be engaged. Why does this matter? Because all of those things impact the very ecosystem Talent Acquisition touches, allowing them first hand information to decide what new succession plans look like, what compensation and benefits they should be offering, and how much reliance they may or may not have on the contingent workforce in the future.

So how can you apply artificial intelligence to augment the hiring and recruiting process?

  • Be ready for the data. As stated earlier, where deep data is available, expect startups focused on applying AI to start promoting how these new insights can be applied to tasks that can be automated and actually solved better by learning from the data..
  • Beta test technology. As Jonathan Kestenbaum mentioned when he spoke at HRTechWorld recently:
Matching 2.0 is working. Try solving for your most difficult to match jobs. In essence, beta test these ideas one instance at a time. Chances are, you’ll see a significant matching and screening burden taken off your shoulders, and for costs that are far less detrimental to your recruiting budget. Don’t believe me? Take the costs you’re currently paying recruiting administrators, sourcers, and yourself to manage this process today and compare it to the annual cost of the tool. No contest right?
  • Readily discharge any arduous tasks. Where the technology is able to demonstrate efficiency there will be positive ROI, even if it takes scale to fully appreciate it.
  • Share learnings with the community. Just like the underlying engine to AI are the neural networks; these distributed computers collaboratively solve problems by comparing data on the problem they are programmed to solve. By sharing the results you’re experiencing with innovative solutions you are experimenting with, you’ll be advancing the state of the art. Debunking overstated claims of efficacy or validating those approaches that prove to add value.

Bottom line? It’s tough to be an industry leading organization without one very crucial thing. Your people applying their intellect. Automation and AI are the result of people innovating. To impacting recruiting and talent management, the coupling of the organization’s human intellect and these innovations are essential. It is a key piece of doing things better. Instead of worrying about AI displacing jobs, let’s focus on how you can become well-versed in assessing and applying the feedback you get when you implement an AI program for any piece of your hiring function.

Despite the gray area we’re seeing in identifying true AI in the space, there are examples of AI in recruitment.

  • Textio: After screening 50 million job requirements and submittal data, it has learned how to predict wording changes to make the job requirement more appealing to certain demographics.
  • Talent Objects / Lumesse: integrating Amazon’s Alexa, Talent Objects leverages that voice interface to converse and the system learns from past interactions and is able to predict how to recreate a certain type of hiring scenarios as a robotic assistant to the Talent Acquisition team.
  • Wade & Wendy: Bots that learn from an organizations’ and applicants’ behaviors and responses to create nurturing relationships, all from asking each questions.

Ready to get started? Talent Tech Labs can help you identify and assess new technologies today!

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