Hexa Coworking

Hexa Coworking provides physical and virtual environments to companies like TechGenies to foster collaboration, innovation, and growth.

Hexa Global Ventures

Hexa Global Ventures enables member companies like TechGenies to thrive with strategy, talent, and access to capital.

Agency 50

Agency 50 works with TechGenies to deliver a seamless experience in product go-to-market, user experience design, and brand storytelling.

English
Spanish

Artificial Intelligence, or AI, has evolved from a lofty, theoretical idea in science fiction flicks. Now, AI is common at home in AI applications such as Amazon’s Alexa or Apple’s Siri to turn on their lights and start their cars.

As low unemployment rates and booming technology advancements have lodged a growing chasm between available talent and business needs, HR departments and recruitment teams are increasingly turning to AI to unlock more value for their organizations. When the software development is done correctly, natural language software can enable an artificial intelligence that can augment a low to mid-level HR professional’s job, freeing more time for human creativity and high-level problem-solving.

What is natural language processing (NLP) and how does it work?

First, let’s break down this simple concept with a big name. In order for computers, or AI, to communicate with humans, we have to be able to use a common language. Computers, work in binary terms, where people speak in a much more complex language. The advantage of natural language processing software is that it bridges the gap, applying algorithms that convert the unstructured data of human communication into a structured form a computer can read. With natural language software, people and computers can communicate in new ways, and people can assign lower-level tasks to the AI.

Applying AI: Bots for business value 

Investment in AI has exploded, increasing by 746% in the five years between 2011 and 2015, and moving the total from $282 million in 2011 to $2.4 billion in 2016. According to a study by Statista, “In 2020, the global total corporate investment in artificial intelligence (AI) reached almost 68 billion U.S. dollars.”

AI is poised to renovate key HR functions, like talent acquisition, talent development, and HR operations. In fact, more than half of the nearly 400 chief human resources officers surveyed by the IBM Institute for Business Value agreed that cognitive computing could be transformative for these functions.

An investment in AI is simply an investment in another tool for human professionals to use. Some key AI adaptions for HR and recruiting include:

  • Implementing chatbots to answer employee FAQs. These chatbots can offer employees answers to frequently asked HR questions, such as “Do we have Memorial Day off?” or “How do I find my dental provider?”. A chatbot can tackle any question and answer set that can be housed in a database. Not only does using an always-available chatbot speed up the customer service for employees, but it also frees up the human HR representatives from sending their 200th e-mail to say, “yes, we do have Memorial Day off.”
  • Advertising new benefits through chatbots. Employees can easily miss HR e-mail updates about new company benefits in a slew of more pressing communications. But chatbots can reach out to employees with individualized messages. This is the difference between Jane receiving an e-mail from HR announcing a new on-site spin class and a chatbot messaging her: “Hey Jane! We have an on-site spin class today at 4 p.m. I can book you for. Click here if you would like to try it out, and I’ll add it to your schedule.”
  • Getting in front of employee issues. Organizations can track the number of queries employees submit on a topic to more quickly isolate issues. For example, if the system suddenly receives a spike in questions around late benefits cards at the start of a new year, it may indicate an issue. HR leaders can then swiftly identify the issue and communicate with employees.
  • Customizing interview questions. A chatbot can source job candidates for a specific role, and then customize the questions they receive specific to the required knowledge, skills, and abilities (KSAs) for the job.
  • Monitoring the recruiting experience. At the conclusion of the recruitment process, a chatbot can quickly capture a Net Promoter Score survey (“How likely are you to recommend this to a friend or colleague?”) and monitor the data over time to gauge whether the experience is improving – or declining. Especially in a tight labor market, getting recruiting right can be the difference between finding top or mediocre talent.
  • Making a better hiring match. AI is only as good as the data that powers it, and in some cases, poor data has led AI to actually introduce bias instead of eliminating it. But when it’s designed and trained correctly, AI can scale and automate industrial psychology data that helps match talent – not demographics – to jobs. A better match means happier, more productive employees, and lower onboarding and turnover costs for organizations. (In fact, AI can ignore personal demographics like age or race altogether).
  • Improving compliance. AI can aggregate and assemble data quickly and accurately, with fewer errors. Fewer errors mean fewer missed deadlines or errors, and ultimately decreased fines.

Getting it right: Does Natural Language Processing (NLP) work?

 Gleaning the best value from your AI investment requires expertise in the language processing system and data sources in software engineering. Businesses should consider three key questions to guide the development of AI that will add value to the business today, and in the future:

  1. Is the AI solution scalable? Your organization should be able to grow this solution alongside your employees, and it should actively offer time, cost, and resource savings.
  2. Is the AI consistent? Artificial intelligence is supposed to be intelligent, which means the technology needs to perform consistently, or it can’t be trusted to augment jobs.

Is your AI creating new, predicative data? This is especially important in recruiting. To reiterate, quality recruiting AI matches talent to jobs, not demographics. And, in many industries, the diversity and representation at organizations today may be behind where that diversity and representation should be in the future. To prevent a perpetual cycle of homogenous demographics in an organization, the AI must leverage predictive data to recruit, or it will simply continue to recruit people who are exactly alike.

TechGenies can help you answer these questions, and guide your on-demand software development for an AI software solution that both meets your business’ needs today and is poised to meet future needs as well. Contact us today to learn more.

Lee