Horizon Point Consulting

Tag: ai in talent development

  • AI for Talent Development: Good, Scary, or Both? 

    AI for Talent Development: Good, Scary, or Both? 

    AI is everywhere. Have you recently googled something and used the AI generated answer without ever clicking a link from the list? It’s one of my new favorite efficiencies…and it freaks me out a little. In the “if something scares you, it probably means you should do it” kind of way. We’ve been talking about AI for HR over the last few weeks in the context of our work and industries we support, and today I’m wrapping it up with my take on AI for talent development as a whole. It freaks me out a little, so it’s probably a good thing. Right? 

    Speaking of Google’s AI Overview, here’s what came up when I googled “is AI for talent development?”: 

    Yes, AI (artificial intelligence) is being used in talent management to improve efficiency and outcomes at every stage of the talent lifecycle. AI technologies like machine learning algorithms and natural language processing can help with:

    Recruitment 
    AI can help match candidates with roles based on their skills, experience, and cultural fit. AI-powered semantic search can also help recruiters find qualified candidates by running Google-like searches that go beyond keyword matching.     

    Employee development 
    AI can help create personalized training plans, learning suggestions, and development goals for each employee. AI can also provide real-time feedback based on performance data, which can help employees make immediate improvements. 

    Performance management 
    AI can help set dynamic goals and provide actionable insights to drive employee performance and development. AI can also help with succession planning by analyzing employee data to identify high performers and predict future leadership needs. 

    Skills intelligence 
    AI-powered tools can help HR leaders gain an in-depth view of their workforce’s skills and potential knowledge gaps. This information can help leaders evolve job architectures as skill needs and business priorities shift.

    For each topic featured in the AI Overview, Google provided links to the source material used to inform the AI response. I wanted to know more about AI in Recruitment, so I clicked the link icon and found more detailed articles: 

    (Shoutout to Avature and HireRoad)

    If you’ve kept up with the latest news at Horizon Point, you likely know that I (Jillian) just returned from a 6-week paid sabbatical. During that time, I slept a lot, I made time for hobbies, and I let my brain slow way, way down. Now coming back to work, it’s nice to ease into slow productivity and learn to incorporate the good of AI into our talent development work. 

    I don’t think anyone can say for sure what the future of AI for HR holds, but for now, let’s be curious and explore AI for talent development with open minds. After all, the simple definition of development is the act of improving by expanding, enlarging, or refining, and AI can certainly help. 

  • How HR Can Actually Use AI

    How HR Can Actually Use AI

    As we wrap up our series on Artificial Intelligence, we’ve learned that AI isn’t as scary as some people make it out to be and that we can use it in a variety of ways- but with some caution- in order to impact our workplaces in a positive manner. 

    We’ve tried to emphasize that AI is best to leverage when: 
    You do the task a lot, 
    It is a manual process, 
    It is prone to human error, therefore:
    It’s time consuming. 
    So if you have the data sources you need and the technology to do it,
    Let AI help. 
    And go do something more value added with the time you save.

    As I’ve wrapped up my personal deep dive into AI for HR, I’ve found our friend Ben Eubank’s book Artificial Intelligence for HR to be a useful tool in framing the technologies that can impact HR by functional area.  Here, I’ll summarize some practical uses by functional areas based on Ben’s insights as well as some of my own.  I’ll also recommend some tools I have seen in action. 

    Workforce Management (Time & Attendance) 

    • Clocking in and out with facial recognition
    • New companies are capturing the market of the uberfication of staffing with AI tools to provide labor on demand to fill gaps in staffing.  Check out Onin Flex as an example. 

    Payroll & Benefits

    • Automating many of the payroll processes and checking for errors that many companies still do manually.  
    • Analyzing pay data for pay parity issues
    • Offering on demand pay. Check out Immediate as an example. 
    • Voice activated and/or chatbot technology to respond to benefit inquiry questions or how employees can perform certain tasks on his/her own. 

    Recruiting/Talent Acquisition

    • Screening resumes by keyword search (you’ve probably been doing this for quite some time) 
    • Take it a step further, once you have your technology query candidates by your filters, have the technology reach out to them to schedule the first step in the selection process
    • Use tools to rediscover applicants and match old candidates for other jobs
    • Use tools to rank candidates and let it learn from your rankings to screen candidates (caution: if you put bias in, you will get bias out)
    • Check out LinkedIn Recruiter that has a variety of features to help identify candidates based on a variety of criteria.  One criteria that I find most interesting (and Ben points this out in his book) is Candidate Receptivity. In other words, how likely will a potential candidate be interested in your opening and company? 
    • Use some pretty cool assessment tools.  One company I’ve been following since 2018 when I met them at the HR Tech conference is Pymetrics.  They are worth checking out.

    Learning and Development and Talent Development and Management

    • There isn’t a day that goes by that I don’t hear about the “skills gap”.  It’s a macro issue and an issue at every company with internal talent.  There are tools on the market now that help you understand your internal talent’s skills and then help you hire internally or place people on projects based on skills analysis (Remember, tools like this are only as good as the data you put in them.  If skills aren’t in the database or aren’t accurate, it won’t work.)   A quick google search will give you a list of software tools in this space. 
    • Tools to recommend learning content for users at the individual level and at the organizational level.  Think of your Amazon Recommendation list for learning content. Take a look at page 153 of Ben’s book to understand how this works.
    • Giving leaders tools for coaching based on performance data and feedback so learning content is customized by user.  Voice technology tools that can listen and help coach a manager through specific issues. 
    • Insights to help you better understand correlation and causation between a number of dimensions and employee performance and engagement.  Features can include what if analysis (What if employee engagement rose by X percentage points, how much would turnover decrease?) to sentiment analysis (taking a large amount of qualitative employee survey data, summarizing it and making recommendations for action). 

    Diversity, Equity, Inclusion and Belonging  

    • Identifying biased communication in email, Slack, etc. and in job postings.  Check out Textio as another company I’ve been following since 2018 in this space. Their technology helps with bias and receptivity in job postings and they also have a product for writing better performance feedback.
    • Blind screening tools for recruiting, removing information that would indicate dimensions in which bias may occur. 

    Of course, this isn’t an exhaustive list of things AI is doing in HR, but it is a start. If you are thinking about vetting technology vendors, this may be a good list to begin with by walking through these items and asking, can your technology do this? 

    If it is a comprehensive list HRM system and it can’t do most of these things, or provide API technology to connect to tools that can, you may need to vet other vendors. 

    What functional area in HR are you most interested in leveraging AI technology? 

  • Be Creative Anyway: How ATD24 Made Me Feel Better About AI

    Be Creative Anyway: How ATD24 Made Me Feel Better About AI

    Attending the ATD24 International Conference made me feel so energized and prepared for another year around the sun in talent development. The obvious buzzword: Artificial Intelligence (AI). I walked away with pages and pages of notes on AI in training and development. Mary Ila kicked off our series on AI last week, so now I’m sharing a rundown (written in part using ChatGPT) of my key AI takeaways from ATD24.

    Generative AI: The Game-Changer in Scenario-Based Learning

    One of the sessions that really stood out to me was “Use Generative AI to Create Scenario-Based Learning” by Kevin Alster and Elly Henriksen from Synthesia. They showed us how generative AI can take the heavy lifting out of creating scenario-based learning (SBL). Imagine being able to quickly craft engaging, real-world scenarios that captivate your learners and improve retention.

    The tools and frameworks they demonstrated were incredibly user-friendly, making it feasible for anyone to enhance their courses without needing a PhD in AI. This session made it clear that SBL, powered by AI, is not just a future concept but a present-day reality that can significantly elevate our training programs.

    Navigating the Inclusion Maze with AI

    Then there was the eye-opening session by Mychal Patterson of The Rainbow Disruption, titled “AI Doesn’t Mean ‘Always Inclusive.’” This was a deep dive into the potential pitfalls of AI when it comes to diversity, equity, and inclusion (DEI). Mychal highlighted some serious risks, like biased data leading to exclusionary outcomes and the lack of diversity in AI development teams. These are real challenges that can undermine your DEI efforts if not addressed properly.

    This session was a reminder that while AI offers huge benefits, we need to implement it thoughtfully and inclusively to avoid reinforcing existing biases. We’ve written about inclusive training before, and now we are reminded to be more intentional with avoiding language and representation bias, with or without the use of AI.

    Demystifying AI for Leadership Development

    DDI also showed up strong with Patrick Connell’s session, “Demystify AI for Development: What’s Hype, What’s Real, and What to Do,” which struck a perfect balance between optimism and practicality. He debunked some common myths about AI (i.e. we’re not all losing our jobs) and showcased how it can be a real asset in leadership development.

    From using AI-driven assistants for data analysis to generating personalized content, Connell provided a roadmap for integrating AI into our strategies in a way that enhances, rather than overwhelms. This session made AI seem less daunting and more achievable. Since the conference, HPC has practiced using AI to write first drafts of program learning objectives, training outlines, and more.

    Redesigning Training Programs to Stay Relevant

    Another session that hit home for me was actually during the Chapter Leaders Conference that some of us from ATD Birmingham attended prior to the International Conference. The session was “Making it Competitive: Redesigning Your Chapter Programming to Offer Relevant Knowledge, Skills, and Abilities” by Miko Nino. Miko stressed the importance of continuously updating and evaluating our training programs to keep pace with the changing demands of employers and learners. Using technology to assess and enhance curriculum effectiveness was a major highlight.

    The session also covered developing marketing and financial plans to ensure these programs are not only impactful but also sustainable. It was a comprehensive guide to making our training offerings more competitive and relevant.

    Tackling AI Integration Challenges

    Of course, the conference didn’t shy away from discussing the challenges of integrating AI in training and development. But the consensus was clear: with careful planning and a commitment to ethical considerations, we can mitigate the risks.

    For us, an example might be clearly identifying when something we deliver is made with AI, even in small part. If we use AI to create graphics or images that we share in marketing or in training programs, we need to clearly label those as made with AI. We’re all still learning how to use AI ethically, and it starts with a good faith effort on the front end.

    So…What’s Next?

    ATD24 gave me so many insights on AI in training and development. The sessions highlighted how AI can help make learning more personalized, efficient, and inclusive. But they also underscored the need for thoughtful implementation; the future of T&D is not just about adopting new technologies, but about doing so in a human way that truly enhances learning for everyone.

    For now, my AI journey is all about “do it anyway”. Feel intimidated by AI and use it anyway. Don’t feel very creative? Create anyway. Using AI in my work helps me be creative anyway, and that’s a positive in my book.

    Image made with AI to illustrate the idea of “create anyway”