Fordi

For many, review season is upon us. A few reminders when giving #feedback to your team: 1.) Superficial -> Precise: Giving surface-level feedback is a waste of time so stay away from labels. E.g. instead of "you're not detail oriented enough" (label alert!) -> "I noticed the investor deck had a typo on the cover last month. Let's dig into our team's proofreading process together ..." 2.) Objective Examples & CTAs: Reference objective examples of areas for improvement in a neutral-tone voice. The second that you portray a hiccup in a negative tone, you're inviting the recipient to go on the defensive and shut down to a growth mindset. With each example, end with a realistic action that they can take to address these hangups in the future. E.g. "We always wait for you to start meetings" -> "I noticed that you're sometimes late to our team meetings. Let's work together to manage your schedule so you're not stretched too thin." 3.) Open, Interim Resources: Remind your team of the available resources at their disposal before your next performance review cycle. Even something as simple as "if you're juggling XYZ problem in the future, remember that you can always ask me how we should handle it and I'd love to work together with you to find our best path forward ..." 4.) Positivity & Assume Good Intention: Positively-inclined feedback leaves the recipient more motivated. Even if you're reviewing a serial underperformer, ending with a positive look to the next cycle is helpful. And unless they've repeatedly shown otherwise, assume good intention. E.g. they probably didn't want to give you that deliverable late, they were probably focused elsewhere. Let's dig in there to avoid this in the future ... What have I missed? Let me know! ??

This week's #HRtidbits includes a few thoughts around evaluating (and implementing) HR tech platforms in 2024. With lots of new technologies and #AI gaining momentum in HR lately, year-end is a great time to check in with your existing and prospective software vendors on their protocols around data security and safety, especially as it relates to AI. In addition to the 3 considerations in the post, here are some common pitfalls that we've seen: 1.) Companies are training AI models with customers' sensitive and confidential data. Obviously this hurts companies as their IP is disseminated. 2.) AI-led results replace human autonomy. For example, if an AI-led hiring platform is applying bias in screening resumes without human intervention, this is obviously problematic. This has led to NY's AEDT legislation from earlier this year requiring companies to perform bias audits on hiring software, for example. 3.) AI is scary and nascent. If employees are scared of or confused by AI, cramming "AI-this" and "AI-that" in your org can cause some amount of resistance and change fatigue. I personally maintain the view that it's the job of software companies (including Fordi) to embed the amazing capabilities of AI deeply in our products for users' benefits without constantly shouting at them about AI. _____ As always, if you'd like to contribute to a future #HRtidbits, drop us a line at [email protected] (and we'll tag you ??)

This week's #HRtidbits include some of our thoughts on DEIB plan considerations, which reflect some of Louise Golding's experience in managing diverse teams well as additional insights that we hear in the market as we talk to people leaders every day. We're strong believers that #DEI / #DEIB goals are here to stay (for good reason) and organizations have to consider some nuances and legal restrictions when designing their plans: