Most organizations that deploy a communication platform end up sitting on a large volume of engagement data — read rates, response rates, participation trends by site and shift — that never gets used for anything beyond a monthly slide. The data collection problem gets solved; ...
Read the post"Workforce analytics" and "workforce intelligence" are increasingly used as if they're interchangeable — two labels for the same dashboard. They aren't, and the difference isn't semantic. It's the ...
Enterprise software spending has, for decades, been shaped by a worker who sits at a desk: a company laptop, a corporate email address, single sign-on into a dozen tools, and a full workday to ...
There's a gap in most mobile workforce software conversations between what gets purchased and what gets used. Organizations buy scheduling suites, communication platforms, and HCM modules with mobile ...
"Workforce analytics" means two genuinely different things depending on who's asking. HR and finance leaders usually mean strategic people analytics — headcount planning, attrition modeling, pay ...
Every category starts as a phrase nobody has defined yet. "Workforce intelligence" is a few years further along that path than "frontline intelligence" — which is still new enough that no one has ...
You hear it constantly in frontline tech conversations: “We need a better distribution solution.” As if distribution is a feature on a roadmap, something you spec out, build, ship, and check off.
Most organizations think about frontline communication technology as a cost to evaluate, something that might pay off, depending on how the numbers shake out.
Here’s an honest thing to say about 14 years of building frontline software: most of the work is invisible.
There’s a version of the frontline AI story that goes like this: buy the AI, train it on some documents, give workers access, and it starts answering questions. Simple, fast, transformative.
You built the model. Maybe you fine-tuned it, built an agent layer on top, gave it tools and context. It answers well. The demos are solid. Your team should be proud.
Every few years, a frontline employer launches a new app. There’s a rollout, maybe some training, maybe an incentive to download it. Adoption is okay at first. Then it drops off. The app sits on ...
The AI industry is converging on a truth that’s uncomfortable for a lot of vendors: the model is commoditizing.
There’s a cost with no line item in any frontline budget. It doesn’t show up in the P&L. Nobody budgets for it. But it shows up every single shift, compounding quietly, in every operation where ...
There’s a number buried in most AI deployments that doesn’t get nearly enough attention: the fallback rate. The percentage of questions the AI answers from something other than your organization’s ...