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.
Read the postMost 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 ...