Technology

Most testing teams react to bugs after customers find them. It’s not laziness. It’s structure. Traditional QA focuses on detection, not prediction. But generative AI in quality assurance changes that equation. It brings foresight into a job built on hindsight. Imagine QA that doesn’t just check what’s built, it sees what’s likely to break next. […]

B2B customer support has always felt like a balancing act involving tight resources, complex queries, and customers who expect answers right now. Most support teams have spent years patching together email threads, legacy ticketing systems, and overworked agents, hoping it would be enough. It wasn’t. 2025 brings something different to the table. Something that is

Manufacturing doesn’t wait around. Production lines move fast and deadlines move faster. But for years, KPI tracking moved at a snail’s pace. Spreadsheets, siloed systems, and outdated dashboards told you what went wrong, days after it actually happened. That worked when machines were simpler and the pace was slower. But not anymore. Now, decisions happen

Manufacturing doesn’t wait around. Production lines move fast and deadlines move faster. But for years, KPI tracking moved at a snail’s pace. Spreadsheets, siloed systems, and outdated dashboards told you what went wrong, days after it actually happened. That worked when machines were simpler and the pace was slower. But not anymore. Now, decisions happen

Running a contact center used to mean juggling a mess of tools – one for recording calls, another for analyzing them, and a third for reporting results. That mess still exists in many organizations today. And it’s not just messy. It’s costly, confusing, and slow. Most teams are stuck switching tabs, cross-referencing dashboards, and chasing

There was a time when quality assurance in contact centers meant listening to a few random calls, checking off a few boxes, and hoping it all worked out. Managers were stuck grading conversations like teachers with half-written essays like missing the nuance, guessing the intent, and often delivering feedback too late to matter. Agents felt

In most contact centers, there’s always been a problem hiding in plain sight: how do you decide who takes which call? For years, it was first come, first serve. Then came skill-based routing. But even that had limits. What businesses needed wasn’t faster guesswork; they needed foresight. That’s exactly what AI predictive call routing offers.

Everyone’s had that customer service call, the one where you say your name five times, repeat your problem to three different people, and sit through music that sounds like it was recorded underwater. It’s frustrating, and frankly, outdated. Customers don’t want to wait or explain themselves twice. They want to get help quickly and feel

Customer support isn’t about answering questions anymore; it’s about reading between the lines, recognizing intent before it’s spoken, and responding faster than a human could without losing the human touch. That’s where generative AI in customer support finds its edge, not in replacing people but in helping them be more present, more consistent, and more

Design cycles aren’t what they used to be. They’re faster, messier, and harder to keep up with – especially when your team’s juggling tight budgets and tighter timelines. That’s where Generative AI steps in, not to replace designers, but to give them more room to think, test, and build. With help from a Generative design