The support world is split between people who say: "Humans like to talk to other humans, therefore..." And people who say: "99% of the time, a good LLM trained on your past support tickets can suggest exactly what is needed to fix, therefore..." Objectively speaking,

AI at scale is teaching millions what “AI” feels like. When a major platform’s support bot traps paying users in loops or needs profanity to route to a human, it doesn’t just frustrate customers. It shapes public trust in AI. The miss here is rudimentary: - Hiding the “talk to

If your KPI is “containment,” you will design mazes. If your KPI is “resolution,” you will design exits. “bot-first” is fine, but “bot-only” crosses the line. Recently, Dutch regulators said chatbots can’t block people from reaching a human. Do you think we’re headed toward

“80% handled by AI” sounds great, but might change quickly when you take a deeper look. This number often mixes two different things: containment (no human touch involved) and actual resolution (the customer got what they needed). Because of this, dashboards look impressive,

Is your team willing to work with AI? Wherever resistance exists, it doesn't seem to be about “anti-tech,” but risk, incentives, and trust. Things like: - Fear of job insecurity or losing recognition. - Lack of trust in AI quality. - Past experiences or assumptions that AI

The buzzword to know is agentic AI. What is it? AI that doesn’t just answer but actually decides what to do and then does it. How? 1. Perceives and understands - Reads input (chat message, email, screen, logs, etc.) - Figures out what’s going on and what the user wants. 2.

Earlier this week at the Service Summit Hamburg, we walked the audience through how our AI uses past resolutions to deliver stronger outcomes in real support environments. Sharing this approach with a room full of practitioners was a real highlight. The reaction was immediate.

Over the past weeks, we’ve run simulations with customer support teams, and the performance data has been fascinating. For context, each team had 30 to 50 of their real tickets processed through Pluno to compare responses and see how our AI would have handled those same

From loops to clarity and controlled conversation flows From premature ending to reliable resolutions that actually reach the finish line From black-box behavior to transparent, predictable AI you can train and trust Mainstream tools are causing frustration, dropped

The magic button is here. Imagine this: you’re working through a long queue of tickets. Normally you’d have to add tags, tweak the title so it actually matches the issue, and craft a proper resolution message for each one. But now, instead of clicking through fields and

Your resolution rate spiked after integrating a deflection chatbot? Be wary. Joseph and his team were thrilled to see their resolution rate rise after implementing a very popular AI agent, supposedly the #1 AI agent. But once they dug into those “resolved” (deflected) tickets,

Customer support quality standards shouldn’t live in a spreadsheet. They should shape every support interaction instantly. With Pluno Support QA, you set the standards using simple language, choose exactly how each point is scored, and shape the QA system around how your team

Your CEO keeps asking when you're deploying AI in customer support. Your team is drowning in tickets. And you're paralyzed. Not because AI can't handle it. But because you haven't seen proof it can handle 𝘺𝘰𝘶𝘳 customers yet. So you do nothing. And the backlog keeps growing.

The traditional QA process in customer support is painfully slow and limited. Human QA reviewers can only spot-check maybe 1-3% of interactions, which means most agent performance goes unmeasured, feedback is delayed, and you’re essentially flying blind on quality. An AI QA

The ALL17 early application deadline is tomorrow at midnight Pacific. Apply at alliance.xyz to: - Receive $500K in Funding - Pitch Top VCs at Demo Day - Join the Alliance Founder Network

"I needed someone checking a view constantly to catch open tickets or tickets that were not really resolved, and then manually pull them into another view." This was the reality for one of our customers before switching to Pluno. Here's what nobody talks about when implementing

Your new support agents spend 3 to 6 months ramping up, while tickets pile up and your experienced agents burn out filling the gaps. Why? Because true proficiency takes time: - Product knowledge and technical systems don’t click overnight. - Neither do nuanced communication,

Here's what support teams don't talk about when they think "scattered knowledge base"... it's not just slow. It's a compounding problem. Your senior agents muddle through; they've been around long enough to know where to look. But your new hires? They're lost for weeks.

As a small support team, when manual engineering escalations take hours you don't have, they end up in the backlog, which keeps growing because the queue never stops long enough to catch up. The trade-off becomes stark: do you keep up with your response metrics on the incoming

Most AI support agents fail in the same quiet way. They’re trained to worship the knowledge base. So when a customer asks something slightly off-script, “We changed billing plans last month, why am I still seeing the old limit?”, the agent runs to the help centre articles. But