Level AI

For years, contact center quality management (QM) solution providers won business with the message: "We can analyze 100% of your contact center conversations." Yet, in 2026, QM solutions can and must do much more. Industry leaders like Level AI, Observe.AI, and AmplifiAI have long recognized this and are pulling the market in exciting new directions. Alongside them are a host of other visionaries winning business in the space. Consider Oversai. Built by the former Founder of Playvox, it thinks of QM at the contact center level, not only agent level, pulling in observability and VoC data to unlock new insights. Centrical is another innovator. It has created a connected employee ecosystem where QM insights automatically translate into coaching actions, learning experiences, and gamified journeys. Then, there's Cresta. It allows quality analysts to define the outcomes they hope to drive. From there, it evaluates performance to recommend agents to coach and which behaviors to focus on. Those are just some examples. Scorebuddy, evaluagent®, and Verint have their own differentiators, while Zendesk and Balto are also making moves in the space. In this market overview recap, CX Foundation's Charlie Mitchell breaks down the state of the contact center QM space and shares more on the vendors that stand out: https://lnkd.in/ereMdvbp #contactcenters #qualitymanagement #qualityassurance

Every marketing leader knows the seduction of a clean number. The kind of metric you can drop into a deck and let it do the talking. A new report on 3.4 million enterprise support conversations reminded me why that instinct can quietly lead you wrong... (p.s. the same blind spot show up across marketing!) The report's core finding: contact centers score the 2-5% of conversations they choose to look at, and remain structurally blind to the pattern sitting in the other 95%. While averages might check out, the details don't get eyeballs. Some highlights: → One theme averaged 3.15 iCSAT, which is... fine, average, and probably clears most SLA thresholds. Underneath that though, 47.8% of customers rated the experience low satisfaction and 40.8% rated it high. Only 11.4% actually sat in the middle. → Another issue around billing offers touched 0.16% of an account's volume and carried its lowest satisfaction score on record. ... But at an industry-standard 2% review rate, they would've caught maybe 3 examples. That's easy to dismiss as noise... but at those satisfaction scores, is also the actual signal. Our marketer's version is uncomfortably familiar: e.g. We build case studies from the customers who say yes to being featured. We write objection handling from callouts our AEs remember. Then we make positioning bets (and roadmap bets) on the aggregate story we've constructed from a self-selected fraction. That's marketing's 2% problem. So, for CX leaders AND marketers: 1️⃣ Ask what shape the underlying data has, not what the average says. 2️⃣ Assume the sharpest signal is in the conversations no one is reading, and hunt deeper

Most AI agents can check an order status in 12 seconds. They cannot tell a customer which running shoe is right for her flat feet and a half marathon. That gap is leaving revenue on the table on every service call. 💰 Retail contact centers handle two types of contacts: 1️⃣ Transactional — order status, returns, store hours. Routine. 2️⃣ Discovery — questions that end in a purchase decision. Most AI deployments cover the first. Almost none are built for the second. Assisted product recommendation calls convert at 2-3x the rate of unassisted digital browsing. That's not a small number. The AI agent opportunity in retail isn't just deflection. It's two things: ➡️ AI handling transactional volume so human agents are free for recommendation calls ➡️ A virtual agent trained on top-performer conversations that handles discovery at a scale human staffing never could 👟 Most retail AI programs are only capturing the deflection side. The revenue side requires an agent trained on what the best agents actually say; not on a product catalog. At Level AI, virtual agents handle the full conversation. Trained on real top-performer data. Built for guidance, not just answers. Wanna find out how we've helped some amazing Retail brands? Lets talk

Every hotel and airline is drowning in guest feedback. Surveys, app reviews, and the phone calls coming into the contact center. Most brands read the surveys and reviews. Then they let the calls pile up in a folder nobody opens. Which is a shame, because the calls are where guests actually name your competitors. Think about it. When a loyalty member calls to ask about a status match, they say the other brand's name out loud. When someone's frustrated about a refund, they tell you which airline handled it better. When a guest mentions a cheaper rate they found, they hand you the exact number they're comparing you against. All of that is sitting in your recordings right now. And none of it reaches the people running loyalty, pricing, or the decisions that made the guest pick up the phone in the first place. So by the time you spot the threat in your churn numbers, the calls have been shouting about it for weeks. That gap is the whole reason we built Level AI. We listen across every conversation, pull out the competitor mentions and the complaint patterns and the mood shifts, and hand them to the teams who can actually do something, with the call itself as proof. If you've ever wondered what your call recordings are trying to tell you, let's talk shop.

The "all-in-one" AI agent is an enterprise myth. Here’s why: When one bloated bot tries to navigate clunky legacy systems, handle complex compliance rules, and recover from a mid-process error on day five of a two-week workflow... things break. Hard. Enterprises don't need a black-box bot holding the entire customer state. They need Orchestration. In our latest blog, we break down why the true path to scalable automation relies on a balanced approach: - Targeted Micro-Agents: Focused, lightweight agents triggered by specific backend events, not endless workflow loops. - Strategic Human Checkpoints: Keeping human experts in the loop for high-stakes decisions to eliminate fraud and compliance risks. - End-to-End Observability: Tracking every call, text, and backend update on a single unified timeline to prevent quality blind spots. Stop risking your customer experience on fragile, long-running agents. Learn how to pair native AI voice engines with micro-agents for rapid resolutions without the risk. Read the full breakdown here: https://lnkd.in/daAfTs3N

"We write our playbooks for 80% of the time they go this way, and I leave room for 20% nuance." Rebecca Holland, Founder at GTM RH Advisory and former VP of Global Customer Success at Vimeo, on how to keep customer success from quietly turning into support. Her rule gives teams a repeatable system for the predictable 80%, and space to read the room for the 20% that is unique to each client. Then she frames every customer interaction the way a volleyball player would: "It's a 0-0 game." You prepare, you do your prep call, you coordinate and choreograph the meeting, and whether the renewal goes well or badly, you win together and lose together as a team. The mindset keeps CS proactive and strategic instead of reactive. Full episode: https://lnkd.in/dq9jBrFz

Margins and Retention are not to be talked in these days of heady "ARR Growth" in AI. In fact Gross Margins and Retention are the two Golden metrics of business traction, could not agree more with my friend Jared Sleeper. Gross Margins, give you a sense of durable economic advantage. Can you become a sustainable business? Can you be default alive when the market turns? Can you do high AI workloads while doing an architecture that supports it? Retention, is the ultimate currency of customer value. Is your product a good to have or must have? Can you sustain cycles of buyer budget changes? Are you a feature ready to be eaten by model companies or a real product? At Level AI we obsess over both, maintaining 65-70% Gross Margins while doing Trillions of tokens and very high GRR! That is the real competitive moat for us.

I came across Atonom's CX Awards last week. I nominated a number of people I thought should be included. Suddenly, I found myself on the list. Thanks, Luke Jamieson! If you want to vote, go for it - there are a *lot* of great people across 4 categories, many of whom I know personally. Being mentioned in the same breath as some of these people is all the award I need. But the real value here is for those looking to expand their network with people who provide value on LinkedIn for anyone in or adjacent to the CX space. If you're not already following or connected with people on this list, you're missing out. You're missing out on insights from people at brands doing the work every day. You're missing out on thoughtful, educational content from people who know what they're talking about. You might be missing out on the perfect person to fill that role you're hiring for, now or in the future. You're missing out on a larger CX community where people give without expectation of getting. Voting ends August 17th. https://lnkd.in/gJytBC6S