Trustpilot: Your Brand Is Invisible to AI, And Reviews Are the Fix

Trustpilot: Your Brand Is Invisible to AI, And Reviews Are the Fix

How customer feedback has quietly become the most powerful trust signal in the age of AI search  CX Today's Associate Editor Rhys Fisher sits down with Alicia Skubick, Chief Customer Officer at Trustpilot, to unpack the platform's latest research.  With AI now shaping how consumers discover and evaluate brands before they ever land on a website, the rules of visibility have changed. This is the conversation that makes the business case for trust in real, measurable terms.  Trustpilot's latest 'What AI Says About You' report analyzed over 800,000 AI responses, and the findings are hard to ignore. Brands without a Trustpilot profile appear in just 1% of AI-generated answers. Those that actively collect and respond to reviews? 75.3%. Alicia breaks down what that means for CX teams on the ground.  AI behaves like a cautious consumer – it checks ratings, reads review distributions, and flags missing profiles as red flags, just like your most sceptical customer would  Responding beats collecting – brands that respond to reviews (not just gather them) see citation rates jump from 53% to 75%. Generic replies don't cut it; specificity is what AI crawlers reward  The NPS question – Alicia makes the case that open-platform review systems are replacing NPS for many businesses, delivering the same real-time insight with the added bonus of AI visibility  Speed to value is real – setting up a profile and inviting customers can shift visibility from near-zero to 53% in weeks, not months, with AI-driven customers worth 4.4x more on average  Whether you're a CX leader mapping new customer journeys or an ops manager stretched thin, this one's worth your 15 minutes.

Why Pega Is Ditching Token Costs and Betting on Business Outcomes Instead

The Director of Pega's AI Lab breaks down the real mechanics behind agentic marketing operations and why "magical thinking" is killing AI projects before they start.  In this CX Today discussion, Associate Editor Rhys Fisher sits down with Peter van der Putten, Director of the AI Lab and Lead Scientist at Pegasystems.  With agentic AI dominating the conversation across the CX space, Peter cuts through the noise to explain what it actually takes to make it work in enterprise marketing, and why orchestration, not individual agent capability, is the real differentiator.  If you're trying to separate genuine AI progress from hype, this one's worth your time.  Agentic AI is everywhere right now, but most of the conversation stays frustratingly shallow.  Peter van der Putten goes deeper, explaining how Pega's newly launched Customer Engagement Studio works alongside Customer Decision Hub to give marketers a governed, agent-powered path from brief to live campaign in minutes.  Outcome-based pricing: Pega is making a bold move away from token-based costs, betting they can tie agentic AI tightly enough to business outcomes to charge on results instead.  Left brain meets right brain: Peter explains how CDH has handled analytical decisioning for over 30 years, with Wells Fargo running 6 billion next best actions a month through it, and how Customer Engagement Studio layers in generative and agentic AI to solve the content and marketing ops bottleneck, not the decisioning one.  How the agents actually work together: A conversational agent orchestrates specialized agents across marketing strategy, creative, data science, compliance, and performance, each with a defined role, none operating in a silo.  Why 40% of agentic AI projects fail: Peter points to "magical thinking", the assumption that throwing agents at a problem will sort itself out. The fix is embedding agents into real workflows tied to measurable business and customer outcomes.

Stop Letting Your AI Agents Off the Hook

Rhys Fisher, Associate Editor at CX Today, is joined by Dave Rennyson, CEO of SuccessKPI, for a candid conversation about what it actually means to run a contact center where humans and AI agents work side by side As agentic AI takes on a growing share of customer interactions, Dave makes the case that the standards we apply to human agents must apply equally to AI, and that the organizations skipping that step are already storing up problems. Honest, specific, and refreshingly free of hype. The hybrid contact center isn't a future concept, it's the reality most operations leaders are already navigating. Dave Rennyson breaks down what it takes to manage it properly. AI agents are handling the simpler conversations, which means human agents are increasingly left with the harder, more complex ones. Dave argues this demands more recognition and support for human agents, not less. Quality management for AI agents is non-negotiable. Treating AI like a self-regulating system is, in Dave's words, like letting your builder do his own home inspections. Third-party QM, whether manual or automated, has to sit on top. CSAT isn't dead, it's due a rebirth. Dave makes the case for using AI to score every conversation on a normalized scale, cutting through the "haters and lovers" bias that has always made survey data unreliable. "Good" in a hybrid contact center means managing the whole. Dave's vision is workforce decisions made minute-to-minute, based on task complexity, live performance data, and force-to-load, with humans and AI operating as a unified system.

Contact Centre AI Is Only as Good as the Data Behind It – So Why Are We Ignoring the Data?

Rhys Fisher, Associate Editor at CX Today, sits down with Dave Rennyson, CEO of SuccessKPI, to tackle one of the most pressing yet underexplored challenges in modern contact center operations: what it actually takes to build an AI-ready data foundation. As AI agents become a fixture of the contact center, Dave makes a compelling case that the real risk isn't the technology, it's the lack of governance, measurement, and rigor underneath it. If your organization is deploying AI without asking how you'll manage it, this is essential viewing.As contact centers race to deploy AI, most are skipping the hard work that makes it sustainable. Dave Rennyson pulls no punches on what's going wrong and what leaders need to do differently. AI agents actually produce more data than human agents, including failure signals and turn-taking data that human conversations never generate. The opportunity is significant, but only if you have the architecture to capture and act on it. "Ground truth" is the step almost everyone skips. Dave breaks down what it means to establish a reliable baseline for AI performance, why it demands real scientific rigor, and what model drift looks like when you ignore it. Agentic AI is genuinely different from legacy IVR. The removal of linear flow constraints opens up a new design space, but only if organizations build the right orchestration and monitoring layers on top. Rather than declaring surveys dead, Dave argues that generative AI can now appraise every single conversation at scale, turning a historically biased metric into something far more powerful. For more Customer Experience tech news visit CX Today [https://www.cxtoday.com].

From Conversation to Case Action: Automating ServiceNow Workflows from Live Voice with Vonage

Rob Wilkinson speaks with Jonathan Kershaw, Director of Product Management at Vonage, about how live voice can bring real-time voice transcription and context directly into ServiceNow to trigger workflows during the call, not after it. Kershaw explains where the pain shows up most clearly: agents toggling between up to nine systems, inconsistent case notes, delayed case creation, and AI outcomes that fall short because they are missing the most important customer context. He shares practical scenarios, from IT outages to payment and fraud-related calls, showing how native voice in ServiceNow can support intent-based routing, mid-call escalations, warm handoffs powered by AI summaries, and faster wrap-up with automated dispositions. The conversation also covers what to automate first, how to prove ROI using credible baselines, and which KPIs tend to move earliest, including average handle time, after-call work, data quality, and reductions in follow-up contacts. The core message is straightforward: treat AI like any other business tool. Start with measurable operational gains, then scale. For more Customer Experience tech news visit CX Today [https://www.cxtoday.com].