CX Today

Personalized customer experience is no longer just about using the right data to deliver a better message. As AI becomes embedded across customer journeys, contact centers, marketing workflows, and digital channels, CX leaders are facing a more difficult question: when should AI be allowed to act?

That question sits at the heart of this CX Today conversation with Nicole Willing, Kristen Zhou, VP of Product at Treasure AI, and Guy Bourgault, Head of Agentic Systems at Concentrix, exploring why AI-powered personalization creates new risks around consent, governance, data access and customer trust.

For Zhou, the biggest shift is that consent can no longer be treated as a static checkbox. AI systems are now capable of inferring preferences, observing behavior, enriching profiles and recommending actions based on signals customers may not have explicitly shared. That means enterprises need clearer permission structures, stronger guardrails and a more thoughtful way to decide where human oversight is required.

Bourgault agrees that customer preferences are becoming more dynamic. Rather than simply capturing a preference and storing it in a customer relationship management (CRM) system, organizations must be able to update, apply and prove that preference across every relevant interaction.

That is where many businesses still fall short. Bourgault warns that enterprises often struggle less with collecting preferences and more with execution and auditability. As agentic AI systems take on more responsibility, leaders need to know why an AI system makes a particular decision.

The discussion also highlights the contact center’s growing role in managing preference integrity. Bourgault argues that accountability is shifting from marketing toward customer support because the contact center is where escalation, care, and real-time customer interactions already converge.

Zhou adds that businesses need an intelligence layer capable of connecting customer context, decisioning, governance, and activation. Without that, personalization can quickly become fragmented or intrusive, especially when AI-generated recommendations draw from inferred intent.

Watch the full interview to learn how CX leaders can balance personalization, trust, compliance and AI-driven scale. 

When Wayne Butterfield posted his reaction to the 2026 Gartner Magic Quadrant for Conversational AI, he wasn't expecting much more than a few nods from peers. What he got was close to 100,000 views and 500 comments....most of them agreeing.

In a conversation with CX Today, Butterfield explains what bothered him most about the report. His core issue is a perceived contradiction in how acquisitions were treated. Salesforce and SoundHound were placed in the Leaders quadrant following acquisitions, while NiCE Cognigy dropped from Leader to Visionary after being acquired by NiCE. To Butterfield, that asymmetry is difficult to justify.

He also raises a broader concern about scope. With over 650 vendors operating in and around the conversational AI space, a report covering 14 of them will inevitably leave serious gaps; including, notably, Microsoft.

But the interview is more than a list of grievances. Butterfield is direct about where he thinks enterprise buyers go wrong, arguing that too much weight gets placed on analyst rankings and too little on implementation quality and commercial fit.

His advice to enterprise leaders is to build a shortlist based on people, commercial flexibility, and a vendor willing to share in the risk.

The quadrant is a starting point, not a conclusion. 

In this CX Today interview, Rob Wilkinson speaks with Hannah Deveney, Senior Director, Product Management at Zendesk, about the rise of the agent system and why service leaders need to redesign operations around humans and AI agents working together.

The conversation explores how customer service is shifting from traditional team structures to coordinated systems where multiple agents can resolve work end to end. Deveney explains why the next phase of AI is not about bolting on another assistant, but about rethinking workflows, knowledge, routing, and orchestration across the entire service environment.

The interview also examines the new roles emerging to support this shift, from AI managers to knowledge and service design specialists, and what these changes mean for organizations trying to scale automation without losing control of quality or customer outcomes.

For customer service and contact center leaders, this discussion offers a practical look at what it takes to build a service model designed for human and AI collaboration.

Rob Wilkinson speaks with Mark Ashton, VP Solution Consulting CRM, EMEA at ServiceNow, about why traditional CRM is no longer enough for modern customer experience.

They explore the growing gap between speed and empathy, why AI projects fail when built on weak foundations, and how disconnected front, middle, and back-office processes create friction for both customers and agents. Mark also shares practical examples of where organizations are using AI and connected workflows to reduce order exceptions, improve resolution times, and free up teams to focus on higher-value customer interactions.

If you are a CX, CRM, or service leader trying to balance automation, trust, governance, and real business impact, this conversation offers useful lessons on where to start and what to avoid.

For more Customer Experience tech news visit CX Today.

In this CX Today interview, Nicole Willing speaks with Martin Taylor, Co-Founder and Deputy CEO at Content Guru, about the market forces reshaping contact center as a service (CCaaS), from vendor consolidation and AI acceleration to rising customer expectations and the pressure to modernize legacy platforms.

Taylor explains why customer experience has become one of the most important battlegrounds for competitive advantage, and why enterprise buyers still running on legacy on-premises contact center infrastructure may find it harder to keep pace with AI-enabled innovation.

The conversation explores the shift from generative AI toward agentic AI, where automation can move beyond summarizing and transcribing to resolving more complex customer queries and orchestrating data across systems. Taylor also shares why many AI projects fail to prove return on investment, and why organizations need to baseline current service performance before rolling out new automation.

For enterprise buyers assessing a replacement platform, Taylor outlines the non-negotiables: vendor experience, security, data sovereignty, sector knowledge, complex migration capability, customer data readiness, and AI orchestration. Taylor also warns against being swayed by a single polished demo when the real challenge is connecting systems, processes, data, and people at scale.

 Gartner VP Analyst Kathy Ross on why AI agents belong in your tech stack, not on your org chart – and what happens when leaders get this wrong.

In this CX Today interview, Associate Editor Rhys Fisher sits down with Kathy Ross, VP Analyst at Gartner, to unpack a provocative Gartner stance: that customer service leaders are fundamentally mismanaging AI agents by treating them like human talent.

With 39% of CEOs already viewing AI agents as employees, the stakes for getting this right – or wrong – have never been higher.

AI agents are powerful. But power without the right management model is a liability, and Gartner's Kathy Ross isn't pulling punches about where most organizations are going wrong.

The scale problem is real: A human agent might affect 30-45 customers if something goes wrong. An AI agent? Hundreds of thousands, in minutes. That changes everything about how failures need to be managed.

The org chart is the wrong place for AI: Placing AI agent oversight under people managers stretches them too thin and puts fundamentally different skill sets in conflict. Operations and technology leaders need to own this.

Accountability has to be deliberate: Ross outlines a clear split: ops leaders own requirements, monitoring, and escalation; tech leaders own observability, integration, and compliance. People managers only get looped in when AI failure directly impacts the human workforce.

This isn't about replacing humans, it's about redirecting them: The real opportunity is using AI to create space for human agents to do what only humans can: build relationships, drive loyalty, and uncover growth.



Read the full Gartner research at gartner.com

Share this with your CX or operations leadership team

Drop your take in the comments: who owns AI agent oversight in your organization today?

For more Customer Experience tech news visit https://www.cxtoday.com 

In this discussion, Rhys Fisher, Associate Editor at CX Today, sits down with Claudio Rodrigues, Chief Product Officer at Omilia, to dig into one of the most pressing questions in customer experience right now: what does truly intelligent, self-learning contact center AI actually look like in practice?

If you're tired of AI conversations that stay at surface level, this one's for you.

The contact center AI story is usually told the same way: clean your data, build your rules, wait months for results. Omilia is telling a very different story.

Claudio Rodrigues breaks down how Omilia's platform skips the data preparation bottleneck entirely, connecting directly to live contact center operations and learning from the ground up:

• The data myth, debunked: Why asking customers to have "perfect data" before deploying AI is a broken approach, and how Omilia listens to 100% of contact center calls to build automation from reality, not assumptions.
• From insight to automation: How the platform identifies automation opportunities, quantifies their business impact, and deploys them to production with built-in safety rails and automatic rollback – all from a single approval click.
• The gap between AI and human agents is closing: What it looks like when AI and human agents are benchmarked in real time, and why Claudio believes the future contact center workforce will be defined by brand experience, not call handling speed.
• The 30-day/six-month/one-year curve: A frank, specific breakdown of what organizations can realistically expect as self-learning compounds over time.

At CCW Las Vegas, CX Today's Rob Scott sat down with Hannah Bloking (Head of Applied AI Acceleration Solutions Architecture, Amazon Connect Customer), Andrei Papancea (Head of AI Engineering, AWS), and Nate Brown (Co-Founder, CX Accelerator) right after their session on why AI pilots fail — and what the 5% who succeed do differently.

Three questions. Three perspectives. Fifteen minutes of the most practical AI deployment advice from the conference floor.

• What CX leaders are really afraid of when it comes to AI 
• What actually kills a pilot before it ships 
• What the organisations that succeed do differently 

Key takeaways:

1. Speed is a differentiator 
2. Iterate — don't aim for perfection 
3. Minimise confusion for customers, agents, and teams 

Reported live from CCW Las Vegas 2026

See Amazon's playbook here

A packed Academy Ballroom at Caesars Forum. Thousands of contact center leaders on their feet. And a conversation about what AI-first service actually looks like when it is no longer a roadmap — it is running, right now, inside one of Europe's most recognised travel brands.

The moment the session closed, Rob Scott pulled three of the people who built that story into a room and kept the cameras rolling.

What followed was something the main stage format simply does not allow. No moderated talking points. No polished announcements. Just the airline, the transformation partner, and the Microsoft product leader behind the platform — talking honestly about what it took, what broke, what surprised them, and what they would do differently.

This is not the version you heard on stage.

easyJet handles millions of customer contacts at the most pressurised moments in travel — and John Leighton, Head of Customer Experience, shared how the expectations on their service team have never been higher. Aggie Pulawski, Executive Vice President at Concentrix, came in not as a vendor, but as a partner sitting inside a genuinely complex three-way relationship — customer, implementer, and ecosystem ally all at once. And Darya Mazandarany, Microsoft's Head of Product for Dynamics 365 Contact Center, was there to answer the questions that do not get asked when a moderator is managing four thousand people and a 30-minute clock.

The result is one of the most candid contact center transformation conversations CX Today has produced — and one of the most relevant for any leader who is serious about what comes next.

The transformation drivers. The partnership dynamics no one talks about publicly. The reality of building a modern contact center when the pressure is live and the margin for error is low. The point at which automation goes too far. The lessons that never make it into a business case. And where all three of them believe this is heading.

In this interview, recorded live at NiCE World London, CX Today's Rob Wilkinson sits down with Dorothy Copeland, Chief Partner Officer at NiCE, to unpack why businesses struggle to move from isolated AI pilots to meaningful, scalable transformation — and why the right partner ecosystem may now be the biggest factor in making AI work at scale.

Dorothy shares her priorities for partner growth at NiCE, breaks down the newly announced NiCE AI Specialization Program, and explains why the shift from point solutions to connected systems spanning AI, data, workflows, and people is becoming the new standard for enterprise transformation.

00:00 Introduction
00:44 Dorothy's key priorities for partner growth at NiCE
01:59 Building a vibrant AI partner ecosystem
02:43 What is the NiCE AI Specialization Program?
04:13 The shift from point solutions to integrated systems
07:07 Why formalizing AI capability in the partner ecosystem matters now
11:01 Closing thoughts

Guest: Dorothy Copeland, Chief Partner Officer, NiCE
Host: Rob Wilkinson, CX Today

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