Introduction

Great customer experience in 2026 is much less about “wow” and more about certainty: customers achieve the outcome they came for, quickly and correctly, with minimal effort and without sacrificing trust. An old Research in Harvard Business Review argues that “delight” has a negligible impact on loyalty compared with reducing customer effort and resolving the issue simply.  It never was more actual than today with AI. In parallel, CEOs are under pressure to prove AI value, not just deploy tools: IBM’s 2025 CEO study found only 25% of AI initiatives delivered expected ROI and only 16% scaled enterprise-wide. AI can improve CX and cost-to-serve, but it can also scale failure—fluent misinformation, dead-end automation, and inaccessible journeys.  The winners will treat “AI in CX” as a service redesign programme: AI removes friction where confidence is high; humans handle ambiguity and high stakes; governance, privacy, and accessibility are part of the experience itself. As I mentioned in a previous article, I continue to see the same reasons why 74% of enterprise CX AI programs fail.

Now videos with voice! :-) 

1. The AI era did not invent new customer needs.

Customers still look for clarity, fairness, speed, and resolution. What has changed is the baseline of what they consider reasonable: instant summarization, context carryover between channels, and competent answers are now assumed—because customers experience those capabilities elsewhere.

In a world where novelty is cheap, the experience that earns trust is the one that works repeatedly under pressure: no repetition, no ping‑ponging between channels, no “I’ll transfer you” loops, and no confident answers that later collapse.

There is also a timing reality. Forrester argues that 2026 is less about glamorous transformation and more about hard operational preparation—simplifying, restructuring, and building the foundations that make AI-first journeys safe and reliable.  That framing matches what many executive teams are feeling: fast experimentation is easy; scaling consistent value is hard.

2. What “great” means now

A practical executive definition is:

Great CX is the consistent delivery of customer outcomes with low effort and high trust—across channels—at a sustainable cost to serve.

This definition matters because it balances what leaders must optimise simultaneously: customers demand simplicity, boards demand efficiency, and regulators demand accountability. Good AI-era CX is the overlap of those forces.

In practice, “great” shows up as five customer-visible attributes:

• Outcome certainty: the customer knows what will happen next and what “resolved” means as his expected outcome, value.
• Low effort: fewer steps, fewer transfers, no re-explaining the problem.
• Human access: escalation is easy for high-stakes or ambiguous issues.
• Trust by design: privacy and fairness are embedded, not bolted on.
• Inclusive service: journeys are accessible and work for people with disabilities.

3. What AI improves—and what it breaks

AI improves CX when it changes the system of service, not just the interface. In the best deployments, customers experience faster competence: triage that works, context that carries over, and answers grounded in accurate policy and knowledge.

Where AI can deliver measurable value is increasingly well-structured. McKinsey & Company, argues that an AI-powered “next best experience” capability can enhance customer satisfaction by 15–20%, increase revenue by 5–8%, and reduce cost to serve by 20–30%—but only when built as a coordinated capability (data, decisioning, operations), not a disconnected tool.

However, AI also creates new failure modes that customers feel immediately:

• Fluent misinformation at scale: The Air Canada chatbot case illustrates accountability and trust risk when automated interfaces provide wrong guidance; tribunals and analysis of the case emphasise organisations remain responsible for what their systems tell customers.
• Dead-end automation: When companies cut staff prematurely and route customers into automation that cannot resolve their situation, friction becomes the experience. Gartner predicts that by 2027, 50% of companies that attributed customer service headcount reduction to AI will rehire staff to do similar work (often under different job titles), signalling that “agent-less service” is often unrealistic.
• Economics that flip: Gartner forecasts GenAI cost per resolution will exceed $3 by 2030, and that AI-related regulatory changes will increase assisted service volume by 30% by 2028—both of which challenge simplistic “AI is always cheaper” expectations.
• Trust and compliance pressure: UK ICO guidance explicitly addresses fairness and best practice for data-protection-compliant AI, while EU AI Act timelines show regulatory obligations applying progressively, with key enforcement and transparency timings moving through 2025–2027.

The CEO layer is now explicit: AI is moving from innovation theatre to business proof. IBM’s CEO study reports ROI and scaling gaps, and Fortune summarizes that the same research was based on a survey of 2,000 CEOs in early 2025—evidence that this isn’t a niche “digital team” issue anymore; it’s a CEO agenda.

The leadership messaging is also shifting into execution language. The author of the provided Forbes Technology Council, piece summarized the takeaway publicly as: AI isn’t a technology story anymore; it’s a leadership test—requiring a value thesis, speed from insight to action, and governance that enables speed rather than slowing it down.

4. Practical recommendations for leaders

A strong AI-era CX strategy is not “deploy a chatbot.” It is “redesign service so customers feel helped, not handled.” Gartner’s research notes that AI will reshape frontline roles and that many organisations plan to expand human agent responsibilities—an implicit acknowledgement that humans remain essential for judgement, emotion, and exceptions.

Start with a disciplined portfolio of journeys. Choose 2–3 journeys where (a) confidence can be high (clear policy, structured tasks), (b) value can be measured (effort reduction, time-to-resolution, repeat contacts), and (c) escalation rules are clear. This aligns with Forrester’s, Gartner and Eglobalis views that the near-term work is simplifying and restructuring operations to support reliable AI-first experiences.

Build and govern the knowledge base like infrastructure. AI quality is rarely a “model problem”; it is usually a knowledge and policy problem—outdated articles, unclear exceptions, inconsistent definitions. Case studies that show strong results (all imply a boundary between what the assistant can do reliably and what must escalate. Make human access part of the promise.

Govern for trust—not just compliance. ICO guidance focuses on clarifying fairness requirements and best practice for data‑protection‑compliant AI; the EU AI Act implementation process signals increasing expectations for transparency and responsible deployment. Trust (what customers feel) is the business output of that governance (what leaders implement).

Design for accessibility by default. Recommendation status and its broad coverage of accessibility needs make it a practical baseline for B2B and B2C customers.

Finally: optimize for customer value, not just cost—shifting focus from cost cuts to improving services and products—illustrates a central AI-era lesson: cost reduction is not the same as customer value, and pursuing savings without experience quality can erode product, service outcomes and trust.  Think if you want take this kind of risk.

Conclusion

In the AI era, “great CX” is not the bot. It is the system: low effort, fast competence, safe escalation, trustworthy data use, and inclusive design. The organisations that win will be those that treat AI as a service redesign programme and that measure outcomes customers care about, not vanity metrics.

The CEO context is also decisive. IBM’s CEO study and Fortune’s coverage underline an uncomfortable truth: value lags ambition for many organisations, and boards are demanding proof at scale.  The leadership message echoed publicly: consistent with that pressure: value thesis, speed to execution, and governance that enables progress.

If you do nothing else, design for certainty: customers should feel, at every step, “I am in capable hands.” That is what “great” means now.

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My columns in several respected CX publications.

• On Eglobalis: https://www.eglobalis.com/blog/
• On CMSWire: https://www.cmswire.com/author/ricardo-saltz-gulko/
• On CustomerThink as Author number one: https://customerthink.com/author/rgulko/
• German on CMM360: https://www.cmm360.ch/author/ricardo/

My last four articles

1. Designing CX for Non‑Human Customers: AI Agents, APIs, and Machines as Users 

https://www.eglobalis.com/designing-cx-for-non%e2%80%91human-customers-ai-agents-apis-and-machines-as-users/

2. Agentic AI and Customer Innovation: Why Governance Is Now the Key Differentiator https://www.eglobalis.com/agentic-ai-and-customer-innovation-why-governance-is-now-the-key-differentiator/

3. Agent Experience (AX): Why AI Agents Need Their Own Experience Design for B2B https://www.eglobalis.com/agent-experience-ax-why-ai-agents-need-their-own-experience-design-for-b2b/

4. Architecting B2B Experiences for the $15 Trillion Machine Customer Economy: The Trust Paradox https://www.eglobalis.com/architecting-b2b-experiences-for-the-15-trillion-machine-customer-economy-the-trust-paradox/

AI Assistance Disclosure

AI tools were used solely for language refinement, grammar, and structural clarity. All ideas, analysis, and conclusions are the author’s own.