Examples of Brands Successfully Using AI for Omnichannel Customer Support (and What VPs Can Copy)

Brands succeed with AI for omnichannel customer support when they use it to resolve high-volume requests consistently across chat, messaging, in-app, and assisted channels—while making escalation to humans seamless. The best examples combine fast self-service, agent-assist, strong knowledge management, and clear guardrails so customers get answers quickly without feeling “trapped” by automation.

Omnichannel support used to mean “more places for tickets to pile up.” Today, it can mean one connected service experience—where customers start in chat, continue in email, switch to voice, and never have to repeat themselves.

But as a VP of Customer Support, you know the hard part isn’t buying a bot. It’s making AI work across channels without breaking quality, compliance, or your team’s morale. Customers are also skeptical: according to Gartner, 64% of customers would prefer that companies didn’t use AI for customer service, and 53% would consider switching if they found out a company was going to use AI for customer service. That’s not a reason to pause—it’s a reason to design AI experiences that feel faster, more human, and easier to escape to an agent when needed.

This article walks through real, verifiable examples of brands using AI in customer support, what they did right, and the repeatable blueprint you can apply to your own omnichannel operation—without turning your service org into a never-ending IT project.

Why “omnichannel + AI” fails in most support orgs

Omnichannel AI fails when it’s treated as a layer on top of fragmented systems instead of a connected execution engine that can resolve issues end-to-end. When channels, knowledge, customer context, and workflows aren’t unified, AI either gives inconsistent answers, can’t take action, or creates more escalations than it prevents.

From a VP seat, the risk is personal and measurable: CSAT dips, repeat contacts rise, AHT inflates, and your best agents become “AI babysitters.” The operational pattern is predictable:

• Channel sprawl: Customers message you on social, web chat, app chat, email, and voice—while each channel has different tooling, macros, and processes.
• Knowledge entropy: Articles are outdated, scattered, or written for humans—not for retrieval and resolution.
• Escalation chaos: AI can answer, but can’t do (refund, reset, update, schedule), so handoffs are constant and painful.
• Trust gap: Customers fear AI will block them from an agent—Gartner explicitly calls “difficulty reaching a person” the top concern.

The brands that win do the opposite: they pick narrow, high-volume journeys, connect AI to the systems that complete the work, and design escalation as a first-class feature—not an afterthought.

How Klarna used AI to resolve customer chats faster—at global scale

Klarna’s success shows what happens when AI is designed to resolve real service “errands” across a high-volume channel, not just deflect questions. Their AI assistant handled the majority of chats quickly, reduced repeat contacts, and supported customers in dozens of languages.

What exactly did Klarna achieve with AI customer support?

Klarna reports that after one month live globally, its AI assistant had 2.3 million conversations—two-thirds of its customer service chats—and performed the equivalent work of 700 full-time agents.

• 2.3M conversations; two-thirds of customer service chats
• Equivalent work of 700 full-time agents
• 25% drop in repeat inquiries
• Resolution in under 2 minutes vs. 11 minutes previously
• 24/7 in 23 markets, supporting 35+ languages

Source: Klarna press release (Feb 27, 2024)

What VPs of Support can copy from Klarna’s omnichannel AI approach

You don’t need Klarna’s scale to copy Klarna’s design choices. The transferable lessons are:

• Optimize for resolution, not deflection: Klarna framed the assistant as a “customer service expert” handling refunds, returns, disputes, cancellations, and invoice issues—work that actually clears queues.
• Make language coverage a growth lever: Multilingual support reduces friction and expands self-service reach without staffing 24/7 across time zones.
• Keep human choice visible: Klarna explicitly notes customers can still choose live agents—this directly addresses the trust gap Gartner warns about.

If you want your AI to move KPIs like repeat contact rate, time-to-resolution, and cost per case, Klarna is the modern benchmark.

How Bank of America scaled an AI assistant while preserving speed and handoff

Bank of America’s “Erica” illustrates a mature model: a virtual assistant that handles massive interaction volume, delivers fast answers, and routes customers to live support when needed. This is omnichannel in spirit because it meets customers where they are—inside a digital channel—then bridges to humans for complex cases.

What results did Bank of America share about Erica?

Bank of America reported that clients have had more than 2 billion interactions with Erica since launch, with customers engaging about 2 million times per day, and that more than 98% of clients get answers within 44 seconds on average.

• 2+ billion interactions since launch
• 2 million interactions per day
• 98%+ get answers within 44 seconds on average
• When needed, customers connect to a live representative via “Mobile Servicing Chat”

Source: Bank of America newsroom (Apr 8, 2024)

What VPs of Support can copy from Erica’s design

Three leadership-grade takeaways stand out:

• Speed is a feature: “44 seconds” is not a vanity metric; it’s a promise customers can feel.
• Proactive insights reduce inbound: Erica provides proactive alerts (e.g., subscriptions, spending behaviors). That’s a blueprint for lowering avoidable contact.
• Escalation is part of the experience: BofA doesn’t treat escalation like failure—it’s the safety net that protects trust and resolution quality.

For a VP, this is a reminder: omnichannel AI isn’t just “more channels.” It’s connected containment with intentional handoff.

How Vodafone used a digital assistant across markets and languages

Vodafone’s TOBi is a strong example of building a consistent AI support experience that can be localized and rolled out across regions—one of the hardest operational challenges in omnichannel support.

What did Vodafone do with TOBi?

Vodafone used Microsoft Azure services to develop and customize a digital assistant named TOBi, expanding it to multiple markets in 15 languages to provide fast, relevant customer support while reducing operational costs.

Source: Microsoft Customer Story: Vodafone transforms its customer care strategy (Dec 10, 2020)

What VPs of Support can copy from Vodafone’s multilingual omnichannel play

Vodafone’s blueprint is especially relevant for midmarket and enterprise orgs with multiple brands, regions, or BPO partners:

• Build once, deploy many: Standardize intent models, tone, and policies—then localize language and regulatory nuances.
• Lower cost without lowering experience: Vodafone frames TOBi as improving satisfaction while reducing operational costs—exactly the “both/and” you’re accountable for.
• Invest in language understanding quality: Better conversational accuracy reduces escalations and repeat contacts.

If your omnichannel strategy spans regions, TOBi is proof that scale is possible when knowledge, policy, and localization are treated as a product.

How KLM used AI in messaging to expand digital service without losing the human touch

KLM’s BlueBot (BB) is a clean example of AI support inside a messaging channel with clear boundaries: it helps customers complete a specific job-to-be-done, and hands off to humans when it can’t.

What did KLM’s AI bot do in a customer channel?

KLM announced that customers could book a ticket on Messenger with the help of an AI service bot, enabling booking without intervention of a KLM agent, while being supported by human service colleagues for handoff when needed.

Source: KLM Newsroom: KLM welcomes BlueBot (BB) (Sep 26, 2017)

What VPs of Support can copy from KLM’s approach

KLM’s lesson is timeless: omnichannel AI wins when it does a real transaction in-channel.

• Pick an outcome customers value: Booking is a high-intent, high-friction workflow—perfect for messaging.
• Design the handoff: KLM explicitly positions BB as supported by human colleagues and refers customers when it can’t help further.
• Plan for channel expansion: KLM notes BB would become compatible with additional digital channels, including voice—how omnichannel maturity actually happens.

If your current “omnichannel” program is mostly routing and triage, KLM is a reminder to target end-to-end journeys customers can complete.

Generic automation vs. AI Workers: what these success stories are really telling us

The common thread across these brands is that AI works when it’s treated as a service teammate that completes journeys—not a chatbot that produces text. Omnichannel excellence requires execution across channels and systems, with guardrails, memory, and auditability.

This is where most support orgs hit a ceiling: they can launch an AI experience in one channel, but they can’t make it operate like the rest of the service org. The outcome is “AI theater”—a shiny front door that still dumps work onto agents.

AI Workers are the next evolution: autonomous digital teammates that can diagnose, decide, and take action inside your tools, end-to-end. That means an AI Worker can:

• Read the conversation (chat, email, social message)
• Look up entitlement and customer history
• Execute the workflow (refund, credit, replacement, reset, status update)
• Document the case and close the loop
• Escalate with full context when exceptions arise

If you want to explore the difference between “assistants” and “execution,” read AI Workers: The Next Leap in Enterprise Productivity. And if you want a concrete model for building an AI Worker like you’d onboard a high-performing employee, Create Powerful AI Workers in Minutes breaks down the framework.

One more leadership point: Gartner reports that 85% of customer service leaders will explore or pilot a customer-facing conversational GenAI solution in 2025, but also notes barriers like knowledge backlogs. That’s the strategic wedge: the winners won’t be the teams with the fanciest model—they’ll be the teams that operationalize knowledge and connect AI to real resolution workflows.

Source: Gartner press release (Dec 9, 2024)

Build your omnichannel AI roadmap: a VP-level checklist you can use this quarter

A practical omnichannel AI roadmap starts by choosing the few journeys that dominate volume, then engineering “resolution loops” that work across channels. You can do this without boiling the ocean.

Which support journeys should you automate first for omnichannel impact?

The best first journeys are high-volume, policy-driven, and system-actionable—meaning the AI can actually complete the work.

• Returns, refunds, credits
• Order/shipping status and exceptions
• Account access and password resets
• Billing discrepancies and invoice questions
• Subscription changes and cancellations

How do you prevent AI from hurting CSAT?

You protect CSAT by making escalation easy, transparent, and context-rich—and by ensuring AI answers are consistent across channels.

• Make “talk to an agent” explicit (and honor it)
• Carry context forward so customers don’t repeat themselves
• Use QA sampling the way you’d coach a new hire
• Instrument containment vs. resolution (resolution is the goal)

If you like the “train it like an employee” approach, EverWorker’s method in From Idea to Employed AI Worker in 2–4 Weeks maps cleanly to support: start controlled, coach hard, then scale.

See what an AI Worker for omnichannel support could look like in your environment

You already have what it takes to lead this transformation: your team knows the workflows, the edge cases, and what “great resolution” looks like. The missing piece is an execution layer that can carry those workflows across channels and systems—without turning your roadmap into a 12-month engineering dependency.

What to take from these brands—and what to do next

Klarna, Bank of America, Vodafone, and KLM aren’t successful because they “added AI.” They’re successful because they redesigned customer support around outcomes: fast resolution, multilingual reach, proactive guidance, and seamless human escalation.

If you’re driving omnichannel support, your advantage isn’t picking the perfect tool. It’s building an AI operating model that compounds:

• Start with 1–2 high-volume journeys
• Unify knowledge and policies so answers are consistent everywhere
• Connect AI to the systems that complete the work
• Design handoffs that build trust, not friction
• Scale what works across channels, regions, and products

The goal isn’t to do more with less. It’s to do more with more: more capacity, more consistency, and more time for your best agents to do the work that actually requires human judgment and empathy.

FAQ

What is an example of AI in omnichannel customer support?

An example is Klarna’s AI assistant, which Klarna reports handled two-thirds of its customer service chats after one month live, reduced repeat inquiries by 25%, and cut resolution time to under two minutes, while still allowing customers to choose live agents when preferred.

How do I measure whether AI is working in omnichannel support?

You measure AI success by resolution rate (not just containment), repeat contact rate, time-to-resolution, CSAT by channel, escalation quality (context completeness), and cost per resolved case. Brands like Klarna also publicly cite repeat inquiry reduction and resolution-time improvement as core outcome metrics.

Why do customers dislike AI in customer service?

According to Gartner, customers’ top concern about AI in customer service is that it will become more difficult to reach a person. Successful programs address this by making escalation easy and seamless, and by ensuring AI improves speed and outcomes instead of adding friction.

Source: Gartner press release (July 9, 2024)