When customers reach out to support, they don’t care what’s happening behind the scenes. They want fast, accurate answers—without having to repeat themselves, wait in a queue, or bounce between channels.

That kind of speed and consistency isn’t possible with disconnected systems or surface-level automation.

You need more than a chatbot that links to FAQs. You need an AI agent that’s tightly integrated with your tech stack and trained to resolve real issues.

In this post, we’ll look at five real-world examples of brands that transformed customer service with smart, strategic AI integrations. These companies didn’t just reduce ticket volume, they boosted CSAT , scaled faster, and saved millions in operational costs by automating real resolutions across channels.

If you’re exploring AI for customer service , these stories show what’s possible when your AI agent is set up to actually get the job done.

what are AI integrations in customer service?

On their own, even the most advanced AI agents are limited. They can understand language, but they can’t access order data, look up account details, or make changes on behalf of a customer.

Without integrations, they’re stuck guessing.

AI integrations in customer service are tools that connect and supply useful data to the AI agent, to personalize interactions and perform actions on behalf of a user. They connect your agent to the rest of your business, pulling in real-time data, triggering workflows, and performing secure actions—just like a human agent would.

For example:

• CRM integrations (https://www.ada.cx/blog/ai-and-your-crm-why-syncing-your-systems-pays-off-in-csat-and-roi/) let your AI agent recognize returning customers, personalize replies, and update account info.
• Order management integrations enable actions like checking order status, rescheduling deliveries, or processing returns.
• Authentication integrations make it possible to securely verify identity and take action.
• Ticketing integrations allow seamless handoffs, complete with full conversation context.

With the right integrations, your AI agent isn’t just answering questions—it’s resolving issues at scale. That’s the difference between deflection and true automation.

5 real-world examples of AI integrations in customer service

Some of the biggest brands across industries use AI integrations to transform customer service. Let’s look at a few real-world examples of how companies integrate business tools with their AI agent and how it benefits them.

ClickUp

The challenge

ClickUp’s customer base exploded to 12 million users, but their support couldn’t keep up. Legacy email forms and basic chat slowed down resolution and left agents scrambling for context.

The integration

By integrating Ada’s AI agent with their Zendesk Help Center , ClickUp enables the AI agent to synthesize support content with real-time user data, delivering more accurate, personalized answers.

The results

• 40% of customer inquiries resolved without human invtervention
• 20% increase in Automated Resolution Rate
• Faster, more intelligent responses—no scripting required

Check out ClickUp's full story .

Indigo

The challenge

Indigo scaled from one national carrier to eight regional shipping partners, adding complexity to order tracking and putting pressure on its support team.

The integration

Indigo connected Ada’s AI agent with project44’s shipment tracking API, creating a branded, AI-powered interface where customers could check order status directly—no matter the carrier.

The results

• 14% reduction in orders requiring customer service intervention
• 30,000+ customers used Ada’s “Instant Help” feature in six months
• $150,000 saved in monthly customer service staffing

Learn more about Indigo's story .

Grab

The challenge

Grab was facing surging support demand across Southeast Asia and a growing backlog of unresolved tickets.

The integration

Ada’s AI agent was deployed on Facebook Messenger, with three critical layers:

• A no-code FAQ system for common inquiries
• Smart handover to agents with full context
• CRM integration for personalized routing

The results

• 3× higher containment rate
• 23% reduction in operational support costs
• 90% decrease in ticket backlog

Lean more about Grab's story here .

Neptune Flood

The challenge

With thousands of monthly support requests and frequent weather events, Neptune Flood needed scalable support—fast.

The integration

Ada’s AI agent was connected to Neptune’s custom APIs and CRM, enabling real-time support for authenticated users and complex workflows like policy changes, payments, and claims.

Results

• 78% reduction in cost per ticket
• 92% reduction in ticket resolution time
• 30–35% of Hurricane Ian claims submitted through Ada

Read Neptune Flood's story here .

IPSY

The challenge

After merging with BoxyCharm, IPSY was juggling fragmented support systems, siloed data, and inconsistent AI solutions.

The integration

The team unified support with Ada and integrated the AI agent with Kustomer. Ada’s AI agent, now named Glam Bot, could personalize answers, resolve more tickets, and pass full context to human agents when needed.

The results

• 27+ hours reduction in average first response time
• 34 hours reduction in average total resolution time
• CSAT jumped from 58% to 76%
• 816,000+ engaged conversations
• $2.7 million in estimated annual savings

Read the full IPSY story here .

roadmap to ACX maturity

Support teams don’t go from zero to automated overnight.

Like any employee, your AI agent needs to be trained, connected, and coached over time. That’s why we built the AI customer experience (ACX) maturity model —to help you understand where you are today, and what it takes to move forward.

Here’s what that journey typically looks like, with a focus on integrations.

level 1: foundational

AI is live, but disconnected from your systems

At this stage, your AI agent is mostly handling simple FAQs or handing off to a human. It doesn’t have access to customer data, so it can’t personalize replies or resolve most inquiries. You’ve launched, but you’re leaving a lot of value on the table.

What to do next:

• Start small by identifying your highest-volume, integration-ready use cases.
• Use available metadata (like location or language) to tailor basic responses.
• Connect your AI agent to a core system—usually your CRM or OMS—to unlock its first real resolution capability.

level 2: developing

AI can retrieve data, but can’t act on it yet.

Now your AI agent can look things up, like order status or account details, but can’t update records or complete workflows on its own. You’ve moved beyond scripted bots, but automation is still limited.

What to do next:

• Add secure authentication so your AI agent can access and use personal data.
• Map out which systems (e.g. billing, loyalty, subscriptions) need read + write access.
• Start coaching the AI agent to personalize responses using live customer data.

level 3: optimizing

AI is resolving inquiries end-to-end, and improving over time.

You’ve now integrated your key systems and built secure workflows. The AI agent can retrieve and update information, take action on behalf of the customer, and handle more nuanced use cases across channels. This is where the impact starts to scale.

What to do next:

• Expand coverage to more systems to close remaining resolution gaps.
• Use feedback and performance data to continuously refine workflows.
• Build out internal APIs where needed to support more advanced tasks.

level 4: innovating

AI is embedded in your service strategy and driving business outcomes.

Your AI agent is fully connected, trusted to handle a wide range of inquiries, and consistently resolving them with little to no human intervention. You’ve moved beyond ticket reduction—AI is now improving CSAT, reducing cost-per-contact, and driving top-line growth.

What to do next:

• Analyze which remaining intents aren’t automated—and why.
• Identify opportunities to shift from reactive to proactive support.
• Think beyond support: how else can your AI agent drive value across the business?

lay a strong foundation with Ada

AI isn’t magic. It’s systems, data, and logic working together to solve real problems for customers—at scale. But none of that works without integration.

Every example in this post points to the same truth: AI agents only reach their potential when they’re connected to the rest of your business. CRM, order systems, ticketing tools, authentication—these aren’t add-ons. They’re the foundation for automation that actually works.

With the right integrations in place, your AI agent stops escalating and starts resolving. That’s when support becomes faster, more consistent, and a whole lot more efficient.

Whether you’re just getting started or scaling to new channels and markets, integrations are how you get from chatbot to fully capable AI teammate.