AI in Customer Support: Success Stories to Learn From

Two strikingly similar headlines surfaced this past week that should make every leader pause: • “Companies Are Pouring Billions Into A.I. It Has Yet to Pay Off.” — New York Times • “Companies Are Pouring Billions Into AI. Here’s Why They’re Not Seeing Returns” — Forbes The NYT points to the human side: employees resist tools they don’t trust. Forbes focuses on the technical side: most AI still can’t understand the context of work. Both are true, and they’re related. When AI lacks context, employees lose trust. It can’t tell the latest doc from last year’s draft. It summarizes a customer conversation but drops the follow-ups buried in the thread. It pulls a response from Slack while ignoring the context in Google Drive. Employees realize it creates more work than it saves, and stop using it. Pilots stall, deployments fade, and projects slide into the “trough of disillusionment" as the NYT describes. Unfortunately, that's the reality for many organizations. At Glean, we work hard to make sure AI understands the enterprise context the way a human does. If a subject matter expert says something, I trust it more. If something’s old, I double-check it. That’s how people think, and it’s how AI should work too. Yet every enterprise has its own documentation culture and quirks, so sometimes we struggle at first. But we persist and co-develop with customers until the system reaches the quality they need. Then we take those learnings to make it work automatically for the next customer. We’ve seen this approach deliver measurable impact for customers: • Booking.com: Glean Agents give teams faster access to customer insights, cutting video production time by 75% and doubling monthly output. • Confluent: Glean’s AI-powered search saves 15,000+ hours/month, boosts support satisfaction by 13%, and cuts ticket investigation time by 10 minutes. • Fortune 100 telecom company: Glean surfaces instant knowledge during support calls, reducing call resolution time by 17 seconds across 800+ agents. • Leading global consultancy: Glean Agents automate RFP workflows, cutting consulting project proposals from 4 weeks to a few hours (97% faster). • Wealthsimple: Glean gives employees instant access to policies and knowledge, driving $1M+ in annual productivity gains. When AI understands the real context of work—across people, tools, and workflows— employees trust it and use it. Instead of falling into the trough of disillusionment, companies climb a slope toward productivity gains and real ROI.

From MIT SMR - how 14 companies across a wide range of industries are generating value from generative AI today: McKinsey built Lilli, a platform that helps consultants quickly find and synthesize information from past projects worldwide. The system integrates with over 40 internal sources and even reads PowerPoint slides, leading to 30% time savings and 75% employee adoption within a year. Amazon deploys AI across multiple divisions. Their pharmacy division uses an internal chatbot to help customer service representatives find answers faster. The finance team employs AI for everything from fraud detection to tax work. In their e-commerce business, they personalize product recommendations based on customer preferences and are developing new GenAI tools for vendors. Morgan Stanley empowers their financial advisers with a knowledge assistant trained on over a million internal documents. The system can summarize client video meetings and draft personalized follow-up emails, allowing advisers to focus more on client needs. Sysco, the food distribution giant, uses GenAI to generate menu recommendations for online customers and create personalized scripts for sales calls based on customer data. CarMax revolutionized their car research pages with GenAI, automatically generating content and summarizing thousands of customer reviews. They've since expanded to use AI in marketing design, customer chatbots, and internal tools. Dentsu transformed their creative agency work with GenAI, using it throughout the creative process from proposals to project planning. They can now generate mock-ups and product photos in real-time during client meetings, significantly improving efficiency. John Hancock deployed chatbot assistants to handle routine customer queries, reducing wait times and freeing human agents for complex issues. Major retailers like Starbucks, Domino's, and CVS are implementing GenAI voice interactions for customer service, moving beyond traditional phone menus. Tapestry, parent company of Coach and Kate Spade, uses real-time language modifications to personalize online shopping, mimicking in-store associate interactions. This led to a 3% increase in e-commerce revenue. Software companies are integrating GenAI directly into their products. Lucidchart allows users to create flowcharts through natural language commands. Canva integrated ChatGPT to simplify creation of visual content. Adobe embedded GenAI across their suite for image editing, PDF interaction, and marketing campaign optimization. For more information on these examples and to gain insight into how companies are transforming with GenAI, read the full article here: https://lnkd.in/eWSzaKw4 images: 4 of the 20 I created with Midjourney for this post. #AI #transformation #innovation

Came back from vacation Monday. Inbox? On fire.🔥 Buried in the chaos: a customer story that stopped me in my tracks (and made me so happy). A Customer Support leader at a fast-growing financial services company used AI to transform his team - in just a few weeks. This leader works for a financial services company that’s in high-growth mode. Great news, right? Yes! For everyone except his Customer Support team… As the business grew faster, they were bombarded with repetitive questions about simple things like loan statuses and document requirements. Reps were overwhelmed. Customers faced longer response times. The company has been a HubSpot customer for nearly 10 years. They turned to Customer Agent, HubSpot’s AI Agent, and got to work: - Connected it to their knowledge base → accurate, fast answers - Set smart handoff rules → AI handles the simple, reps handle the complex - Customized the tone → sounds like them, not a generic bot (you know the type) In a short space of time, things changed dramatically: - Customer Agent now resolves more tickets than any rep - 94.9% of customers report being happy with the experience - For the first time, the team can prioritize complex issues and provide proactive support to high-value customers It’s exciting to see leaders using Customer Agent to not just respond to more tickets, but to increase CSAT and empower their teams to drive more impact. 2025 is the year of AI transformed Customer Support. I am stunned by how quickly that transformation is playing out!

Klarna’s AI-powered customer service is a masterclass in how to scale CX without sacrificing quality. OpenAI helped them automate 66% of their CX workload and add $40M in profit to their bottom line. Here's how it went down: When they rolled out their AI assistant (powered by our friends at OpenAI), Klarna wasn’t just testing the waters—they were making a huge bet to transform their customer service. With over 150M customers worldwide, this was a bold move. But it paid off. According to Klarna's CEO, Sebastian Siemiatkowski, AI agents got them some wild outcomes: → 2.3 million conversations handled in 1 month (2/3 of their total service chats) → Replaced the need for 700 full-time human agents → 11-minute resolution times down to 2 minutes with CSAT scores rivaling human agents AI in customer service can be a double-edged sword: If it works, it’s transformative. If it doesn’t, you lose customer trust—and fast. Klarna understood this and made their AI assistant feel like an extension of their brand. How? → Made it available 24/7 in 23 markets and 35+ languages → Matched the AI with the brand’s tone and style to make interactions consistent → Designed core features like the personal financial assistant to align with Klarna’s values of smart banking Their success highlights a bigger trend: as AI agents rapidly become more capable, brands that leverage them well will have a competitive advantage by exceeding customer expectations. This involves really molding the AI around your business logic to look up data, take actions, and more. That is exactly what we do at Decagon. Klarna would never have been able to add $40M in bottom-line revenue without using AI agents in their CX motion and I'm seeing more and more brands have the same realization: AI agents are the most effective and proven path to efficiency and quality at scale in CX.

Lately I’ve been reading a lot about hyper-personalization and AI customer experience, something we’re evolving day in and out with Zendesk AI. A recent CMSWire article caught my attention, alluding to the retail industry leading the charge on personalized AI experiences and I couldn’t agree more. Many retailers have nailed tailoring interactions to individual customer needs, because in such a competitive marketplace they need their customers to feel seen, heard, and understood to retain them.  And retailers aren’t just focusing on personalization with AI, but efficiency and customer satisfaction as well. A great example of this is one of our global retail customers, Next, who has found Zendesk AI has allowed their customer representatives to focus less on simpler tickets and more on complex issues. This has led to a 15% decrease in average handling time and the ability to roll out AI tools at scale across the 127 different countries they operate in. As Head of Customer Contact Experience Technology Raz Razaq says, “The driver [for adoption] was to maintain our high-level service, especially as we’re growing organically.” For retailers operating at scale, AI can be a well-managed solution to fully transform the CX experience, from personalization to self-service to omni-channel support. I love great stories like the one from NEXT, the kind that really show the practical application and far-reaching potential of AI in the industry. Learn more: https://lnkd.in/gZxc6Aip #CX #CustomerStory