Rikki Lear
Video Overview
Artificial intelligence is rapidly transforming how companies interact with customers. For businesses managing thousands of support conversations every week, scaling customer service without increasing headcount can be a major challenge.
In a recent customer panel hosted by HubSpot, Ryan Orner spoke with Andrea Martin from fintech company Wallet about how they implemented AI agents inside their CRM and the measurable results they achieved.
This real-world case study shows how AI-powered customer support—especially within a unified CRM—can dramatically improve efficiency, scale conversations, and deliver better customer experiences.
The Challenge: Scaling Customer Conversations with a Small Team
Wallet serves gig-economy workers—such as rideshare drivers and delivery couriers—who rely on mobile access to financial tools. Their users often need fast answers about cash advances, account status, or payment timelines.
Because their customers are primarily mobile-first, support interactions happen mostly through in-app chat.
However, the company faced several operational challenges:
- A small support team of five agents
- Thousands of incoming chats per week
- Limited customer support hours
- High volumes of repetitive questions
- A growing customer base
At peak times, Wallet was receiving over 3,000 chats per week—a volume that simply wasn’t sustainable for a small team.
The result?
- Long response times
- Customer frustration
- Team burnout
- Limited ability to scale
The company knew it needed an AI-powered solution that could handle routine conversations and operate 24/7.
Discovering a Native AI Solution Inside HubSpot
While evaluating AI chat vendors, Wallet initially explored third-party AI chat platforms. The team was close to signing with an external provider when something unexpected happened.
Several team members attended INBOUND and discovered that HubSpot’s new AI agents could support mobile chat directly inside the CRM.
That discovery changed everything.
Instead of adding another tool to their tech stack, they realized they could deploy AI directly within the platform they were already using.
This mattered because:
- Their customer data already lived inside HubSpot
- Their knowledge base was already stored in HubSpot
- Their teams already worked in the CRM daily
Using AI inside the CRM meant the system could understand the full context of each customer interaction.
Why Native CRM AI Matters
One of the biggest advantages of using AI directly within HubSpot CRM is access to real-time customer data.
This allowed Wallet’s AI agent to:
- Verify customer identities
- Check where users were in their onboarding process
- Provide personalized responses
- Pull answers directly from the knowledge base
- Escalate complex issues to human agents
Compared to the third-party tools they evaluated, the response quality was significantly better because the AI had access to the company’s actual CRM data.
Implementation: Faster Than Expected
Many companies hesitate to adopt AI because they assume implementation will be complex.
In Wallet’s case, deployment was surprisingly quick.
Within less than one week, the team had:
- Built a proof of concept
- Connected the AI agent to their knowledge base
- Integrated the chat widget inside their mobile app
- Begun testing internal conversations
Interestingly, the biggest effort wasn’t technical.
Instead, the most time-consuming part was training the knowledge base so the AI could provide accurate answers.
This reinforces an important lesson for AI adoption:
AI success depends heavily on the quality of your data and documentation.
Addressing the Human Side of AI
Whenever companies introduce AI into customer support workflows, one concern often arises:
Will AI replace human jobs?
At Wallet, the opposite happened.
With thousands of weekly chats coming in, the support team welcomed the help.
Instead of replacing agents, AI allowed them to:
- Focus on complex conversations
- Handle higher-value support cases
- Provide better customer experiences
The AI agent handled repetitive tasks such as:
- Email verification
- Process status checks
- Basic FAQ responses
- Initial customer validation
This freed human agents to focus on meaningful problem-solving.
The Results: Real ROI from HubSpot AI
Since implementing HubSpot’s customer AI agent, Wallet has seen dramatic results.
Key metrics include:
- 18,000+ conversations automated
- 277 AI chats handled per day
- Nearly 2,000 conversations per week
- 24/7 customer support availability
Previously, customer support only operated during business hours.
Now, customers can get answers any time of day, without waiting for human agents to come online.
Business Impact
The benefits go far beyond support efficiency.
1. Faster customer responses
Customers receive immediate answers instead of waiting in queues.
2. Increased productivity
Human agents can focus on higher-value support cases.
3. 24/7 support coverage
AI provides around-the-clock assistance without increasing staffing costs.
4. Data-driven staffing decisions
By analyzing AI conversation data, the company can optimize staffing hours based on demand.
What’s Next: Expanding AI Across Teams
Wallet isn’t stopping with customer support.
They’re now exploring AI across additional departments, including:
- Collections teams
- Sales teams
- Internal operations
- Knowledge assistants for staff
Using tools like Breeze, the company plans to automate repetitive tasks across the organization.
The goal is simple:
Free employees to focus on the work that matters most—helping customers.
Key Takeaways for Businesses Considering AI
The Wallet case study highlights several important lessons for companies exploring AI adoption.
1. Start with clear problems
AI works best when solving specific operational bottlenecks.
2. Use your existing platform
Deploying AI inside your CRM reduces complexity and improves context.
3. Invest in your knowledge base
Better documentation leads to better AI responses.
4. Use AI to augment—not replace—humans
AI should remove repetitive tasks, not eliminate human expertise.
5. Measure success with real metrics
Track conversations handled, response times, and operational efficiency.
The Future of Customer Engagement
As AI capabilities continue to improve, companies will increasingly rely on CRM-embedded AI agents to manage conversations at scale.
Platforms like HubSpot are leading this shift by embedding AI directly into customer workflows rather than offering disconnected tools.
The result?
- More efficient teams
- Faster customer service
- Better data insights
- Scalable customer engagement
And as Wallet’s experience shows, companies that adopt AI strategically can unlock massive operational improvements without expanding their tech stack.
FAQ
What is a customer AI agent in HubSpot?
A customer AI agent in HubSpot is an AI-powered assistant that automatically responds to customer conversations using CRM data and knowledge base content.
How does HubSpot AI improve customer support?
HubSpot AI can automate responses, verify customer data, summarize conversations, and route complex issues to human agents.
Can AI replace customer service teams?
No. AI is best used to automate repetitive tasks while human agents handle complex or sensitive customer issues.
How long does it take to implement HubSpot AI agents?
Many companies can launch a proof of concept within days, depending on how well their knowledge base and CRM data are structured.
What industries benefit most from CRM AI agents?
Industries with high conversation volumes benefit most, including fintech, SaaS, e-commerce, and customer support operations.
