Navigating the AI Transformation Journey
Calendly is taking a comprehensive approach to AI implementation across its entire customer experience – not just within the product itself. As CPO Steven Shu explains, “AI has the power to transform the customer experience within an app, across apps, and in every way that products overlap with human experience.”
Bios:
Steven Shu, Chief Product Officer at Calendly Steven brings over 8 years of AI expertise to Calendly. Previously, he led design teams at LinkedIn where he built recommendation products for product onboarding and developed AI chatbots. His background combines deep product strategy with practical AI implementation experience.
Jess Clark, Head of User Experience at Calendly Jess leads Calendly’s user experience initiatives, working closely with Steven to create intuitive scheduling experiences that help people connect more efficiently and achieve their desired outcomes.
4 Unexpected Learnings:
- Parallel AI experiences often fail: Calendly’s experiment with a conversational scheduling chatbot couldn’t retain users because the original experience was more efficient. Building separate AI interfaces can create unnecessary tech debt and learning curves.
- Meeting intensity KPI challenge: Sometimes AI efficiencies can reduce a company’s core metrics (like Calendly’s “meeting intensity”), requiring leadership to make conscientious decisions about value tradeoffs.
- Don’t fix what isn’t broken: When something in your app already works well and delights users, don’t force AI into it just for the sake of innovation.
- The 86% loyalty factor: Companies that provide strong onboarding and continuous educational experiences see 86% higher customer loyalty rates – making AI-powered personalization a critical retention tool.
The Cold Start Problem with AI in SaaS
We’re all trying to figure out AI. But here’s the thing – most enterprise AI initiatives are failing because they’re not becoming part of users’ daily workflows and habits. That’s what Calendly’s leadership team discovered as they embarked on their AI journey.
So What’s Working? A Few Key Learning From 20m+ Users
Calendly has grown to 20m+ users by obsessing over scheduling workflows. When they started adding AI, here’s what they learned works:
1. Solve Real Problems, Not AI Demo-ware
The reality? Users don’t care about AI. They care about productivity. Calendly found their AI initiatives only stuck when they:
- Targeted specific, high-friction workflows users struggled with daily
- Focused on measurable productivity gains
- Integrated seamlessly into existing product experiences
2. Don’t Build Parallel AI Products
A classic mistake? Building separate AI experiences that force users to learn new workflows. When Calendly tried this with an AI scheduling chatbot, adoption cratered. Users preferred their existing, efficient core product.
3. The New AI ROI Framework
Before investing in AI, Calendly’s team evaluates four key factors:
- Customer value (will this meaningfully improve outcomes?)
- Cost structure (can we sustainably deliver this?)
- Data quality requirements
- User experience impact
4. Personalization at Scale is the Real AI Win
Here’s what’s actually working with AI at Calendly:
- Automatically surfacing relevant features based on usage patterns
- Personalizing the experience across all touchpoints (product, support, marketing)
- Using AI to rapidly understand customer segments and needs
5. Measure What Matters
The metrics that matter for AI success:
- User engagement with AI features
- Task completion rates
- Impact on core product metrics (careful: sometimes AI properly reducing usage is good)
- Customer churn and expansion rates
6. Data Architecture Makes or Breaks AI Success
The unsexy truth? Your AI is only as good as your data infrastructure. Calendly found that:
- A unified data architecture is crucial for consistent AI experiences
- AI agents need to “speak the same language” across touchpoints
- Clean, structured data matters more than algorithm sophistication
7. The Multi-Channel AI Experience is Critical
Don’t just think product. Calendly’s winning approach:
- Unified AI voice across support, marketing, and product
- Consistent personalization across all channels
- Training AI on company style guides for brand consistency
8. Cost Management is the Hidden AI Challenge
Real talk on AI economics:
- GenAI costs can explode at scale
- Be strategic about what features to monetize vs include
- Consider building hybrid approaches for cost-intensive features
9. The “Invisible AI” Principle
Key insight: Users are getting AI fatigue. Calendly’s approach:
- Make AI invisible when it’s working well
- Only surface AI explicitly when user trust is needed
- Focus on outcomes, not the AI itself
10. Continuous Education is the New Retention Strategy
86% of customers stay loyal to brands offering educational experiences. Calendly’s winning formula:
- AI-powered contextual feature discovery
- Personalized learning paths based on usage patterns
- Just-in-time education at key moments
4 Things That Didn’t Work Well in AI at Calendly
- Conversational Scheduling Chatbot: Their attempt to implement a conversational interface for scheduling ultimately failed to gain traction. Users consistently preferred the original, more streamlined scheduling experience, demonstrating that sometimes the non-AI solution is actually more efficient and user-friendly.
- Generic AI Recommendations: Early attempts at providing AI-driven suggestions without sufficient personalization or context awareness led to low adoption rates. As Shu notes, “Customers quickly dismissed features that didn’t clearly understand their specific use case or scheduling patterns.”
- Over-branding AI Features: Initially, Calendly prominently labeled and marketed their AI features, which created heightened expectations. “We found users actually became more critical of features explicitly branded as ‘AI-powered,’” explains Clark. “The moment we stopped emphasizing the AI and just focused on the value, adoption improved.”
- Cross-channel Consistency: Maintaining a consistent voice, tone and knowledge base across different AI touchpoints proved challenging. “Our first attempts at implementing AI across customer support, product tours, and in-app assistance created disconnected experiences where the AI seemed to have different personalities and knowledge levels depending on where you encountered it,” admits Shu. This underscored the importance of a unified data architecture and comprehensive training approach.
Most importantly, the product must maintain a genuine product-market fit with customers. As Shu concludes, “Gathering feedback early and often is crucial to making informed decisions about investments that yield returns for the customer.”
The companies that will win in the AI era are those that prioritize problems that truly matter to customers, maintain consistently high quality experiences, and take a holistic approach to every customer touchpoint with their brand.
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- Source: https://www.saastr.com/10-key-lessons-from-calendlys-cpo-and-head-of-ux-on-building-ai-that-actually-works/
