How AI Impacts Customer Support, with Productboard’s Head of Global Support
Highlights from the AI Speedrun podcast, Pavel Malyshev (Head of Support at Productboard) shares how his team uses AI.
Pavel Malyshev leads global customer support at Productboard - a product management platform used by orgs like Autodesk, Zoom, and Salesforce.
We were especially excited for this conversation because Productboard’s approach mirrors a trend we’ve been watching closely: AI is pulling engineering and support closer together.
At Productboard, support and engineering operate as one system. Pavel shared how his team uses AI to shorten resolution times, streamline escalations, and surface the right context so engineers can fix issues faster.
Here are the highlights from our conversation.
Resolution times are what ultimately matter
Many companies obsess over deflection rates as the north star for support success. Pavel thinks that approach is wrong. Customers don’t care about deflection rates. They care about how quickly their problems are resolved.
Deflections don’t necessarily mean you get the best customer experience. Resolutions do.
Everything Pavel and his team do is oriented towards reducing resolution times and interaction counts. To that end, they use AI to triage issues better and escalate them smarter.
The faster an issue reaches the right engineer with the right context, the faster it gets solved. That’s where AI is making the biggest impact: shortening time-to-resolution by reducing back-and-forth interactions.
His advice to support leaders: Don’t over-index on deflections. Track resolution times and interaction counts. If AI can cut a four-message ticket down to one or two, that’s the metric that matters.
Closing the gap between support and engineering
At Productboard, support and engineering are tightly linked: both measured by product outcomes.
When a ticket comes in, Pavel’s team uses AI to enrich it with technical context, so engineers can get straight to fixing the problem instead of diagnosing it.
AI helps surface patterns, context, and even hints from the code itself, giving engineers the clarity they need to resolve issues faster (and freeing support from endless back-and-forth).
The golden age of DIY
Off-the-shelf solutions like Intercom’s Fin have been useful for async replies, but Pavel thinks DIY is where the real leverage is. His team is constantly experimenting with Zapier, Make, and n8n to craft lightweight internal automations.
Whether it’s QA checks, sentiment analysis, or custom copilots, the ability to stitch together tools means teams don’t have to rely on other companies to ship features that are hyper-specific to their workflow.
Pavel recommended starting small. Pick a single pain point (like analytics or QA) and automate with low-code tools before scaling up.
Support tickets as product feedback loops
Support is a goldmine of customer feedback, but most orgs only analyze a fraction of their tickets. Pavel sees AI as a way to unlock the full potential of support tickets.
By running tickets through LLMs, his team can generate trend reports, voice-of-customer insights, and targeted product feedback. These insights help the product team prioritize, and give support a louder voice in the product development loop.
Even a simple pipeline that summarizes themes from 1,000 tickets can give PMs more insight than quarterly customer interviews.
How support jobs are evolving with AI
There’s a lot of noise online about AI completely replacing support roles. Pavel disagrees. Instead, he sees two paths emerging:
- Human support becomes white-glove: support roles evolve into CSM-like roles, delivering white-glove experiences and uncovering “aha moments” for customers.
- AI orchestration: ops-driven roles where support reps design, tune, and oversee the automations powering support at scale.
Either way, the baseline skill set is changing. Critical thinking, analytics, and the ability to “speak AI” will soon matter more than fast typing or handling simple tickets.
The Bigger Picture
What Pavel sees at Productboard is a glimpse of where customer support is headed:
- Resolution quality > deflection rates
- Increased collaboration between support and engineering
- DIY AI ops as a competitive advantage
- Feedback loops powered by LLMs
- New roles focused on orchestration and strategy
The core truth remains: customers don’t care how many tickets you deflected. They care how quickly and accurately their problem gets solved.
AI isn’t replacing support, it’s redefining what great support looks like.
We had a great time jamming with Pavel! We’ve been having similar conversations with product and engineering leaders at top engineering orgs (like Vercel, Wix, Intercom, etc) to unpack how they actually build with AI. You can find previous episodes on YouTube.