With 250,000 customers and $1.2B in revenue, Monday.com's CPTO explains why they threw out their roadmap and rebuilt everything around agentic AI.

Topics Include:

• Daniel Lereya joined Monday.com when it had just 30 people and five engineers.
• He grew the R&D org from five engineers to roughly 900 over a decade.
• Three years ago Daniel became Monday.com's first ever CPTO.
• Monday.com initially approached AI by adding small features across the product.
• They called this early phase "sprinkling AI dust" — helpful but not transformative.
• A pivotal board meeting made Daniel realise AI hadn't changed Monday's core value.
• Monday.com decided to rethink its mission from first principles around AI.
• The new mission: AI agents that actually execute work, not just manage it.
• AI gives businesses an "infinite workforce" regardless of company size.
• Agents can now do hyper-personalised work at a scale humans simply cannot.
• Monday's platform puts agents at the centre, replacing boards and dashboards.
• Shared context and human-in-the-loop handoffs make their agents uniquely powerful.
• Monday ran an "AI month" — pausing the entire 900-person builder org to transform.
• The month rebuilt team mindset and energy, reminding staff of early startup days.
• Monday also ran an "agentic week" where every department built their own agents.
• Finance built agents to automatically match incoming payments to customer accounts.
• Scaling AI adoption internally remains the biggest challenge across businesses today.
• Monday introduced "effective AI" — balancing capability with cost efficiency.
• They acquired voice AI startup One AI to add specialised model capabilities.
• On pricing, Monday shifted to a hybrid seats-plus-AI-credits consumption model.

Participants:

• Daniel Lereya – Chief Product and Technology Officer, Monday.com
• Kamil Davidov – Sales Leader Israel ISV-BizApps, Amazon Web Services
• Johan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web Services

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

PagerDuty SVP Rukmini Reddy explains why AI is making software operations exponentially more complex — and why the companies that learn and recover fastest will be the ones that win.

Topics Include:

• PagerDuty powers critical digital operations for enterprises and AI-native companies.
• Founded by early AWS employees who experienced always-on system failures firsthand.
• The platform evolved from simple alerting into a full operational intelligence platform.
• Complexity exploded with microservices, cloud-native infrastructure, and multi-cloud environments.
• Reliability must be a core value — not an operational afterthought.
• PagerDuty's culture champions the customer above everything else.
• Employee recognition extends beyond sales to celebrate the whole business.
• AI is accelerating software creation but making operations far more complex.
• AI fails differently — silently, unpredictably, with a much larger blast radius.
• Enterprises should leverage their operational history as a competitive AI asset.
• AI-native companies must build operational resilience early, not bolt it on later.
• The winners won't build fastest — they'll learn and recover fastest.

Participants:

• Rukmini Reddy – Senior Vice President of Engineering, PagerDuty

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

From cracked data foundations to multi-agent AI, Starburst Data's co-founder shares hard-won lessons on getting the right data, not just more of it.

Topics Include:

• Matthew Fuller, co-founder and VP of Product at Starburst Data, joins the show.
• Starburst is built on Trino, a fast SQL engine for federated data queries.
• Their platform lets users query data across lakes, stores, and databases seamlessly.
• Governed "data products" give organizations access to their full data estate in context.
• A strong data foundation is essential before any AI use case can succeed.
• AI doesn't create data problems — it exposes the cracks already there.
• Common mistake: assuming everyone in an org defines "customer" or "revenue" the same way.
• More data isn't always better — getting the right data is what matters.
• Customers include HSBC, Comcast, Zalando, ZoomInfo, and DBS, many running on AWS.
• AWS partnership spans technical support, SLA reliability, and proactive product briefings.
• Advice for product leaders: always anchor new technology back to the customer problem.
• 2026 will be defined by specialized multi-agents working together autonomously.

Participants:

• Matt Fuller – Co-Founder, Vice President of Product, Starburst Data

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/