The 2026 AI Product Lifecycle Playbook for High-Velocity Teams

Are you trying to run an effective product lifecycle while AI turns every idea into a prototype overnight and every stakeholder into a “we should build this” request?

Join Dragonboat CEO Becky Flint as she unpacks what it takes to successfully manage the full Product Development Lifecycle (PDLC) in the fast-paced, AI-driven landscape of 2026. From determining the right product investments to navigating the complexity of AI prototypes and delivering measurable impact, Becky will share strategic insights and actionable practices to help product leaders harness AI acceleration without being overwhelmed by it.

You’ll learn how to:
– Make clear investment calls when AI expands the opportunity set and the noise
– Bring discipline to AI prototypes so they drive effective decisions, not endless cycles
– Keep teams empowered and aligned from strategy to roadmap to delivery
– Move fast while staying in control of scope, priorities, and outcomes

If AI is speeding everything up except clarity, do not miss this session. You’ll leave with a practical playbook to innovate at speed without losing control or your sanity.

Join us for new conversations with leading product executives every week. Roll through the highlights of this week’s event below, then head on over to our Events page to see which product leaders will be joining us next week.

Show Notes:

1. AI has enabled rapid prototyping, reducing build time from weeks to hours.
2. Upfront costs for creating prototypes are significantly lower with AI.
3. Cross-functional collaboration is essential for leveraging AI effectively in product development.
4. AI allows PMs to prototype with minimal engineering involvement, but cross-team dependencies persist.
5. The real challenge is moving features from prototype to customer hands, not just speed of creation.
6. AI productivity improvements are most evident in coding, design, research, and writing tasks.
7. Despite high AI adoption, aligning prototypes with overall product strategy remains difficult.
8. The cost curve has shifted: Upfront build cost is lower, but maintenance post-launch is higher due to model drift and non-deterministic AI outputs.
9. Dependencies between product teams and organizational bottlenecks can delay getting impactful features to market.
10. AI can cause “strategy drift” if teams lose alignment between big-picture goals and on-the-ground implementation.
11. Orchestrating the product development life cycle (PDLC) is far more complex than simply building features with AI.
12. Shared context—integrating strategy, customer insights, and operational data—is crucial for effective product decision-making.
13. AI tools help with task execution but have limits handling large-scale product orchestration and cross-team alignment.
14. Maintaining and evaluating AI products requires more ongoing resources than traditional software, due to unpredictability of results.
15. The “Dragon Boat” metaphor: Teams must move in sync, sharing a unified context and strategy to achieve velocity and impact.
16. To separate “cool” from “valuable” in AI, always start with desired business and customer outcomes before choosing solutions.
17. Evaluate new AI features for not just initial fit, but also for downstream impact on resources and other product priorities.
18. The best go/no-go signals for investing in an AI prototype include: clear customer need, readiness for additional maintenance, and resource availability.
19. Orchestration tools (like Dragon Boat) are necessary to manage context, data, and human-AI collaboration effectively at scale.
20. The biggest reason AI initiatives fail at scale is lack of holistic, integrated orchestration, not lack of technology.