How AI Is Transforming The Digital Product Lifecycle

Authored by Maya Mort, Senior Architect

AI has become a daily companion to many curious minds, transforming both personal and professional lives and getting better every day. Its influence on digital product development is especially profound - it’s transforming how we imagine, design, build and evolve digital products. In fact, 88% of organisations report using AI in at least one business function [1], highlighting how widespread its impact has become across industries.

For many product teams, AI has become a quiet partner, one that helps them think faster, work smarter, and deliver to expectations. Across every stage of the product lifecycle, AI is starting to do something remarkable: it’s making teams more productive and effective by giving back time to do the work that really matters. 

Let’s explore how it reshapes each stage of the product lifecycle, starting with strategy.

Smarter Strategy

Every great product begins with the problem we are solving and for whom? AI is helping teams answer that question with incredible speed. Tools like ChatGPT and Perplexity can summarise research, analyse trends, or generate early personas that capture customer needs. A platform like Brandwatch can sift through vast amounts of online conversations to surface emerging patterns. With the help of AI, insights that once took weeks of analysis now appear in hours. The result is a more informed starting point, where strategy decisions are grounded in evidence rather than guesswork.

It’s no surprise that 49% of technology leaders say AI is now fully integrated into their core business strategies [2], illustrating how essential data-driven insights have become in guiding product direction. And little wonder that 45% of market researchers use generative AI to analyse customer data and speed up the research phase dramatically [3].

Accelerated Design

Armed with sharper insights, teams move into the design phase, where AI continues to accelerate creativity and experimentation. Designers using Figma AI, Uizard, or Canva Magic Design can turn multiple simple prompts into layouts or prototypes within minutes. Adobe Firefly and Runway ML generate imagery, icons, and even video concepts that help teams experiment freely without the heavy lifting. It’s worth noting that AI doesn’t replace human creativity; rather, it helps imagination flow faster. Indeed, 59% of designers and developers are already using AI for brainstorming, generating images and text, and rapid prototyping to iterate quickly on ideas [4].

Development Reinvented

Once ideas are visualised, development teams take over. Here, AI becomes a powerful partner, helping engineers build smarter and faster. Tools like Claude Code, GitHub Copilot and Cursor suggest smarter code and prevent common errors. Security scanners such as Snyk and DeepCode AI detect vulnerabilities early, while Jira AI Assist helps write clearer user stories and acceptance criteria. Testing is evolving, too. Testim, Functionize, and Mabl use AI to generate and maintainautomated test cases that keep up as the code changes. In fact, a study of over 4,000 developers found those using AI coding assistants like GitHub Copilot completed tasks 26% faster on average [5]. The result is faster releases, cleaner builds and fewer incidents. 

Predictive Maintenance

With the product now live, the focus shifts to performance and reliability. AI steps in once again, this time as a vigilant guardian. Forrester reported in 2025 that organisations implementing AI-driven IT operations (AI Ops) achieved a 60% reduction in mean time to repair (MTTR) while also identifying the root cause of incidents up to 10x faster [6]. Tools like Datadog, Dynatrace, and New Relic monitor performance and flag unusual behaviour before it turns into downtime. ServiceNow Predictive Intelligence helps identify recurring issues, and chatbots such as Zendesk AI handle everyday support questions instantly. Meanwhile, analytics platformslike Amplitude and Mixpanel turn usage data into stories, showing what features people love, what they ignore, and where they struggle. That feedback loop makes every release smarter than the last.

Conclusion

AI is no longer just a shiny new tool, it’s the quiet force powering modern product management. From strategy to support, it helps teams move faster, think deeper, and deliver better. But its true power lies in what it gives back: time. Time to imagine bold ideas, to listen to users, to refine what matters most. To truly unlock AI’s potential, teams must be intentional - cultivating the right culture, setting clear boundaries, and embracing experimentation. The future of product development isn’t just faster, it’s smarter, more human, and more creative than ever.

References

[1] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) 

[2] https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html (https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html) 

[3] https://business.columbia.edu/insights/digital-future/ai/generative-ai-market-research (https://business.columbia.edu/insights/digital-future/ai/generative-ai-market-research) 

[4] https://www.figma.com/reports/ai-design-trends-2024/ (https://www.figma.com/reports/ai-design-trends-2024/) 

[5] https://www.infoq.com/news/2024/09/copilot-developer-productivity/ (https://www.infoq.com/news/2024/09/copilot-developer-productivity/) 

[6] https://sciencelogic.com/product/resources/tei-report (https://sciencelogic.com/product/resources/tei-report)