In today's rapidly evolving software development environment, AI has moved beyond coding assistants to full agentic workflows—where AI handles multi-step development tasks autonomously, from writing code to running tests to debugging failures. While studies report productivity gains ranging from 21% faster task completion in controlled enterprise settings (Google internal study, 2024) to a 26% increase in completed tasks across multi-company research (IT Revolution, 2024), product managers and designers must continue adapting their approaches to capitalize on this acceleration while ensuring quality and strategic alignment.
The New Development Reality
The evolution from AI coding assistants to agentic development has compressed traditional development timelines even further than we anticipated. Tasks that once took days now complete in hours—sometimes minutes. At The Gnar, we've standardized on tools like Cursor with Claude powering our agentic workflows, and GitHub Copilot handling code reviews.
But this acceleration raises critical questions that haven't changed: Are we building the right things? Are we designing experiences that truly solve customer problems? And perhaps most importantly, are our product management and design practices keeping pace with development that now moves at machine speed?
The key insight we've gained: these tools amplify expertise. Research confirms that junior developers see the largest productivity gains from AI tools, while senior developers see more modest improvements of 7-16% (IT Revolution, 2024). Junior developers using agentic tools don't suddenly perform like seniors—but senior developers become force multipliers.
The Productivity Paradox
Before we celebrate velocity gains, a sobering reality check: a rigorous July 2025 study from METR found that experienced open-source developers were actually 19% slower when using AI tools like Cursor Pro with Claude—despite believing they were 20% faster (METR, 2025; arXiv:2507.09089). This perception-reality gap underscores why senior engineering judgment remains essential in evaluating AI-assisted output.
The lesson isn't that AI tools don't help—it's that productivity gains depend heavily on context, codebase familiarity, and how teams integrate these tools into their workflows. Product managers need to understand this nuance rather than assuming blanket productivity multipliers.
Why Product Management and Design Matter More Than Ever
While development velocity has increased dramatically, the fundamental need to build the right product hasn't changed. In fact, the stakes are higher:
- Increased Cost of Wrong Direction: When development moves faster, taking the wrong product direction becomes more costly. Teams can build the wrong features with impressive efficiency—and they will, unless product management provides precise guardrails.
- User Experience Quality Gaps: AI-assisted development excels at implementing specified functionality but may not inherently understand the nuanced user experience considerations that make products successful. Research from USENIX Security 2025 found that AI coding models hallucinate package recommendations 19.6% of the time on average (UTSA study, arXiv:2406.10279), while separate analysis indicates 48% of AI-generated code contains security vulnerabilities (Second Talent, 2025). This makes experienced review more critical, not less.
- Strategic Alignment Challenges: Accelerated development cycles can easily outpace strategic planning, creating disconnects between business objectives and what gets built. When your team can ship a feature in days instead of weeks, the quarterly planning cycle becomes a bottleneck—not the engineering capacity.
Product management and design serve as the essential guardrails ensuring that increased development speed translates to business value rather than merely more code.
What Must Change in Product Management
In this new landscape product managers need to evolve their approaches:
Prompt Engineering Literacy: This is the new core competency. Product managers need the ability to articulate requirements in ways that AI systems can execute without ambiguity. Vague user stories that human developers would clarify through conversation? Agentic systems interpret them literally—and build exactly the wrong thing, faster. Write acceptance criteria so precise that an AI system, which cannot read your mind, will build what you actually need.
Tighter Feedback Loops: With development moving faster, product managers must establish more frequent checkpoints to validate direction. The traditional sprint review may not be sufficient when significant functionality ships in days. Consider daily async reviews of AI-generated work and continuous prototype access for stakeholders.
Outcome-Focused Requirements: As AI handles more implementation details, product managers should focus more on desired outcomes and acceptance criteria rather than prescriptive solutions. Define what success looks like, not how to build it. Traditional PRDs often included implementation guidance to help developers understand intent—with agentic development, that guidance becomes constraint.
Market Intelligence Over Development Coordination: With AI accelerating development execution, product managers should redirect their energy toward deeper market analysis and competitive intelligence rather than sprint-level coordination. When engineering velocity is no longer the bottleneck, market insight becomes the competitive advantage.
Design's Critical Evolution
Designers face a similar evolution in their roles—but now from two directions:
Systems Over Screens: As development velocity increases, designing comprehensive design systems becomes more important than individual screens. Component libraries with clear specifications allow agentic systems to implement consistent UI at scale. A well-documented button component gets implemented correctly a thousand times; a one-off screen design gets interpreted differently each time.
Collaborative Prototyping: Designers need to work alongside developers and AI tools in more fluid prototyping processes, focusing on rapid iteration rather than pixel-perfect handoffs. The traditional handoff—designer completes mockups, throws them over the wall, developer implements weeks later—is too slow for agentic workflows.
User Experience Governance: With AI implementation moving quickly, designers must establish stronger quality controls to ensure design consistency across rapidly developed features. This means automated design linting, component usage audits, and systematic review processes. Google's DORA 2024 research found that while AI use improves documentation speed, it causes a 7.2% drop in delivery stability (Second Talent citing DORA, 2024)—a warning sign for teams prioritizing speed over sustainable quality.
AI as Design Collaborator: Generative UI tools and AI-powered prototyping are emerging rapidly. The designer's role is shifting from "create the interface" to "curate and refine AI-generated options while ensuring brand consistency and accessibility compliance." Designers who thrive will treat AI as a collaborator that handles exploration while they focus on judgment.
The Path Forward for Product Teams
For SaaS executives looking to maximize the benefit of AI-accelerated development, several strategic priorities emerge:
Invest in Discovery: With implementation becoming more efficient, the relative value of upfront discovery work increases. Understanding customer needs thoroughly before accelerated implementation begins is crucial. The ROI of discovery has never been higher.
Build Cross-Functional Fluency: Product managers and designers need prompt engineering literacy—the ability to communicate with AI systems, understand their capabilities and limitations, and write specifications with the precision these tools require.
Audit Your Architecture: Before investing heavily in agentic development, honestly assess your technical debt. Agentic development amplifies whatever foundation you're building on. Clean, modular codebases with strong test coverage? AI becomes a force multiplier. Brittle monoliths with tribal knowledge? AI generates more technical debt, faster.
Measure Value, Not Velocity: The ultimate measure of success isn't how much gets built, but the value delivered to customers and the business. Traditional metrics like story points and lines of code become meaningless when AI generates thousands of lines in minutes. The JetBrains 2025 survey found that 66% of developers don't believe current metrics reflect their true contributions (JetBrains, 2025). Instead, track time-to-validated-feature, rework rates on AI-generated code, and test coverage trajectory. Focus on business outcomes—churn reduction, shorter onboarding cycles, revenue impact—rather than output.
The transformation brought by agentic development represents an enormous opportunity, but only for organizations that evolve their product management and design practices to match this new reality. By strengthening these disciplines alongside AI-powered development, SaaS companies can convert pure coding efficiency into meaningful user growth and customer value.
