Problem

Andrew Ng observed that AI makes development faster, but shifts the bottleneck to product management. When engineering can compress sprint-level work into hours and deliver features in days instead of weeks, the question shifts from "how fast can we build?" to "what should we build next?"

Traditional product workflows, like crafting PRDs, generating user stories, creating go-to-market materials, processing research data, drafting release communications, etc. operate at human-like speed while development moves much faster.

This velocity mismatch creates a new form of organizational friction. Product managers spend hours documenting, translating PRDs into various downstream artifacts for different audiences, and manually synthesizing quantitative and qualitative research into actionable insights. Meanwhile, engineering teams equipped with AI-assisted development tools wait for clarity on priorities and specifications. The bottleneck has simply moved upstream from execution to strategy and planning.

Solution

My hypothesis: If development cycles compress through AI assistance, product workflows must accelerate proportionally to maintain organizational speed. I experimented with GenAI across the entire product spectrum to test this premise, transforming how product managers create and leverage assets. I first mapped out the existing cycle from concept to market, then repurposed a key data source to generate assets for each functional node through structured prompts.

PRDs became source material rather than endpoints and helped generate marketing material, training documentation, release notes, FAQ content, and stakeholder communication plans through AI transformation. User stories with comprehensive acceptance criteria that previously required hours now materialized in minutes. High-volume qualitative data, complex, nuanced customer feedback, research transcripts, and feature requests, distilled through AI analysis into actionable feature opportunities with supporting evidence and user context. What's interesting and encouraging is that these activities took hours instead of weeks too.

Impact

Taken with a pinch of salt, the results are promising, and demonstrate that product velocity could match AI-accelerated engineering cycles. Product managers reclaim time previously spent on manual documentation work, reallocating focus toward strategic thinking, stakeholder alignment, user research, and complex prioritization decisions that AI cannot handle independently.

The broader implication validates Ng's observation while providing a path forward: when AI accelerates any part of the product development lifecycle, every adjacent function must compress proportionally or become the new constraint.

Problem

Every quarter at athena, over 200 teams across the organization gather for what's internally known as the BURP — Bottom Up Release Plan. For three days, scrum teams sit in closed rooms anchoring on OKRs, writing EPICs, user stories, and acceptance criteria, then slotting them into 7-8 defined sprints for the upcoming release.

The complexity compounds quickly. Long-term roadmap alignment is imperative. Cross-team dependencies demand closer inspection of timelines and readiness. Story points need to align with developer bandwidth. Functional-to-technical work ratios must be factored in. With all of this happening in real-time across a room full of engineers, product managers, and other stakeholders, it's no surprise that teams have a name for it: Death by Grooming.

Solution

Rather than replacing the ritual entirely, I decided to redesign it by using a tool the team was already familiar with, but in this case, adding an AI layer on top.

I asked the scrum team to join a structured Microsoft Teams call with a defined agenda and clear guardrails to keep the conversation focused. No drift. No tangents. We moved through our planned scope in sequence, and three hours later, we had a complete meeting transcript.

That transcript became the raw input for a carefully structured GenAI prompt. The output: full PRDs with user stories and acceptance criteria, already estimated, with resource allocations mapped against the development team's known bandwidth and functional-technical work split. Not just documentation, an actual release plan, ready to action.

From there, MCP handled the rest. Work was pushed directly to Confluence and all stories were extracted into their respective Jira epics.

Impact

We saved over 18 hours of planning and discussion time. We closed out documentation for 90% of functional items planned — a figure many teams struggle with even after the full three-day event.

But the real shift was less visible. The team wasn't exhausted. They were informed and ready to go. The AI layer together with the MCP layer transformed the transcript into structure and eliminated the documentation work, leaving more time for deliberation and constructive planning.

What had been a gruelling, multi-day commitment became a focused, single-session workflow.

Death by Grooming doesn't have to be the cost of staying organized. The expressway exists — you just have to be willing to take it.

Problem

Traditional feature development followed a somewhat disconnected, sequential pattern where Product, Engineering, and Design operated on separate swimlanes toward the same endpoint. Product managers spent hours crafting PRDs and user stories. Design handed off specifications after requirements were finalized. Engineering translated these artifacts into code, often discovering gaps mid-development that triggered additional coordination cycles. Documentation happened at the end for all parties, including downstream functions.

A feature estimated at 8 story points typically consumed 64 hours of development time. Any break in the chain created friction requiring additional interactions to resolve missing links. After development and unit testing, the branch was merged and passed on to QA for further testing and regression. On successful completion, product teams would schedule downstream activities with cross-functional teams to bring the product to market, a timeline that could span 6 sprints (384 hours). But in the world of AI, do these timelines make sense?

Solution

Through three progressive experiments building real production features, a small team (1PM, 1Dev) evolved from using AI as a coding assistant to creating an integrated workflow spanning Product, Engineering, and Design. Each experiment expanded scope and deepened cross-functional integration.

Experiment 1 established the foundation using Windsurf AI for pattern-based development, delivering the Patient Implant History feature, complete with Redux state management, two-screen UI flow, and 80%+ unit test coverage, in just 16 hours or 2 days versus the original 8-day estimate. The key learning: AI excelled when given concrete patterns from existing codebases, learning team standards and development language.

Experiment 2 expanded upstream into Product workflows. Using LLMs (ChatGPT and Claude), I expedited PRD creation and generated comprehensive user stories with acceptance criteria, tasks that would take 2-4 hours, in under 30 minutes, accelerating documentation workflows by over 75%. With feature APIs in place, Engineering picked up the story and executed in less than 12 hours. Product now moved at a higher velocity, though Design remained in its conventional lane and workflows weren't truly integrated.

Experiment 3 built the expressway. By implementing Model Context Protocol (MCP) to connect AI tools directly to Atlassian, Figma, and Windsurf, the team created a production line tying all three disciplines together. I crafted requirements using LLMs, auto-generated properly structured Jira stories through Atlassian integration, including downstream material required to release the feature. Designs were built to requirements in Figma, and Windsurf pulled complete context through MCPs to understand what to build, why, and how it should function. The feature reached integration-ready status in 4 days instead of the estimated 16 days, with all disciplines contributing to a shared, AI-augmented process.

Impact

The transformation delivered quantifiable efficiency gains: development time reduced saving the team 212 hours, PRD creation accelerated user story generation by 70%, TRR and PRR documentation time was cut significantly by 80%.

Beyond metrics, the work fundamentally changed what's possible with existing teams. Product managers elevated from documenters to orchestrators, focusing on strategic thinking around user problems and clinical workflows. Engineers shifted from writing boilerplate to architecting systems, concentrating on performance optimization and integration challenges. Design moved from middle-step handoffs to embedded collaboration, with specifications becoming living context.

Most significantly, this enables small teams to achieve what previously required larger headcount, speeds up innovation time for novel problems and creative solutions, and accelerates healthcare software development where speed directly translates to patient care impact.

The three experiments proved something more fundamental than faster feature delivery: AI can reimagine how Product, Engineering, and Design collaborate by transforming sequential handoffs into parallel workflows. Once you know the expressway exists, there's no going back.

Problem

The healthcare industry loses over $150 billion annually to appointment no-shows. Protecting even 1% of these lost appointments translates to $1.5 billion in recovered revenue. Current scheduling systems lack predictive intelligence to identify high-risk appointments or surface contextual patient information that could inform better booking decisions, double-booking strategies, or proactive confirmation outreach.

Solution

This solution includes 4 phases of development. One such phase introduces predictive analytics generating probability risk scores for patient no-shows based on historical attendance patterns and a set of contextual variables and weights. This intelligence surfaces during booking, helping practices make informed decisions about double-booking strategies or targeted patient outreach to ensure higher probability of attendance, thus protecting revenue.

Impact

Significantly, the predictive analytics component directly impacts revenue models. By identifying high-risk appointments and surfacing actionable patient insights, practices can implement strategic interventions to reduce no-shows, protecting slot utilization, and maximizing revenue potential for stakeholders.

Problem

Clinical inbox workflows at athenahealth are highly repetitive yet context-dependent. Physicians, nurses, and medical assistants process hundreds of inbox items daily—lab results, patient cases, imaging reports, referrals. Currently, users manually navigate and select appropriate actions for every Inbox item, creating unnecessary interaction friction. On mobile devices especially, these extra taps compound inefficiencies, slowing task completion and reducing overall throughput. While not a blocker, this represents a significant opportunity: even saving one click per action, multiplied across thousands of daily interactions, could meaningfully improve productivity and user satisfaction.

Solution

This feature leverages machine learning to predict the most likely next action when a user opens an inbox item, surfacing that recommendation as a floating action button (FAB) within the detail screen. The prediction model analyzes historical behavioral patterns across multiple contextual inputs. When confidence exceeds 70%, the system displays the recommended action prominently while maintaining an "All Actions" override option, preserving user autonomy. The approach balances intelligent assistance with user control—nudging efficiency without forcing automation.

Impact

Success metrics target measurable workflow improvements: 10% reduction in task completion times, 15% increase in mobile action volume etc.

The broader strategic value lies in establishing a foundation for intelligent workflow assistance across the platform. By demonstrating that ML can meaningfully reduce decision fatigue while respecting clinical judgment, this feature opens pathways for expanded AI integration throughout the athenahealth ecosystem, ultimately enabling clinicians to focus more energy on patient care rather than administrative navigation.