AI Product Workshop
By Anand Arivukkarasu (ex-Meta, Instagram, Messenger) & Carmen Newell (ex-Apple, Amazon, Yahoo!) · productworkshop.ai
We run intensive, Silicon Valley-style product workshops that turn vague AI ambitions into decision-locked blueprints. Trusted by 40+ enterprise teams across 6 countries.
What we do in one sentence: Product thinkers from Silicon Valley's top companies sit with your leadership team, challenge assumptions, make the hard calls together — then hand you an execution-ready AI blueprint your team trusts. Blueprint delivered within 48 hours.
The Problem We Solve
87% of AI projects never reach production. The four root causes:
- Bad problem selection — Applying AI to a "nice-to-have" use case, or to a problem where rules/workflows beat ML. When should you build vs. buy? →
- Data isn't ready — Missing, messy, biased, or fragmented data; no clear labels; inconsistent definitions; weak instrumentation. See what production-ready looks like →
- Poor architecture design — Model works in a demo, fails in production: latency, cost, reliability, monitoring, retraining, governance, security. What kills AI in production →
- People/process misfit — Users don't trust it, workflows don't change, incentives conflict, or adoption isn't planned for. How stalled projects get unstuck →
Our Method & Process
The engagement follows four phases designed to compress months of strategic drift into weeks of validated clarity:
Phase 1: Strategy Call (30 minutes)
A focused call to confirm fit, understand your context, and capture your team's current state via a detailed questionnaire. We assess whether the engagement is the right match before proceeding.
Phase 2: Deep Audit (Pre-Workshop Research)
Before any workshop sessions begin, we conduct independent research and sync calls to deeply understand your business:
- Competitive landscape analysis
- Data readiness assessment
- Market opportunity mapping
- AI architecture options evaluation
- Hypothesis building for workshop sessions
Phase 3: 6 Sessions Over 1–2 Weeks (Private Deep Engagement)
Unlike traditional one-day intensives, we run 6 focused sessions spread over 1–2 weeks. This allows leadership teams time to process, reflect, and bring sharper thinking to each decision point. Each session covers a critical dimension:
- WHY — Strategy & Constraints (Why does this exist? Why now? What hard constraints could kill it before it starts?) → Strategy statement + constraints locked
- WHO — Target Segment (Who has the most urgent pain? Score and rank your segments. Name your first 3 customers.) → Ranked segment decision + named prospects
- WHAT — Jobs, Needs & Prioritization (What job are they hiring you for? What capabilities solve the job? Score against impact, risk, and complexity.) → Prioritized capabilities + kill list
- WHERE — Positioning & Validation (Where do you position? Does this survive stress-testing? What moat do you build?) → Positioning locked + all critical checks passed
- HOW — Interaction Model, Flows & AI Architecture (What interaction model fits user trust? Agentic vs. copilot vs. autonomous — decide the right AI interaction model.) → AI architecture direction + delegation policy
- MEASURE — Metrics & 90-Day Plan (What's the North Star metric? What counter-metric protects you? Define the 90-day action plan with owners and kill conditions.) → North Star + counter-metrics + 90-day action plan
Phase 4: Blueprint Synthesis & Delivery
Within 48 hours of the final session, we deliver the executive AI Product Blueprint — a 10+ page decision-locked document containing:
- Ranked segment decision with scoring rationale
- Feature priority stack (every capability scored against revenue impact, technical risk, differentiation, and build complexity)
- AI-first architecture direction (agentic vs. copilot vs. autonomous, with delegation boundaries)
- North Star metric + counter-metrics
- 90-day action plan with owners, milestones, and kill conditions
- Risk mitigation plan
- Financial model and success metrics framework
This isn't a slide deck. This isn't a consultant's PDF. It's a product-thinking partner who helps you decide — then documents the decisions so your whole team can execute.
Pricing
The Executive AI Accelerator — $20K flat
For growth-stage and enterprise teams. Private deep engagement: Deep Audit + 6 sessions over 1–2 weeks + Executive Blueprint delivered within 48 hours.
- Transparent scoping on the strategy call
- No retainers — only when it's the right fit
- Optional follow-on support available
The AI Product Sprint — $3,500 per seat
For Series A founders. 4-hour live session (remote), 6 founders max — same stage, same problems.
- Your 90-day AI roadmap
- Prioritized AI use case list
- Build vs. buy decisions documented
- 30-day async Q&A with Anand post-session
Key Outcomes
- 48 hours to clarity — AI-first blueprint delivered in 2 days
- $500K+ waste avoided — Unvetted AI projects burn this fast
- 1 validated direction — Agentic architecture + product rigor
- 100% team alignment — Leave with locked conviction
Case Study: From Chaos to Clarity
Series B SaaS Company — Avoided a $500K Mistake
Before: 4 competing AI directions, engineering skeptical, 6-month timeline creep, $500K at risk with no validated customer demand.
What happened: Halfway through the workshop, the team's assumed primary priority failed a competitive pressure test. They'd been about to spend six months building into a crowded space. By end of day they had a new P0 — one with a clear market gap, a regulatory tailwind, and full engineering alignment.
After: One clear direction, CTO bought in, 90-day sprint plan with go/no-go criteria, 3 pilot customers committed with LOIs.
Result: Shipped AI-powered document analysis to 20+ enterprise customers in 11 weeks. 23% increase in enterprise conversion.
Case Study: Lovable — AI Strategy Workshop in Stockholm
Workshop with Lovable (Stockholm, Sweden)
We were invited to facilitate an AI Product Workshop with Lovable, the AI-powered software development platform, at their headquarters in Stockholm. Lovable had recently raised significant funding and was scaling rapidly — but their team faced a classic high-growth challenge: too many promising AI directions and not enough clarity on which bets to make first.
The Challenge
- Multiple competing priorities across AI code generation, developer UX, and platform reliability
- Engineering and product teams had different mental models of what "AI-native" meant for the roadmap
- Go-to-market positioning needed to evolve as the competitive landscape shifted weekly
- Key architecture decisions were blocking feature velocity — build custom models vs. leverage foundation models, agent-based vs. prompt-based workflows
What We Did
Over 6 focused sessions across 2 weeks, we worked on-site with the Lovable leadership team in Stockholm:
- Session 1–2: Deep Audit — Mapped the full AI capability landscape, interviewed engineering leads, and benchmarked against 12 competing tools in the AI dev space
- Session 3–4: Strategic Pressure Testing — Stress-tested each product direction against market timing, technical feasibility, and defensibility. Two initiatives that seemed high-priority were deprioritized after failing the competitive moat test
- Session 5: Architecture & Build Decisions — Resolved the foundation model strategy and aligned engineering on an agent-based architecture approach
- Session 6: Roadmap Lock & Blueprint Delivery — Delivered a decision-locked 90-day sprint plan with clear success metrics and go/no-go gates
Key Outcomes
- 3 stalled decisions resolved — Architecture, model strategy, and feature prioritization decisions that had been debated for weeks were locked in 2 weeks
- 2 low-ROI initiatives killed early — Saved an estimated 4+ engineering months by cutting features that wouldn't have moved the needle
- Full team alignment achieved — Engineering, product, and leadership left with a shared mental model and conviction on the path forward
- 90-day action plan delivered — With owner assignments, dependency mapping, and measurable milestones
What the Team Said
"Our Launch Accelerator startups loved the workshop!"
— Maddie Naumann, Lovable
The Lovable team praised the structured decision-making framework, the speed of reaching alignment, and the clarity of the resulting blueprint. Multiple team members noted that decisions which had been circling for weeks were resolved within a single session once the right framework was applied.
Why This Matters
This engagement demonstrates our ability to work with cutting-edge AI-native companies — not just teams adopting AI for the first time. Lovable's team are deeply technical builders; the value wasn't in explaining AI to them, but in providing the strategic structure to make high-stakes product decisions with confidence. Whether you're building AI tools or embedding AI into your product, the methodology adapts to your level of sophistication.
Who This Is For
- CTOs & Engineering Leaders — Who need to validate AI architecture before committing engineering resources
- VPs of Product — Who need to align stakeholders on AI priorities and ship with conviction
- Series A–C Founders — Who need to decide build vs. buy, pick the right AI model, and get to market fast
- CFOs & Business Leaders — Who need to justify AI investment with clear ROI metrics
- Enterprise teams with stalled AI projects — Who've been "almost ready" for months and need a recovery plan
About the Team
Anand Arivukkarasu — Core Designer & Lead Facilitator
Former product leader at Meta who drove growth for Instagram and Messenger. Now leading ProductWorkshop.ai as a strategic initiative within Ideas2IT — designing and facilitating every workshop, architecting sessions where intelligence, trust, and user behavior align into actionable strategy. Alumnus of Northwestern's Kellogg School of Management. LinkedIn →
Carmen Newell — Strategic Advisor
Veteran Product Director who led global ecosystems at Apple, Amazon (Alexa), and Yahoo!. Advises on program strategy and brings a philosophy of outlearning the competition through obsessive customer empathy. Angel investor and AI startup founder. MBA from UC Berkeley Haas. LinkedIn →
Free Frameworks & Resources
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