Product & Business Strategy

Audience: CTOs • Senior Tech Leads • AI/ML Engineers • Product Managers
Purpose: turn “LLM demos” into reliable, adoptable, defensible, profitable agentic products.

The thesis (what wins in Agentic AI)

1) Build workflow wedges, not generic chatbots

A strong wedge is a narrow, high-frequency workflow step where you can deliver “wow” in < 30 seconds, cheaply, and defensibly.

2) Design distribution as a 3-layer system

• Layer 1 – GTM Wedge: how you enter a workflow
• Layer 2 – PLG Loop: how usage recruits the next user
• Layer 3 – Moat Flywheel: how usage compounds defensibility (data / workflow / trust)

3) Treat trust + governance as a growth engine

For agents, reliability, auditability, and oversight are not “enterprise add‑ons” — they unlock scale.

Playbook map

1) Direction: choosing the right wedge (7-step “AI Strategic Lens”)

1. Pick the ICP + job-to-be-done (one role, one workflow)
2. Map the workflow (before/after, bottlenecks)
3. Choose autonomy (assist → approve → bounded autonomy)
4. Decide the moat to build first (data, distribution, or trust)
5. Design the feedback loop (each use improves quality/cost)
6. Model unit economics early (cost per successful outcome)
7. Define the eval gate (what “good” means + regression tests)

Heuristic: start at the lowest autonomy that still produces a measurable delta.

2) A 5-phase roadmap to ship & scale (use this to run programs)

3) Differentiation vs Moat (how you survive commoditized models)

Differentiation (day‑1): why users choose you now

• Workflow-native UX (inside tools they already use)
• Domain constraints (policies, templates, terminology)
• Output artifacts in real formats (PRDs, tickets, decks, filings)
• Opinionated defaults (best practices baked into flows)

Moat (month‑6+): why users can’t switch later

• Workflow moat: you become the OS of the workflow
• Data moat: unique structured feedback + labeled traces
• Trust moat: governance + reliability others can’t match

4) AI UX paradigms (choose deliberately)

5) Unit economics: make growth a feature (not a liability)

The two curves you must model

1. Value curve (user): time saved, quality improved, risk reduced
2. Cost curve (you): inference + tool calls + human review + support

North-star metric: cost per successful outcome (not cost per token).

Pricing patterns that work well for agents

• Outcome-based: $ per resolved ticket / approved report / shipped PRD
• Seat + usage: base + metered heavy actions
• Tiered autonomy: assist → automate → operate

6) Distribution: wedge → loop → moat (with checklists)

Layer 1 — GTM Wedge checklist

• Solves a step users do daily
• Value in < 30 seconds
• Ships inside an existing surface (IDE, email, CRM, docs)
• Passes the “platform test” (you still win if a model vendor clones a feature)

Layer 2 — PLG Loop checklist

• Outputs share naturally (viral artifacts)
• Collaboration pulls in teammates (team loops)
• Templates and reports advertise the product

Layer 3 — Moat Flywheel checklist

• Each interaction creates moat assets
• Assets lower cost / increase quality
• Switching costs become team-wide (formats, policies, integrations)

7) Failure modes & mitigations (what breaks + how you prevent regressions)

8) Governance posture (permissions, approvals, audit trails, rollout)

Governance levels (pick one per workflow)

1. Read-only copilot
2. Action with approval (draft → human approve → execute)
3. Bounded autonomy (policy-limited, auto-approve low risk)
4. Full autonomy (rare)

Minimum governance controls for agentic systems

• Identity & permissions: least privilege; scoped tokens; RBAC for tools
• Approvals: step-up auth for high-impact actions
• Audit trails: prompt + tool calls + outputs + human decisions
• Rollout strategy: flags, canary cohorts, kill switch
• Incident playbook: escalation, rollback, postmortem + eval updates

9) Operating cadence (how teams ship agent products)

The “2-week agent loop”

• Week 1: ship 1 workflow slice + instrumentation
• Week 2: fix top failure mode + expand scope slightly

What to measure (minimum)

• Activation (time-to-first-wow)
• Outcome success rate (acceptance)
• Human effort saved (minutes per task)
• Cost per outcome ($)
• Trust (override/hand-back rate)
• Safety (policy violations)

10) The AI Product Leader “Meta-Framework” (7 layers)

A compact checklist for senior leaders shipping agentic products end-to-end.

1. Context depth — what the system must know (and what it must ignore)
2. Intelligent interface sense — UX for uncertainty, transparency, hand-offs
3. Agentic workflow thinking — task decomposition → tools → autonomy
4. Reliability engineering — evals, regression gates, observability
5. Economics & pricing — cost per outcome, margin-aware growth
6. Governance & safety — permissions, approvals, audits, incident response
7. Distribution & moat — wedge → loop → flywheel

11) Templates

A) Agent PRD (one page)

• Problem + ICP
• Workflow map (before/after)
• Wedge statement (3–5 words)
• Autonomy level + approvals
• Tools/integrations required
• Success metrics (outcome + cost + trust)
• Top 5 risks + mitigations
• Evals plan + regression gates
• Rollout plan (flags, canary, kill switch)

B) ROI worksheet (quick)

• Baseline time/task × hourly cost × volume
• Quality delta (rework reduction %)
• Risk delta (incident reduction %)
• Agent cost per task (model + tools + review)
• Net value = (time + quality + risk) − agent cost