Enterprise AI Strategy: Complete Framework for Series B+ Companies

Most enterprise AI strategies fail because they start with technology. We start with leverage points.
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Here's the framework we use across our portfolio-and you can steal it.

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Week 1: Assessment

Before writing a single line of code, map your leverage points.

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Step 1: Process mapping

Document your top 10 workflows:

• What happens?
• How long does it take?
• Who's involved?
• Where do bottlenecks occur?

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Step 2: Cost analysis

For each workflow, calculate:

• Direct costs (salaries, tools)
• Indirect costs (time, opportunity cost)
• Error costs (bugs, compliance, support)

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Step 3: Automation potential

Rate each workflow 1-10 on:

• Repetitiveness (how similar is each instance?)
• Volume (how often does it run?)
• Structured-ness (are rules clear?)
• Measurability (can you verify success?)
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The formula: Automation score = (Repetitiveness + Volume + Structured + Measurable) / 4 Workflows scoring 7+ are prime candidates.

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Week 2: Prioritization matrix

You can't transform everything at once. Here's how we prioritize:

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Quadrant 1: Quick wins (START HERE)

• High automation score
• Low technical complexity
• High business impact
• Examples: QA testing, data validation
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Quadrant 2: Strategic bets

• High automation score
• High technical complexity
• High business impact
• Examples: Customer onboarding, compliance
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Quadrant 3: Nice-to-haves

• Low automation score
• Low technical complexity
• Medium business impact
• Examples: Content generation, email drafting
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Quadrant 4: Avoid (for now)

• Any combination that doesn't fit above
• Examples: Strategic planning, crisis management

Week 3: Build vs Buy decision

For each prioritized workflow, evaluate:
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Build custom when:

• Workflow is core to your product
• Volume is very high (>10K/month)
• You need deep customization
• You have AI engineering resources
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Buy platform when:

• Workflow is common (sales, support)
• Volume is moderate (<5K/month)
• Speed to market matters most
• You lack AI engineering talent
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Hybrid approach:

• Start with platform for learning
• Build custom for scale
• Most successful companies do both
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Months 1-6: Implementation roadmap

Month 1: Foundation

• Pick first workflow (from Quadrant 1)
• Build minimal viable agent
• Deploy to 10% of traffic
• Measure religiously

Month 2: Optimization

• Reduce costs (prompt engineering, caching)
• Improve accuracy (better prompts, fine-tuning)
• Increase coverage (20% → 50% of traffic)
• Track ROI weekly

Month 3: Scale

• Deploy to 100% of traffic
• Build monitoring dashboards
• Document learnings
• Start second workflow

Month 4: Expansion

• Second workflow from Quadrant 1
• Apply learnings from first workflow
• Should take half the time
• Begin planning Quadrant 2 initiatives

Month 5: Sophistication

• Start first Quadrant 2 workflow
• Begin multi-agent orchestration
• Invest in shared infrastructure
• Hire/train specialized AI team

Month 6: Consolidation

• Measure cumulative ROI
• Identify patterns and reusable components
• Plan next 6-month roadmap
• Scale what works, cut what doesn't

The team structure

Phase 1 (Months 1-3):

• 1-2 full-stack engineers (building)
• 1 product manager (prioritizing)
• Subject matter experts (domain knowledge)

Phase 2 (Months 4-6):

• Add: 1 ML engineer (optimization)
• Add: 1 data engineer (infrastructure)
• Keep: PM and SMEs

Phase 3 (Months 7-12):

• Dedicated AI engineering team (3-5 people)
• Shared services for all AI initiatives
• Center of excellence model

The technology stack

Must-haves:

• LLM provider (OpenAI, Anthropic, or both)
• Orchestration layer (Temporal, Inngest)
• Vector database (Pinecone, Weaviate)
• Monitoring (Datadog, custom dashboards)

Nice-to-haves:

• Agent framework (LangChain, CrewAI)
• Fine-tuning infrastructure
• Prompt management system
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Avoid:

• Over-engineering infrastructure
• Building what you can buy
• Lock-in to single vendor

The measurement framework

Track these metrics from day one:

Business Metrics:

• Cost savings ($ per month)
• Time savings (hours per week)
• Quality improvements (error reduction %)
• Speed improvements (cycle time reduction)

Technical metrics:

• Task success rate (target: >90%)
• Average latency (target: <5 seconds)
• Cost per task (optimize monthly)
• Confidence scores (track distribution)

Strategic metrics:

• Team velocity (features shipped)
• Customer satisfaction (NPS impact)
• Competitive advantage (time-to-market)

The governance model

AI transformation needs guardrails:

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Decision rights:

• Product team: What workflows to automate
• Engineering team: How to build it
• Leadership team: Budget allocation and priorities
• Compliance/Legal: Approval for sensitive workflows
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Review cadence:

• Weekly: Tactical progress and blockers
• Monthly: Strategic direction and ROI
• Quarterly: Portfolio review and reallocation

Risk management:

• High-stakes decisions require human approval
• Gradual rollout (10% → 50% → 100%)
• Circuit breakers for runaway agents
• Regular audits of agent decisions

Common Pitfalls

1. Analysis paralysis: Don't spend 6 months planning. Build something in month one.
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2. Feature factory: Don't build 10 mediocre agents. Build 2-3 excellent ones.
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3. Ignoring pperations: Agents need monitoring, maintenance, and optimization.
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4. Underestimating Change Management: Your team needs to learn new workflows. Budget time for this.

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The success pattern

Across 40+ transformations, the winners:

1. Started with one high-impact workflow
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2. Measured religiously from day one
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3. Optimized costs in month two
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4. Scaled proven patterns to new workflows
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5. Built shared infrastructure by month six

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They didn't try to transform overnight. They compounded small wins.

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Your next step

If you're leading AI transformation at a Series B+ company:
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‍This week: Map your top 10 workflows and score them

Next week: Pick one Quadrant 1 workflow to start

Month one: Build and deploy minimal viable agent

Month three: Measure ROI and decide: scale or pivot

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The companies moving fastest aren't waiting for perfect strategy. They're building, measuring, and learning.
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That's the playbook.

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Need help with your AI strategy? Islands offers strategy workshops for Series B+ companies. Visit islandshq.xyz/contact

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