🎯 AI Strategy Framework
- Audit — কোথায় repetitive task?
- Prioritize — high impact + low risk আগে
- Pilot — small POC
- Measure — clear metric
- Scale — successful pilot expand
- Govern — policy, training
💼 Business Function-wise Use Case
Sales
- Lead enrichment automation
- Email personalize at scale
- Call transcript → CRM update
- Proposal generate
- Objection handling script
- Cross-sell recommendation
Marketing
- Content generation (blog, social)
- SEO research + optimization
- Ad copy A/B variants
- Customer segmentation
- Sentiment monitoring
- Campaign performance analysis
Customer Support
- L1 chatbot (deflect 30-50%)
- Ticket triage + routing
- Knowledge base Q&A
- Agent assist (suggest reply)
- Sentiment-based escalation
- Multilingual support
HR
- Resume screening
- Interview question generate
- Onboarding chatbot
- Policy Q&A
- Performance review draft
- Training content personalize
Finance
- Invoice processing
- Expense categorize
- Forecast with reasoning
- Anomaly detection
- Financial report draft
- Compliance check
Operations
- Process documentation
- Vendor evaluation
- Contract analysis
- Inventory optimization
- Supplier communication
- Quality check on photos
Product
- User feedback synthesize
- Feature prioritization aid
- PRD draft
- User research transcribe + analyze
- Competitor monitoring
- A/B test design
Engineering
- Code review assist
- Documentation auto-update
- Bug triage
- Test generation
- Architecture review
- Tech debt scan
📊 ROI Calculation
Use case: Customer support chatbot
Current state:
- 1000 ticket/day
- Avg handle 8 min
- Agent cost $20/hour
- Daily cost: $2,667
With AI (50% deflection):
- 500 ticket/day automated
- Per ticket cost: $0.05
- Saved: 500 × 8 min × $20/h = $1,333/day
- AI cost: $25/day
- Net: $1,308/day = $475K/year ROI
🎯 Pilot Selection Criteria
- High volume, repetitive
- Clear success metric
- Low risk if fail
- Quick win (3 মাস)
- Stakeholder support
- Data available
🏗️ Build vs Buy Decision
| Build (custom) | Buy (SaaS) |
|---|
| Unique workflow | Standard problem |
| Privacy critical | Quick deploy |
| Strategic moat | Cost-effective |
| Engineering capacity | No AI team |
| Long-term ROI | Validate first |
👥 Team Structure
- AI Champion — internal advocate
- AI Engineer — build + maintain
- Prompt Engineer — optimize
- Data Engineer — RAG pipeline
- Domain Expert — eval, validate
- PM — coordinate
- Compliance — review
📋 Governance Checklist
- ☐ Approved tool list
- ☐ Data classification policy
- ☐ Training opt-out
- ☐ Audit logging
- ☐ Incident response plan
- ☐ Vendor risk assessment
- ☐ Compliance review (GDPR, HIPAA)
- ☐ Employee training
- ☐ Disclosure policy
- ☐ Quality benchmark
🚧 Common Pitfalls
- Solution looking for problem
- "Boil the ocean" — too ambitious
- No baseline measurement
- Underestimate change management
- Privacy/compliance afterthought
- Vendor lock-in
- One-time deploy, no iteration
- Ignore user adoption
🎓 Change Management
- Why before how (motivate)
- Training সবার জন্য
- Quick win celebrate
- Champion network
- Feedback channel
- Iterate based on usage
- Success story share
💡 Maturity Model
Level 0: No AI use
Level 1: Individual ad-hoc (ChatGPT)
Level 2: Approved tool, training
Level 3: Custom GPT/Project, workflow
Level 4: Custom apps, RAG, automation
Level 5: Agent-driven, autonomous workflow
বেশিরভাগ company Level 1-2-এ আছে এখন।