The AI Transformation Sprint: 6 Weeks from Discovery to Deployed POC

Your CFO just asked: "We've been working on AI for 18 months. When will we see something in production?"

You explain: "We're still in the pilot phase. We need to test the model more, get stakeholder buy-in, finalize the architecture, secure budget..."

She cuts you off: "Our competitor launched an AI-powered feature last quarter. What's taking us so long?"

Here's the uncomfortable truth: Most organizations treat AI pilots like traditional IT projects—long waterfall timelines, extensive planning, consensus-building, and risk mitigation that stretches initiatives over quarters or years.

The result:

• 18-24 months from idea to first deployed POC (if it happens at all)
• 60% of AI pilots never make it to production (Gartner research)
• By the time you deploy, competitive advantage is gone or requirements have changed
• Teams lose momentum, executives lose patience, and the initiative dies

Meanwhile, AI-native companies move at a completely different speed:

• 6-8 weeks from concept to deployed POC
• Rapid iteration based on real user feedback
• Fast failure (kill bad ideas in weeks, not months)
• Continuous learning that compounds into competitive advantage

The difference isn't technical capability. It's process. Organizations succeeding with AI have abandoned waterfall pilot approaches and adopted sprint-based methodologies that compress learning cycles and accelerate time-to-value.

Let me show you the 6-week AI Transformation Sprint methodology that takes you from initial discovery to a deployed proof-of-concept—with real users, real data, and measurable business value—in just 42 days.

The 18-Month AI Pilot Anti-Pattern

Month 1-3: Discovery and Business Case

• Multiple stakeholder meetings to define requirements
• Endless debates about use cases and priorities
• 50-page business case document
• Approval process through committees

Month 4-6: Data Preparation

• Inventory existing data sources
• Negotiate access to data from different systems
• Data quality assessment
• Build data pipeline infrastructure

Month 7-10: Model Development

• Experiment with different algorithms
• Iterate on features and hyperparameters
• Extensive testing for accuracy
• Documentation of technical approach

Month 11-14: Risk and Compliance Review

• Legal review
• Risk assessment
• Compliance checks
• Privacy review
• Ethics review

Month 15-18: Deployment Planning

• Production architecture design
• Integration planning
• Change management
• Training materials
• Rollout strategy

Month 19+: Deployment (Maybe)

• Integration delays
• Stakeholder concerns resurface
• Requirements changed during 18-month process
• Model performance degraded (data drift)
• 60% probability project gets cancelled before deployment

Why This Fails

Problem 1: Sequential Process Creates Compounding Delays

Each phase depends on previous phase completion. Any delay cascades:

• Data prep takes 1 month longer → Model development delayed
• Legal review takes 2 months longer → Deployment delayed
• Result: 18 months → 24+ months

Problem 2: Long Feedback Loops = Wrong Solution

You don't learn if solution works until Month 18+:

• By then, business needs changed
• Competitive landscape shifted
• Technology evolved (GPT-3 → GPT-4 → GPT-4.5)
• Initial assumptions invalidated
• Result: You build the wrong thing

Problem 3: Excessive Planning Without Empirical Data

You spend months planning based on assumptions:

• Assume data quality is good (it's not)
• Assume model will be accurate enough (untested)
• Assume users will adopt it (unvalidated)
• Assume ROI will materialize (uncertain)
• Result: Plans are fiction

Problem 4: Organizational Energy Depletes

18-month initiatives lose momentum:

• Executive sponsors change roles
• Team members leave
• Budget gets reallocated
• Urgency fades
• Result: Project dies of organizational neglect

Problem 5: Binary Success/Failure (All or Nothing)

After 18 months of investment:

• If it works → Great, but massive sunk cost
• If it fails → Catastrophic waste of time and money
• No room for rapid iteration or course correction

What AI-Native Organizations Do Differently

Philosophy: Learn through rapid experimentation, not extensive planning

Approach:

• Short sprints (4-6 weeks) from idea to deployed POC
• Real user feedback within weeks, not months
• Parallel workstreams (don't wait for perfect data or approvals)
• Minimum Viable AI (simplest model that proves value)
• Fast failure (kill bad ideas quickly, cheaply)
• Continuous iteration (learn → improve → repeat)

Timeline: 6 weeks → Deployed POC → Measure → Decide (scale / iterate / kill)

Success Rate: 2.4x higher production deployment rate than traditional 18-month pilots

The 6-Week AI Transformation Sprint Framework

Sprint Overview

Goal: Deploy a working AI proof-of-concept in production (with real users, real data) in 6 weeks

Scope: One focused use case (don't boil the ocean)

Team:

• Sprint Leader (1 person, owns delivery)
• Data Scientist (1-2 people, builds model)
• Engineer (1-2 people, builds deployment infrastructure)
• Business SME (1 person, domain expertise)
• Design/UX (0.5 person, user interface)
• Total: 4-6 people, full-time for 6 weeks

Deliverables:

• Working AI model in production
• Real user feedback
• Measured business impact
• Decision: Scale / Iterate / Kill

Week 1: Discovery & Definition (Day 1-7)

Goal: Define use case, success criteria, and data sources; align stakeholders

Day 1-2: Kickoff and Use Case Scoping

Activities:

• Kickoff Workshop (4 hours):
  • Business problem: What are we solving?
  • Current state: How is it done today? What's broken?
  • Target users: Who will use this?
  • Success criteria: What does "good enough" look like for a POC?
  • Constraints: Budget, timeline, data, tech stack

Output:

• One-Page Use Case Brief:

  USE CASE: [Name]
  BUSINESS PROBLEM: [What we're solving]
  TARGET USERS: [Who will use it]
  CURRENT PROCESS: [How it works today]
  AI SOLUTION: [How AI will help]
  SUCCESS CRITERIA (POC): 
    - Accuracy: [Target metric]
    - User satisfaction: [Target]
    - Business impact: [Target metric]
  OUT OF SCOPE (for POC): [What we're NOT doing]
  

Example:

USE CASE: Customer Support Ticket Auto-Routing
BUSINESS PROBLEM: 40% of support tickets mis-routed, causing 2+ day delays
TARGET USERS: 12 support agents, 5 specialized teams
CURRENT PROCESS: Manual review and routing by tier-1 agent (15 min/ticket)
AI SOLUTION: NLP model auto-categorizes and routes tickets to correct team
SUCCESS CRITERIA (POC):
  - Routing accuracy: ≥80% (vs. 60% manual)
  - Time to route: <1 minute (vs. 15 min manual)
  - Agent satisfaction: ≥7/10
  - Test on 500 tickets over 2 weeks
OUT OF SCOPE: Integration with CRM, auto-responses, complex workflows

Day 3-4: Data Discovery and Assessment

Activities:

• Inventory data sources:

  • What data exists?
  • Where is it? (databases, APIs, files, etc.)
  • How accessible? (API? Database access? Export?)
  • How fresh? (real-time, daily, weekly?)
  • Data volume? (10K rows? 1M rows?)

• Data quality spot check:

  • Pull sample data (1,000-10,000 records)
  • Quick analysis: completeness, accuracy, consistency
  • Identify data issues (missing values, errors, etc.)

• Label availability (for supervised learning):

  • Do we have labeled training data?
  • If not, can we create labels quickly?
  • How much labeled data do we need?

Output:

• Data Inventory Sheet:

  Data Source 1: Support tickets database
  - Location: PostgreSQL DB
  - Access: API available
  - Volume: 50K historical tickets
  - Freshness: Real-time
  - Quality: 85% complete (some missing category labels)
  - Labels: 30K tickets have manual categories (60% labeled)
  
  Data Source 2: Customer account data
  - Location: Salesforce
  - Access: API with rate limits
  - Volume: 10K active customers
  - Freshness: Daily sync
  - Quality: 95% complete
  

• Data Issues & Mitigation:

  Issue 1: Only 60% of tickets labeled
  → Mitigation: Use 30K labeled tickets for training, label 5K more manually
  
  Issue 2: Missing ticket text for 10% of records
  → Mitigation: Exclude from training set
  
  Issue 3: Category labels inconsistent (typos, variations)
  → Mitigation: Normalize labels (clean up data in Week 2)
  

Day 5-7: Sprint Planning and Stakeholder Alignment

Activities:

• Define 6-week roadmap:

  • Week-by-week milestones
  • Assign tasks to team members
  • Identify dependencies and risks

• Stakeholder alignment:

  • Present use case brief and sprint plan
  • Get buy-in from business stakeholders
  • Identify compliance/legal requirements (if any)
  • Clarify decision-making authority

• Set up infrastructure:

  • Provision cloud resources (compute, storage)
  • Set up version control (Git repo)
  • Create project documentation space (Confluence, Notion, etc.)
  • Set up communication channel (Slack, Teams)

Output:

• 6-Week Sprint Plan (1-page visual roadmap)
• Stakeholder Sign-Off (documented agreement to proceed)
• Development Environment Ready

Week 2: Data Preparation & Baseline Model (Day 8-14)

Goal: Clean data, build simplest possible baseline model, establish benchmark performance

Day 8-10: Data Preparation

Activities:

• Data extraction: Pull full training dataset

• Data cleaning:

  • Handle missing values (impute, drop, or flag)
  • Normalize inconsistent labels
  • Remove duplicates
  • Fix data quality issues identified in Week 1

• Feature engineering (basic):

  • Text preprocessing (tokenization, stopword removal, etc.)
  • Create basic features (ticket length, time of day, customer segment, etc.)

• Train/test split:

  • 80% training, 20% test
  • Ensure representative distribution

Output:

• Clean training dataset (ready for modeling)
• Test dataset (held out for evaluation)
• Data preprocessing pipeline (code to repeat process)

Day 11-14: Baseline Model Development

Activities:

• Build simplest model first:

  • For classification: Logistic regression or simple decision tree
  • For NLP: TF-IDF + Naive Bayes or Logistic Regression
  • For recommendations: Collaborative filtering (basic)
  • Goal: Establish baseline performance quickly, not perfect model

• Evaluate baseline model:

  • Accuracy, precision, recall, F1 score
  • Confusion matrix (where is model wrong?)
  • Error analysis (what types of mistakes?)

• Compare to current process:

  • How does baseline model compare to manual process?
  • Where is model better? Where is it worse?

Output:

• Baseline Model Performance Report:

  Model: Logistic Regression on TF-IDF features
  Accuracy: 75% (vs. 60% manual)
  Precision: 0.78
  Recall: 0.72
  F1: 0.75
  
  Error Analysis:
  - Most errors: Confusing "Technical Issue" vs. "Bug Report"
  - Hypothesis: Need better feature engineering (metadata features)
  
  Comparison to Manual:
  ✅ Better: 15 percentage points higher accuracy
  ✅ Better: 100x faster (instant vs. 15 min)
  ❌ Worse: Struggles with edge cases (rare categories)
  

• Model code (version controlled)

• Evaluation metrics dashboard

Week 3: Model Improvement & Testing (Day 15-21)

Goal: Improve model performance, test with realistic scenarios, build deployment version

Day 15-17: Model Iteration

Activities:

• Feature engineering (advanced):

  • Add metadata features (customer segment, time, priority, etc.)
  • Engineer domain-specific features (based on SME input)
  • Experiment with feature combinations

• Try more sophisticated models:

  • Ensemble methods (Random Forest, XGBoost)
  • Deep learning (if warranted and feasible)
  • Pre-trained models (BERT, GPT for NLP)

• Hyperparameter tuning:

  • Grid search or random search
  • Cross-validation
  • Optimize for target metric (accuracy, F1, precision, etc.)

Output:

• Improved Model Performance:

  Model: XGBoost with engineered features
  Accuracy: 84% (baseline: 75%, manual: 60%)
  Precision: 0.87
  Recall: 0.81
  F1: 0.84
  
  Improvement Analysis:
  - Added customer segment features → +4% accuracy
  - Used XGBoost ensemble → +5% accuracy
  - Total improvement: +9% over baseline
  

Day 18-21: Model Testing and Validation

Activities:

• Edge case testing:

  • Test on unusual inputs (typos, gibberish, multi-topic tickets)
  • Adversarial testing (intentionally confusing examples)
  • Boundary testing (ambiguous cases)

• User acceptance testing (UAT):

  • Show model predictions to 2-3 support agents
  • Ask: "Would you agree with this routing?"
  • Collect feedback on mistakes and near-misses

• Bias and fairness testing:

  • Check for unintended bias (by customer segment, geography, etc.)
  • Ensure model doesn't systematically disadvantage any group

• Model documentation:

  • How model works (high-level explanation)
  • Features used
  • Performance metrics
  • Known limitations

Output:

• Testing Report:

  Edge Cases: 78% accuracy (lower than overall 84%, expected)
  User Feedback: 8/10 agents agree with routing (strong signal)
  Bias Check: No significant bias detected across customer segments
  Limitations: Struggles with tickets in languages other than English
  

• Model Documentation (for stakeholders and future reference)

Week 4: Deployment Infrastructure & Integration (Day 22-28)

Goal: Build production-ready deployment, integrate with existing systems, prepare for launch

Day 22-24: Model Deployment

Activities:

• Containerize model:

  • Package model in Docker container
  • Include dependencies (libraries, frameworks)
  • Create API endpoint (REST API for predictions)

• Deploy to staging environment:

  • Cloud deployment (AWS SageMaker, Azure ML, GCP AI Platform, or Kubernetes)
  • Set up autoscaling (handle variable load)
  • Configure monitoring (latency, throughput, errors)

• Create prediction API:

  POST /api/v1/predict
  Body: {"ticket_text": "My account is locked", "customer_id": "12345"}
  Response: {"predicted_category": "Account Access", "confidence": 0.89}
  

Output:

• Deployed Model API (in staging environment)
• API Documentation (how to call the API)
• Monitoring Dashboard (latency, throughput, error rates)

Day 25-28: System Integration

Activities:

• Integrate with support ticket system:

  • When new ticket created → Call prediction API
  • Display predicted category to agent
  • Allow agent to accept/reject/override prediction

• Build user interface:

  • Simple UI for agents to review predictions
  • "Accept" button (use prediction)
  • "Override" dropdown (choose different category)
  • Confidence indicator (show model confidence)

• Implement feedback loop:

  • Capture agent accept/reject/override decisions
  • Log for future model improvement

• Integration testing:

  • End-to-end test (create ticket → model predicts → agent reviews)
  • Test error handling (API down, slow response, etc.)
  • Load testing (100 concurrent tickets)

Output:

• Integrated System (ticket system + AI model + UI)
• User Interface Mockup/Prototype
• Integration Test Results

Week 5: Pilot Launch & User Testing (Day 29-35)

Goal: Launch POC with small group of users, collect real-world feedback, measure business impact

Day 29-30: Pilot Preparation

Activities:

• Select pilot users:

  • 3-5 support agents (enthusiastic early adopters)
  • Train agents on how to use AI-assisted routing
  • Set expectations: "This is a test, we want your feedback"

• Define success metrics:

  • Routing accuracy: % of predictions accepted by agents
  • Time savings: Time to route tickets (before vs. after)
  • Agent satisfaction: Survey (1-10 scale)
  • Business impact: Tickets routed correctly on first attempt

• Launch readiness checklist:

  • ✅ Model deployed and tested
  • ✅ Integration working
  • ✅ Monitoring in place
  • ✅ Pilot users trained
  • ✅ Feedback mechanism ready
  • ✅ Rollback plan defined (if things go wrong)

Output:

• Pilot Launch Plan
• Success Metrics Dashboard
• Rollback Plan (how to disable AI if needed)

Day 31-35: Pilot Execution

Activities:

• Launch to pilot users:

  • Turn on AI-assisted routing for pilot agents
  • Monitor closely (daily check-ins)
  • Collect real-time feedback

• Daily stand-ups with pilot users:

  • What's working well?
  • What's not working?
  • Any bugs or issues?
  • Ideas for improvement?

• Monitor metrics:

  • Track routing accuracy daily
  • Measure time savings
  • Collect agent feedback surveys
  • Log all predictions and agent decisions

• Rapid iteration (if needed):

  • Fix critical bugs immediately
  • Adjust model or UI based on feedback
  • Re-deploy updated version (if necessary)

Output:

• 5 Days of Real-World Usage Data
• Agent Feedback (qualitative insights)
• Quantitative Metrics:

  500 tickets processed during pilot
  Routing Accuracy: 82% (agents accepted predictions)
  Time Savings: 12 min → 2 min per ticket (83% reduction)
  Agent Satisfaction: 8.2/10
  Business Impact: 15% reduction in mis-routed tickets
  

Week 6: Evaluation & Decision (Day 36-42)

Goal: Analyze results, make data-driven decision (scale / iterate / kill), document learnings

Day 36-38: Data Analysis and Insights

Activities:

• Quantitative analysis:

  • Calculate performance metrics (accuracy, time savings, etc.)
  • Compare to success criteria defined in Week 1
  • Calculate ROI (cost to build vs. value delivered)

• Qualitative analysis:

  • Review agent feedback (surveys, interviews)
  • Identify pain points and improvement opportunities
  • Capture success stories and failures

• Error analysis:

  • Where did model fail? (categorize errors)
  • Why did agents override predictions?
  • What would improve performance?

Output:

• POC Results Report:

  SUCCESS CRITERIA EVALUATION:
  ✅ Routing accuracy: 82% (target: ≥80%)
  ✅ Time to route: 2 min (target: <5 min)
  ✅ Agent satisfaction: 8.2/10 (target: ≥7/10)
  ✅ Business impact: 15% reduction in mis-routing (measurable)
  
  ROI ANALYSIS:
  - Investment: $60K (6 weeks, 4-6 people)
  - Annual value: $240K (12 agents × 10 min saved/ticket × 2,000 tickets/year)
  - ROI: 4x (first year)
  
  KEY INSIGHTS:
  - Model works well for common ticket types (80% of volume)
  - Struggles with rare/ambiguous tickets (needs more training data)
  - Agents trust predictions when confidence >85%
  - Biggest time savings: Eliminates manual categorization step
  
  IMPROVEMENT OPPORTUNITIES:
  - Add more training data for rare categories
  - Improve UI to show "similar past tickets" for context
  - Integrate with knowledge base for suggested responses
  

Day 39-41: Decision and Planning

Activities:

• Decision workshop with stakeholders:
  • Present POC results
  • Discuss findings and insights
  • Make decision: Scale / Iterate / Kill

Decision Options:

Option 1: SCALE (if POC successful)

• Roll out to all support agents (12 → 50+)
• Invest in production-grade deployment (monitoring, alerting, retraining)
• Budget: $150K for next 6 months (scale to production)
• Timeline: 3 months to full rollout

Option 2: ITERATE (if POC promising but needs improvement)

• Run another 4-6 week sprint to address issues
• Focus on specific improvements (more data, better UI, etc.)
• Budget: $40K for iteration sprint
• Timeline: Re-evaluate after iteration

Option 3: KILL (if POC failed to meet criteria)

• Document learnings
• Shut down project
• Reallocate resources to higher-value initiatives
• Cost: $60K sunk (but failed fast, not after 18 months)

Output:

• Decision: [Scale / Iterate / Kill]
• Rationale: [Why this decision makes sense]
• Next Steps: [Action plan for chosen option]

Day 42: Documentation and Handoff

Activities:

• Document learnings:

  • What worked? What didn't?
  • Key technical insights
  • Organizational lessons
  • Recommendations for future AI sprints

• Handoff (if scaling):

  • Transition from sprint team to production team
  • Knowledge transfer (technical documentation, runbooks)
  • Ongoing support plan

• Retrospective:

  • Sprint team reflection
  • What would we do differently next time?
  • Celebrate wins (even if decision is to kill)

Output:

• Sprint Retrospective Document
• Handoff Plan (if scaling)
• Lessons Learned (for future sprints)

Real-World AI Sprint Success

Let me share how a healthcare organization used this 6-week sprint to accelerate AI adoption.

Context:

• Mid-size hospital system (8 hospitals, 3,000 beds)
• Problem: No-show appointments (18% no-show rate, $12M annual loss)
• Previous approach: 18-month traditional pilot → cancelled after 14 months (no results)

Challenge:

• CFO skeptical: "We wasted 14 months and $300K. Why will this be different?"
• IT cautious: "We need more planning before deploying AI."
• Clinical staff resistant: "We don't trust algorithms with patient care."

Solution: 6-Week AI Sprint

Week 1: Discovery

• Use case: Predict no-show likelihood for appointments 48 hours in advance
• Success criteria: ≥75% accuracy, enable proactive outreach to high-risk patients
• Data sources: 2 years of appointment data (500K appointments), patient demographics, appointment history

Week 2: Data Prep + Baseline Model

• Cleaned 500K appointment records
• Baseline model (Logistic Regression): 71% accuracy
• Better than random (50%) but below target (75%)

Week 3: Model Improvement

• Added features: appointment type, day of week, weather, patient distance from hospital
• Upgraded to XGBoost: 78% accuracy (exceeded target!)
• Key insight: Patients >30 miles away + Friday afternoon appointment = 42% no-show rate

Week 4: Deployment

• Built API: Input (patient ID, appointment details) → Output (no-show probability)
• Integrated with scheduling system
• UI for staff: Color-coded risk (green/yellow/red)

Week 5: Pilot with 2 Clinics

• 150 patients flagged as high-risk (>60% no-show probability)
• Staff called high-risk patients 48 hours before appointment (reminder + address barriers)
• Tracked no-show rates for pilot vs. non-pilot clinics

Week 6: Evaluation

• Results:
  • No-show rate for high-risk patients: 42% → 23% (45% reduction)
  • Overall no-show rate in pilot clinics: 18% → 14% (22% reduction)
  • Staff satisfaction: 7.8/10 ("Helps prioritize who to call")
  • ROI: $180K annual value (per clinic) vs. $60K sprint cost = 3x ROI
• Decision: SCALE to all 8 hospitals

6-Month Post-Sprint Results:

• Deployed to all hospitals (3-month rollout)
• System-wide no-show rate: 18% → 13% (28% reduction)
• Annual value: $3.4M (reduced lost revenue)
• Total investment: $60K sprint + $200K scaling = $260K
• ROI: 13x (first year)

Key Success Factors:

1. Fast proof-of-value: Results in 6 weeks vs. 18 months
2. Real user feedback: Clinical staff involved from Week 1
3. Iterative improvement: Baseline → improved model in 2 weeks
4. Managed risk: Small pilot before full rollout
5. Data-driven decision: Clear metrics, no guesswork

CFO's Response: "This is how we should do all AI projects. Fast, focused, measurable."

Your 6-Week Sprint Toolkit

Tool 1: Sprint Planning Canvas

One-page visual to plan your sprint:

Tool 2: Weekly Sprint Checklist

Use this checklist to track progress each week:

Week 1: Discovery & Definition

• Kickoff workshop completed
• One-page use case brief finalized
• Data sources identified and assessed
• Data quality spot-checked
• 6-week roadmap created
• Stakeholder sign-off obtained
• Development environment set up

Week 2: Data Prep & Baseline Model

• Training data extracted and cleaned
• Train/test split completed
• Data preprocessing pipeline built
• Baseline model trained
• Baseline performance measured
• Error analysis completed
• Model code version-controlled

Week 3: Model Improvement & Testing

• Advanced features engineered
• Multiple models tested
• Best model selected
• Hyperparameter tuning completed
• Edge case testing done
• User acceptance testing conducted
• Model documentation written

Week 4: Deployment & Integration

• Model containerized
• API endpoint created
• Deployed to staging environment
• Monitoring dashboard set up
• Integrated with existing systems
• User interface built
• End-to-end integration testing passed

Week 5: Pilot Launch & User Testing

• Pilot users selected and trained
• Success metrics dashboard ready
• Pilot launched
• Daily user feedback collected
• Metrics tracked in real-time
• Critical issues resolved
• 5 days of usage data gathered

Week 6: Evaluation & Decision

• Data analysis completed
• POC results report written
• ROI calculated
• Decision workshop held
• Decision made (Scale / Iterate / Kill)
• Next steps planned
• Sprint retrospective completed
• Documentation finalized

Tool 3: Sprint Success Scorecard

Evaluate POC success using this scorecard:

Decision Rule:

• ≥80% score + all critical criteria met → SCALE
• 60-79% score → ITERATE (promising but needs work)
• <60% score → KILL (not viable)

Get Expert Sprint Facilitation

Running a successful 6-week AI sprint requires balancing speed with quality, managing stakeholder expectations, navigating technical challenges, and making fast decisions under uncertainty.

I facilitate AI Transformation Sprints for organizations ready to accelerate AI adoption—providing sprint leadership, technical guidance, and decision support to compress 18-month pilots into 6-week deployed POCs.

→ Book a consultation to discuss your AI sprint where we'll identify your highest-value AI use case, assess readiness for a 6-week sprint, and design a customized sprint plan for your organization.

Or download the AI Sprint Toolkit (Templates + Facilitator's Guide) with sprint planning canvas, weekly checklists, success scorecards, stakeholder communication templates, and technical playbooks.

The organizations moving fastest with AI don't plan for perfection—they sprint to proof-of-value and iterate based on real-world feedback. Make sure your AI initiatives deliver results in weeks, not years.