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Why 90 Days?

Your board approved the AI initiative. Budget: $500K. Timeline: "As fast as possible."

Your newly hired AI lead presents a 12-month roadmap. Months 1-3: Infrastructure buildout. Months 4-6: Data preparation. Months 7-9: Model development. Months 10-12: Testing and deployment.

Twelve months to deploy one model.

Your board's response? "Unacceptable."

They are right to be frustrated. Twelve-month timelines made sense in 2018. In 2026, they are organizational malpractice.

Modern AI projects should operate on 90-day cycles. Assessment to production. Idea to value. Zero to deployed model.

A 2024 Gartner study found that AI projects completed in under 90 days have 4x higher success rates than projects taking 6+ months. Why? Momentum. Focus. Urgency. Speed forces prioritization.

This article lays out the exact 90-day sprint methodology that ITSoli uses with clients to go from "we should do AI" to "our first model is in production generating measurable value."

Why Most AI Projects Take Too Long

Before we discuss the sprint, let us understand why projects drag.

Time Trap 1: Perfectionism

Teams wait for perfect data, perfect infrastructure, perfect models. Perfection takes forever and is rarely necessary.

A logistics company spent 5 months cleaning data before starting model development. They discovered after deployment that 80% of the cleaning was unnecessary—the model worked fine with messy data.

Wasted time: 4 months.

Time Trap 2: Scope Creep

Projects start narrow and expand. "Let us also add this feature." "We should handle this edge case." "Can we make it work for this other department too?"

Each addition adds weeks. Twelve small additions turn a 3-month project into a 9-month project.

Time Trap 3: Committee Decision-Making

Every decision requires three meetings and four approvals. Model architecture choice? Two-week approval cycle. Deployment approach? Three-week review process.

Bureaucracy kills momentum.

Time Trap 4: Handoff Delays

Data science team finishes the model. They "throw it over the wall" to engineering. Engineering schedules deployment for next quarter because their backlog is full.

Model sits for 8 weeks waiting for deployment slot.

Time Trap 5: No Dedicated Team

Team members are split across multiple projects. Your AI project gets 20% of their time. What should take 10 weeks of full-time work takes 50 weeks at 20% allocation.

The 90-Day Sprint Framework

Here is how ITSoli delivers production models in 12 weeks.

Week 1: Rapid Assessment and Scoping

Day 1-2: Business Problem Definition

Facilitated workshop with stakeholders. What business problem are we solving? What is the current state (baseline metrics)? What is success (target metrics)? What is the business impact (revenue, cost, risk)?

Output: One-page business case.

Example: Problem: Manual invoice processing takes 4 hours per invoice. Current state: Processing 200 invoices/week, 8% error rate. Success: Reduce processing time to <30 minutes, <2% errors. Business impact: Save $480K annually in labor costs.

Day 3-4: Data Assessment

What data exists? Where? In what format? What quality?

Do not wait for perfect data. Assess what you have and determine if it is "good enough" for a pilot.

Criteria for good enough: Covers the use case (even if sparse). Contains signal (not pure noise). Accessible within 2 weeks (no 6-month data engineering projects).

If data does not exist, pivot to a different use case. Do not spend weeks generating new data.

Day 5: Feasibility and Approach

Technical assessment. Is this problem solvable with current AI techniques? What modeling approach (classification, regression, NLP, computer vision)? What infrastructure (cloud, on-premise, vendor platform)? What are the risks?

Output: Technical approach document (3 pages max).

By End of Week 1: Defined business problem. Confirmed data exists. Selected technical approach. Identified risks. Secured stakeholder commitment.

If you cannot complete Week 1 in one week, your organization is not ready. Address organizational readiness before starting sprints.

Weeks 2-6: Build and Validate

Week 2: Data Pipeline and Exploration

Extract data. Clean minimally (remove obvious errors, handle nulls). Explore distributions and correlations.

Do not over-engineer pipelines. For a pilot, manual extraction is fine. Automate later if model proves valuable.

Output: Dataset ready for modeling.

Weeks 3-4: Model Development

Try 2-3 modeling approaches. Evaluate. Select best.

Do not chase 99% accuracy when 85% delivers business value. Diminishing returns kick in fast.

Example progression: Baseline (simple heuristic): 72% accuracy. Model 1 (logistic regression): 81% accuracy. Model 2 (gradient boosting): 87% accuracy. Model 3 (deep learning): 89% accuracy.

Stop at Model 2. The 2-point improvement from Model 3 is not worth the complexity and deployment challenges.

Output: Trained model, validation metrics, model documentation.

Week 5: Business Validation

Test model on real scenarios. Do predictions make sense? Are they actionable?

This is not statistical validation. This is business validation. Show predictions to domain experts. Ask: "Would you act on these recommendations?"

If experts say "these predictions are useless," no amount of accuracy improvement will fix it. You are solving the wrong problem.

Example: A churn prediction model had 91% accuracy. But the sales team said, "We already know which customers are at risk. We need to know WHY they are churning and WHAT to do about it."

The model needed redesign to provide explanations and recommendations, not just risk scores.

Week 6: Integration Planning

How will predictions reach end users? Dashboard? Email alerts? API integration with existing system? Embedded in workflow tool?

Design integration. Mock up UI. Get user feedback.

Do not build yet. Just design and validate.

Output: Integration design, user feedback incorporated.

Weeks 7-9: Deploy and Pilot

Week 7: Build Integration

Develop the integration designed in Week 6.

For pilots, simple is fine. A daily batch job that outputs to a spreadsheet? Totally acceptable.

Do not wait for real-time APIs and beautiful UIs. Ship something that works.

Week 8: Deploy to Pilot Users

Select 10-30 pilot users. Deploy model. Train them. Support them.

Monitor adoption and feedback closely. Are they using it? Does it help? What is frustrating?

Iterate daily if needed.

Week 9: Measure and Optimize

Track technical metrics (accuracy, latency, errors). Adoption metrics (percentage of users actively using the tool). Business metrics (impact on the target metric: cost, time, quality).

Identify issues. Fix them fast. This is rapid iteration mode.

Weeks 10-12: Scale and Handoff

Week 10: Expand Pilot

If pilot is successful (users are using it and it is delivering value), expand to broader user group.

If pilot failed, either pivot (different approach to same problem) or abandon (wrong use case, try different one).

Both are valid outcomes. Better to know fast than invest 12 months before discovering failure.

Week 11: Operationalize

Move from pilot infrastructure to production infrastructure (if different).

Set up monitoring, alerting, retraining schedules.

Document everything: code, model, deployment, monitoring.

Week 12: Handoff and Measure

Transfer ownership to operational team (if applicable).

Measure business impact. Calculate ROI.

Present results to stakeholders and executive sponsors.

By End of Week 12: Model in production. Users trained and using it. Business impact measured. Lessons documented. Celebration.

What Makes 90-Day Sprints Work

Several principles enable this speed.

Principle 1: Fixed Timeline, Flexible Scope

Timeline is non-negotiable: 90 days.

Scope is flexible: We will ship the best version we can build in 90 days.

If halfway through you realize the original vision is too ambitious, descope. Ship a simpler version that still delivers value.

Principle 2: Full-Time Dedicated Team

No splitting attention. Team members are 100% dedicated to this sprint for 90 days.

A 3-person team working full-time for 90 days accomplishes more than a 6-person team working part-time for 6 months.

Principle 3: Empowered Decision-Making

The sprint team has authority to make decisions without external approvals.

Architecture choices? Team decides. Tool selection? Team decides. Tradeoffs? Team decides.

If every decision requires committee approval, 90 days becomes 9 months.

Principle 4: Daily Progress, Weekly Milestones

Daily standups. Weekly demos. Constant momentum.

If a week passes without visible progress, something is wrong. Address it immediately.

Principle 5: Bias for Action Over Analysis

When in doubt, ship and learn. Do not spend 2 weeks debating the perfect approach. Try something. If it fails, try something else.

Velocity beats perfection.

Case Study: Insurance Claims Automation in 83 Days

A property insurance company wanted to automate damage assessment from photos.

Traditional approach estimate: 9-12 months. 3 months: Data collection and annotation. 3 months: Model development. 2 months: Integration with claims system. 2 months: Testing and UAT. 1 month: Deployment.

90-Day Sprint with ITSoli:

Week 1: Scoping and assessment. Problem: Manual damage assessment takes 2 days per claim. Data: 15K historical claims with photos and adjuster notes. Approach: Computer vision model to classify damage severity.

Weeks 2-6: Build phase. Week 2: Extracted and organized 15K photos, labeled severity. Weeks 3-4: Fine-tuned ResNet50 model on damage types. Week 5: Achieved 84% accuracy on severity classification. Week 6: Business validation with 100 test claims—adjusters confirmed usefulness.

Weeks 7-9: Deploy phase. Week 7: Built simple web interface for adjusters to upload photos. Week 8: Deployed to 15 pilot adjusters. Week 9: Monitored usage, fixed bugs, improved accuracy to 87%.

Weeks 10-12: Scale phase. Week 10: Expanded to 60 adjusters. Week 11: Integrated with claims management system. Week 12: Measured business impact.

Results: Model deployed in 83 days (ahead of schedule). Reduced assessment time from 2 days to 4 hours (83% reduction). Processing 200 claims/month with model. Projected savings: $640K annually. ROI on $95K investment: 674% in year 1.

Traditional approach would have taken 12 months and cost $280K.

When 90-Day Sprints Are Right (And When They Are Not)

90-Day Sprints Work Best For:

First AI Projects — When you are building organizational muscle, speed and learning matter more than perfection.

Scoped Use Cases — One department, one process, one decision type. Not enterprise-wide transformations.

Existing Data — You have data that is "good enough" today. Not requiring 6 months of new data collection.

Clear Business Metrics — You know what success looks like and can measure it.

Committed Stakeholders — Business owners who will support the sprint and remove blockers.

90-Day Sprints Are Wrong For:

Mission-Critical Systems — If failure means regulatory violations, patient harm, or financial catastrophe, take more time to ensure safety.

Complex Multi-System Integrations — If deployment requires coordinating with 10 legacy systems across 5 departments, 90 days may be unrealistic.

Novel Research Problems — If you are trying to solve something no one has solved before, exploration takes longer.

Greenfield Data Collection — If you need to build sensors, collect data for 6 months, then model, 90 days does not work.

For these cases, extend to 120-180 days. But even then, find ways to demonstrate value incrementally.

The ITSoli 90-Day Sprint Offering

ITSoli has productized the 90-day sprint for startups and mid-market enterprises.

What You Get:

Dedicated Team: 1 senior AI consultant (lead). 1-2 ML engineers. 1 data engineer. 1 project manager.

Proven Methodology: Week-by-week playbook. Templates for deliverables. Quality gates at each phase.

End-to-End Delivery: Assessment and scoping. Model development. Integration and deployment. Training and handoff.

Knowledge Transfer: Documentation of everything. Training your team on the model. Recommendations for future projects.

Pricing:

Standard Sprint: $75K-$125K depending on complexity. Includes all team members for 90 days. Deployment to production. 30 days post-deployment support.

Pilot Sprint: $60K for lower-risk use cases. Smaller team. Deploy to pilot users (not full production). Good for testing AI value before bigger commitment.

What Happens After 90 Days:

Option 1: Next Sprint — The first model delivered value. Start a new 90-day sprint for a different use case.

Option 2: Scale and Iterate — Expand the deployed model to more users, more data, more functionality. Typically 4-6 week engagement.

Option 3: Transition to Retainer — Move to ongoing partnership model where ITSoli provides continuous AI support.

Most clients do Option 1: Stack multiple sprints to build 3-5 production models in 12 months.

Building Your Own 90-Day Sprint Capability

If you want to run sprints in-house (or with a partner like ITSoli), here is what you need.

Organizational Readiness:

Executive Sponsor — One executive who believes in the sprint, has budget authority, and will remove blockers.

Dedicated Team — Full-time for 90 days. No splitting attention across multiple projects.

Decision Authority — The sprint team can make decisions without multi-week approval processes.

Defined Use Case — Clear problem, existing data, measurable success criteria.

User Access — Can deploy to pilot users for testing and feedback.

First Sprint Checklist:

Week before Sprint starts: Executive sponsor committed. Business owner identified and committed. Use case selected and scoped. Data location confirmed (even if messy). Team members identified and cleared calendars. Kickoff meeting scheduled.

Week 1: Business problem defined. Success metrics agreed. Data assessed as "good enough". Technical approach selected. Risks documented.

Week 6: Model trained and validated. Business users validated predictions. Integration approach designed.

Week 12: Model deployed to production or pilot users. Business impact measured. Lessons documented. Next steps identified.

If you cannot check all these boxes, you are building a demo, not a pilot.

Demos are fine for learning. But they should not be confused with production-bound projects.

From Sprints to Momentum

One 90-day sprint proves AI can work in your organization. It builds credibility.

Three sprints build muscle. Your team learns. Your processes improve. Your stakeholders trust AI.

Five sprints build momentum. AI is no longer experimental. It is part of how you operate.

By month 18, you have deployed 6 models. Learned what works. Built capability. Demonstrated ROI.

And you did it without 12-month projects that never ship.

Speed is a strategic advantage. Organizations that sprint beat organizations that marathon.

Set your timeline: 90 days. Assemble your team. Pick your use case.

Then sprint.