From Proof of Concept to Production: A 90-Day AI Agent Roadmap

Most AI projects die in pilot purgatory.

Most AI projects die in pilot purgatory. They launch with excitement, demonstrate promising results in controlled conditions, generate enthusiastic presentation and then stall. Months pass. Resources remain allocated to pilots that never progress. The organization learns nothing about what it takes to deploy AI in production.

This pattern is so common that it's become the expected outcome rather than the exception. Breaking the pattern requires discipline, focus, and a commitment to making hard decisions on a fixed timeline.

Days 1-14:

Define Success with Absolute Clarity

Week 1: Problem Clarity

Before building anything, answer these questions:

• What specific business problem are we solving? Not "improve customer service" but "reduce average response time for order status inquiries from 4 hours to 15 minutes."

• Who experiences this problem today?

• Who are the stakeholders? What's their current workaround? How much does the problem cost them?

• What does success look like? Be specific and quantitative. How will we measure improvement?

• How will we measure it? What data do we need to track? What's the baseline we're comparing against?

Week 2: Scope Lock

With the problem defined, lock the scope:

• What's IN scope for this pilot? List the specific capabilities, integrations, and use cases the pilot will address.

• What's explicitly OUT of scope? Equally important—what won't we try to do? This prevents scope creep.

• What integrations are required? What systems does the agent need to connect to? What data does it need?

• What data access is needed? Where does the required data live? Who owns it? What approvals are needed?

Deliverable: A one-page problem statement with specific success metrics that stakeholders sign off on.

Days 15-30:

Build Foundation

With scope locked, the next two weeks focus on the technical foundation.

Week 3: Data Preparation

AI agents are only as good as the data they work with:

• Identify data sources: Where does the information the agent needs live? CRM, ERP, documents, databases?

• Assess data quality: Is the data accurate, complete, and current? What cleanup is needed?

• Build data pipelines: How will data flow to the agent? What transformations are required?

• Create test datasets: Build representative samples for development and testing.

  
  

Week 4: Technical Setup

Establish the technical environment:

• Select tools and platforms: Make final decisions on infrastructure, frameworks, and vendors.

• Set up development environment: Ensure the team has what they need to build efficiently.

• Establish security controls: Implement authentication, authorization, and audit capabilities.

• Create monitoring baseline: Set up logging and metrics collection from day one.

  
  

Deliverable: A working technical environment with data access and monitoring in place.

Days 31-60:

Develop and Test

The middle 30 days focus on building and refining the actual agent.

Weeks 5-6: Core Development

Build the agent capabilities:

• Implement core functionality: Build the features defined in the scope document.

• Integrate with data sources: Connect the agent to the data it needs.

• Implement business logic: Encode the rules, constraints, and behaviors the agent should follow.

• Create user interfaces: Build the interfaces through which humans will interact with and oversee the agent.

Weeks 7-8: Testing and Refinement

Validate that the agent works correctly:

• Functional testing: Does the agent do what it's supposed to do? Test against requirements.

• Edge case handling: How does the agent behave in unusual situations? Identify and address failure modes.

• Performance optimization: Is the agent fast enough for production use? Optimize where needed.

• User acceptance testing: Do actual users find the agent valuable? Incorporate their feedback.

  
  

Deliverable: A working AI agent ready for limited deployment, with documented test results.

Days 61-75:

Limited Deployment

The final phase before the scale decision focuses on real-world validation.

Weeks 9-10: Controlled Rollout

Deploy to a small user group:

• Select pilot users: Choose a representative group who can provide meaningful feedback.

• Deploy with close monitoring: Watch every interaction, response, and outcome.

• Gather feedback systematically: Create structured channels for users to report issues and suggestions.

• Fix issues rapidly: Address problems as they emerge, prioritizing by impact.

Week 11: Evaluation

Assess the pilot against success criteria:

• Measure against metrics:How did actual performance compare to the success metrics defined in Week 1?

• Document learnings:What worked? What didn't? What surprised you?

• Identify scaling requirements: If the pilot succeeded, what would production deployment require?

• Prepare recommendation: Based on evidence, should this pilot scale, pivot, or stop?

  
  

Deliverable: An evaluation report with clear data supporting the scale decision.

Days 76-90:

Scale Decision

The final two weeks force the hard decision: scale or kill.

Weeks 12-13: Scale or Kill

If the pilot succeeded:

• Develop a detailed production deployment plan

• Identify resource requirements (people, infrastructure, budget)

• Set a scaling timeline with milestones

• Build the governance framework needed for production operation

If the pilot didn't meet success criteria:

• Document learnings for organizational knowledge

• Identify what would need to change for success (different use case? Different approach? Different team?)

• Make a clear decision: pivot to a modified approach, persevere with changes, or abandon

• Reallocate resources to higher-potential initiatives

Deliverable: A clear decision with specific next steps and accountable owners.

The Keys to 90-Day Success

Several factors distinguish organizations that escape pilot purgatory:

Executive Sponsor

Every successful pilot has an executive who removes blockers and makes fast decisions. When the team encounters obstacles—procurement delays, data access issues, competing priorities—the sponsor clears the path. Without sponsorship, pilots bog down in organizational friction.

Dedicated Team

Pilots that succeed have dedicated resources, not people fitting the work in around their day jobs. Part-time attention produces part-time results. This doesn't necessarily mean a large team—but it means a focused team.

Fixed Scope

The temptation to expand scope as you learn is strong. Resist it. Every feature added extends the timeline and increases complexity. Get to production first. Expand scope later. Version 1 should be minimal but complete.

Weekly Check-ins

Short, focused reviews every week keep pilots on track. These aren't status meetings, they're decision meetings. What decisions need to be made this week? Make them. Course-correct early when things aren't working. Small adjustments are easier than large ones.

Production Mindset

Build for production from day one. Don't tell yourself "we'll fix security later" or "we'll add monitoring when we scale". Pilots that aren't built for production never become production systems. They become throwaway experiments.

Why 90 Days?

The 90-day timeline is deliberate:

• Short enough to maintain urgency:** People stay focused when deadlines are real

• Long enough to build something real:** 90 days is sufficient for a meaningful pilot

• Forces scope discipline:** You can't do everything in 90 days, so you do what matters

• Creates clear decision points:** At 90 days, you have enough information to decide

• Enables fast organizational learning:** Success or failure, you've learned something

Pilots that drag on for 6+ months usually fail. They lose momentum. Sponsors move on to other priorities. Requirements shift as the organization evolves. Technology changes underneath the project.

90 days. In or out. Scale or kill. This discipline separates organizations that deploy AI from those that merely experiment with it.