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How We Implement AI Agents Discovery, Pilot, Stable Operation in 90 Days

A proven three-phase approach that validates fit, reduces risk, and delivers stable operation within three months. Not a one-time deployment. A structured journey from initial assessment to sustained operation.

Explain our implementation methodology, timelines, resource requirements, and what clients experience at each phase. Address concerns about disruption, complexity, and effort.

The Implementation Reality

Most AI implementations fail not because of technology, but because of approach.


Companies either move too fast without validation, or delay indefinitely waiting for perfect conditions.


Our implementation methodology balances these extremes. We start small, validate thoroughly, and achieve stable operation within 90 days. Each phase has clear deliverables, decision points, and go or no-go criteria.

This is not a sales process disguised as implementation. This is a structured approach that works whether you proceed to full deployment or determine AI agents are not right for your situation yet.


This describes how we typically help a mid-market company implement their first AI agent process. Your timeline might be shorter or longer depending on your ERP setup, exception volume, and how your team works. We figure out the specifics together during discovery.

The Three-Phase Implementation Model

Our implementation follows three distinct phases:

Phase 1
Discovery (Weeks 1-2)

Understand your situation and determine fit

Phase 2
Pilot (Week 3-8)

Validate approach with limited scope in production

Phase 3
Stable Operation (Week 9-12)

Optimize and transition to ongoing management

Not every engagement proceeds through all three phases. Some companies determine during Discovery that they are not ready. Some complete Pilot and choose to pause. This is acceptable and expected.
TIMELINE OVERVIEW:

Discovery to Decision

1-2 weeks

Pilot Implementation 

6 weeks from kickoff

Stable Operation

4 weeks refinement

Total Timeline

90 days to stable production

After stable operation is achieved with one process, expansion to additional processes follows proven patterns and moves faster.

Phase 1 - Discovery (Weeks 1-2)

What Discovery Involves

Week 1: Current State Assessment

  • Exception volume analysis (how many monthly, by type)

  • Current handling process documentation

  • Staff time analysis (coordination hours, resolution rates)

  • ERP integration assessment (API availability, data access)

  • System architecture review (infrastructure, security requirements)

Week 2: Feasibility and Recommendation

  • Automation potential calculation (realistic percentage)

  • Technical feasibility confirmation

  • Risk assessment (technical, operational, organizational)

  • Readiness scoring (are you ready now or should you wait)

  • Go or no-go recommendation with reasoning

Discovery Deliverables

At the end of Discovery, you receive:

  • Current State Documentation - Your exception handling process mapped and quantified

  • Automation Feasibility Assessment - Realistic automation percentage (typically 60-70%)
     

  • Technical Readiness Report - ERP integration approach and requirements

  • Readiness Score - Assessment across five dimensions with gaps identified

  • Implementation Recommendation - Proceed now, improve readiness first, or do not proceed

Time Investment

Your Team

6-8 hours across 2 weeks (interviews, process review)

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Your IT Team

2-3 hours (API/architecture review)

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Outcome

Clear decision with supporting data

Proceed to Pilot
60-70% of Cases

Fit confirmed, readiness validated, compelling business case identified.

Improve Readiness First
20-25% of Cases

Potential exists but gaps need addressing (documentation, upgrades, prep).

Do Not Proceed
5-10% of Cases

Volume too low, growth insufficient, or processes not mature enough.

This is an honest assessment. If you are not ready, we tell you why and what would need to change.

Phase 2 - Pilot Implementation (Weeks 3-8)

What Discovery Involves

Week 3: Setup and Integration

  • ERP API integration and testing

  • Decision rule configuration

  • Approval workflow setup

  • Monitoring dashboard deployment

  • Staff training on oversight process

Week 4: Shadow Mode

  • AI identifies exceptions but does not contact anyone

  • Staff review AI recommendations

  • Decision logic refined based on feedback

  • No customer or vendor contact yet

Week 5-7: Live Pilot

  • AI handles defined exception subset in production

  • Human oversight initially intensive (daily review)

  • Escalations reviewed promptly

  • Performance data collection begins

  • Rules refined based on actual results

Week 8: Pilot Assessment

  • Performance analysis (automation rate, quality, adoption)

  • Staff feedback collection

  • Customer and vendor feedback review

  • Go or no-go decision for Phase 3

Pilot Objectives

The Pilot validates three critical questions:

  • Technical: Can AI agents integrate with your ERP and handle your exception types effectively?

  • Operational: Does automation improve outcomes compared to manual handling?

  • Organizational: Will your team adopt this approach and manage it effectively?

The Pilot is not a proof of concept. It handles real exceptions with real customers and vendors in a controlled production environment.

Pilot Scope

Limited by Design:

  • One exception process (typically AR collections or AP three-way match)

  • One customer or vendor segment (often 20-30% of total volume)

  • Defined exception types (excludes VIP accounts, complex situations initially)

  • Clear escalation rules (when AI should immediately involve humans)

Success Criteria Defined Upfront:

  • Automation rate target (typically 60-70%)

  • Quality benchmarks (compared to manual baseline)

  • Customer or vendor feedback (neutral or positive)

  • Staff adoption (are they using it effectively)

Pilot Resource Requirements:

Your Team Commitment:

  • Week 3: 12-15 hours (integration, setup, training)

  • Week 4: 6-8 hours (shadow mode review and refinement)

  • Week 5-8: 3-4 hours per week (oversight and management)

Your IT Team:

  • API access configuration: 2-4 hours (Week 3)

  • Integration testing: 2-3 hours (Week 3)

  • Ongoing support: minimal (escalations only)

Pilot Decision Point (Week 8)

At Week 8, results determine next steps:

Outcome 1
Clear Success (70% of pilots)

  • Automation rate meets or exceeds target (60-70%)

  • Quality matches or beats manual baseline

  • Staff comfortable with oversight model

  • Customer or vendor feedback neutral to positive

  • Decision: Proceed to Stable Operation phase

Outcome 2
Success with Adjustments (20% of pilots)

  • Automation rate acceptable but refinement needed

  • Quality good with minor improvements identified

  • Staff needs process adjustments

  • Specific improvements identified

  • Decision: Implement adjustments, extend pilot 2 weeks, then decide

Outcome 3
Not Ready (10% of pilots)

  • Automation rate significantly below target (<50%)

  • Quality issues requiring significant rework

  • Staff resistance or adoption challenges

  • Organizational or process gaps discovered

  • Decision: Pause implementation, address gaps, reassess readiness

What Happens If Pilot Does Not Succeed

You have invested 6-8 weeks to learn your actual automation potential, where process gaps exist, and what readiness looks like. This is not a failure. This is validation preventing a larger failed implementation.

Phase 3 - Stable Operation (Weeks 9-12)

What Discovery Involves

Week 9-10: Optimization

  • Pilot performance analysis (what worked, what needs improvement)

  • Pattern identification (where AI excelled, where it struggled)

  • Rule updates deployed

  • Escalation thresholds adjusted

  • Staff feedback integrated

Week 11: Validation

  • Performance improvement measurement

  • Quality verification across full scope

  • Staff confirmation of sustainable oversight model

  • Process documentation finalized

Week 12: Transition

  • Move from pilot support to ongoing management

  • Establish monthly review schedule

  • Define quarterly assessment process

  • Document lessons learned for future process expansion

Stable Operation Objectives

Assuming Pilot success, Stable Operation optimizes performance and transitions to ongoing management:

  • Rule Optimization - Fine-tune decision logic based on pilot results

  • Escalation Threshold Adjustment - Reduce unnecessary escalations, catch risky situations earlier

  • Quality Stabilization - Address any remaining quality gaps

  • Oversight Process Maturation - Move from intensive daily review to review-by-exception

  • Ongoing Management Training - Establish sustainable oversight routines

Pilot Scope

At Week 12, you have:
Mature Single-Process Operation:

  • 60-70% of exceptions handled by AI within defined scope

  • 20-30% AI-assisted (AI gathers info, human decides)

  • 10% human-only (VIP accounts, complex situations)

  • 90-92% quality consistently (measured against manual baseline)

  • 3-4 hours weekly oversight model established

  • Clear patterns identified for expansion to additional processes

Expansion Readiness:

  • Lessons documented for applying to next process

  • Staff trained and comfortable with management approach

  • Technical integration proven and stable

  • Performance baseline established

Stable Operation Resource Requirements

Your Team Commitment:

Week 9-10
4-5 hours per week 

optimization and testing

Week 11-12
3-4 hours per week

validation and transition

Ongoing:
 3-4 hours per week

sustainable management model

Beyond 90 Days - Expansion and Scale

Expanding to Additional Processes
Once first process is stable (Week 12), expansion follows proven patterns:

Next Process Implementation (Weeks 13-20):

  • Discovery: 3-5 days (faster, lessons already learned)

  • Setup: 1 week (infrastructure already exists)

  • Pilot: 4 weeks (shorter, team experienced)

  • Stable Operation: 2 weeks (streamlined based on first process)

  • Timeline: 8 weeks from start to stable second process.

Each additional process benefits from:

  • Established infrastructure

  • Experienced team

  • Proven integration approach

  • Documented decision-making patterns

  • Refined oversight processes

Typical Expansion Pattern:

  • Month 1-3: First process to stable operation (90 days)

  • Month 4-5: Second process implementation (8 weeks)

  • Month 6-7: Third process if applicable (6 weeks)

  • Month 8+: Mature multi-process operation

What Mature Operation Looks Like

Monthly Management (3-4 hours per process):

  • Performance dashboard review (automation rate, quality, escalations)

  • Exception review (unusual patterns, quality issues)

  • Rule refinement decisions

  • Escalation threshold adjustments

Quarterly Assessment (2-3 hours):

  • Trend analysis (improving, stable, or declining)

  • Rule effectiveness evaluation

  • Process improvement opportunities

  • Expansion planning

Continuous Improvement:

  • Pattern recognition revealing systemic issues

  • Rule refinement based on results

  • Process improvements eliminating exceptions at source

  • Escalation threshold optimization

Companies that maintain 3-4 hours weekly oversight see sustained 70-75% automation rates. Those that neglect management see quality drift to 55-60% within 6-12 months.

What You Are Committing To

Time Commitment Summary

Discovery:

6-8 hours over 2 weeks

Stable Operation:

3-4 hours per week for 4 weeks

Pilot Setup:

12-15 hours in Week 3

Ongoing Management:

3-4 hours per week (sustainable model)

Pilot Operation:

3-4 hours per week for 6 weeks

Total First 90 Days:

Approximately 60-70 hours of your team time

Ongoing:

3-4 hours weekly per process (12-16 hours monthly)

This is not "set and forget." This is managed automation with human oversight

Infrastructure & Subscription Requirements

Beyond our implementation services, you will need access to certain infrastructure and subscriptions to run AI agents:

Voice AI Platform Subscription

AI agents need a voice conversation platform to make calls and conduct interactions. This is a monthly subscription service separate from our implementation. The platform handles call routing, voice recognition, conversation management, and integration with your ERP.

AI Model Access

AI agents require access to large language models like OpenAI or Claude for conversation and decision-making capabilities. These are monthly subscription services based on usage volume.

Workflow Automation Platform

AI agents use workflow automation platforms like n8n or similar to orchestrate processes, connect systems, and manage logic flows. This provides the coordination layer between your ERP, voice platform, and AI models.

Cloud Infrastructure

AI agents run on cloud infrastructure (typically AWS, Azure, or Google Cloud). If you already use cloud services, agents can run in your existing environment. If not, you will need basic cloud infrastructure. Most companies incur modest costs for compute and storage resources.

ERP API Access

Your ERP system needs to provide API access for the AI agents to read exception data and update records. Most modern ERPs include API access in standard licensing. Some older systems may require an API module add-on.

Monitoring and Analytics

We provide a pre-built monitoring ecosystem that includes performance dashboards, activity tracking, and analytics specifically designed for AI agent operations. This gives you visibility into automation rates, quality metrics, escalations, and patterns without building custom reporting.

What This Means for You

These are ongoing operational costs separate from implementation services. The exact requirements depend on your exception volume, call volume, and infrastructure choices. We help you understand and plan for these costs during the discovery conversation. Most mid-market companies find these operational costs manageable compared to the capacity and working capital benefits.

We do not mark up or resell infrastructure services. You contract directly with platform and infrastructure providers and maintain full control over your environment.


Investment Discussion:
Implementation investment varies based on:

  • Exception volume and complexity

  • Number of processes being implemented

  • ERP environment and integration requirements

  • Organizational readiness and change management needs

  • Timeline preferences and resource availability

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We discuss investment during the discovery conversation when we understand your specific situation. Our approach is structured to validate fit before significant investment, with clear decision points at each phase.

Common Implementation Questions

What Makes Our Approach Different

We Tell You If You Are Not Ready

Many vendors will implement regardless of readiness. We conduct honest Discovery and tell you if gaps exist. Better to know during Week 1 than discover at Week 6.

We Validate Before Full Commitment

The Pilot is a real validation with clear success criteria. Not a proof of concept designed to lead inevitably to full deployment.

We Plan for Sustainable Management

We build 3-4 hours weekly oversight into the implementation model. Companies that plan for ongoing management sustain value. Those expecting zero effort experience quality drift.

We Measure Against Your Manual Baseline

Success is not "AI achieved 95% automation." Success is "AI at 68% automation with 92% quality outperforms manual at 100% volume with 85% quality and staff burnout."

We Achieve Stable Operation in 90 Days

Not 6 months. Not 12 months. Three months from discovery to stable production for first process. This makes implementation approachable for mid-market companies.

Implementation Readiness

When We Recommend Waiting

Process Immaturity:

If exception handling processes are undocumented or highly variable, AI will amplify inconsistency. Document and standardize first, then automate.

ERP Instability:

If your ERP is undergoing major upgrades or migration, wait until stable. AI agent integration needs a stable foundation.

Organizational Resistance:

If staff view AI as a threat and leadership has not addressed this, implementation will struggle. Cultural readiness matters as much as technical readiness.

Insufficient Volume:

If you have fewer than 60 exceptions monthly with stable or declining growth, timing may not be right. We discuss volume thresholds during discovery.

Unclear Decision Logic:

If your team cannot explain how they decide what to do with exceptions, AI cannot replicate it. Clarify logic first.

When We Recommend Proceeding

Clear Process Logic:

Your team can explain how they handle exceptions. Decisions are based on documentable rules, not just intuition.

Volume Growth:

You have sufficient exception volume and it is growing. Staff are overwhelmed or approaching capacity limits.

ERP Stable:

Your ERP is on a stable version with accessible APIs. IT team willing to provide integration support.

Organizational Readiness:

 Leadership committed, staff cautiously willing, pilot scope achievable within existing resources.

Clear Value Path:

Discovery reveals compelling working capital benefits, capacity expansion, or quality improvement opportunity.

After Stable Operation - Ongoing Management

Sustainable Oversight Model

Weekly Review (3-4 hours per process):

  • Performance dashboard check (automation rate, quality metrics)

  • Escalation review (why AI escalated, was it appropriate)

  • Quality spot check (sample interaction review)

  • Immediate adjustments if needed

Monthly Deep Dive (1-2 hours):

  • Trend analysis (improving, stable, or declining)

  • Pattern identification (recurring issues, opportunities)

  • Rule refinement planning

  • Exception elimination opportunities

Quarterly Assessment (2-3 hours):

  • Full performance review

  • Staff feedback collection

  • Process improvement identification

  • Expansion readiness evaluation

Continuous Improvement Model

  • Pattern recognition revealing systemic issues

  • Rule refinement based on actual results

  • Escalation threshold optimization

  • Process improvements eliminating exceptions at source

Companies that invest 3-4 hours weekly see sustained 70-75% automation rates and continuing improvement. Those that neglect oversight see quality drift to 55-60% within 6-12 months.

Schedule Discovery Conversation

Ready to understand your automation potential and 90-day implementation path? Schedule a discovery conversation. We will evaluate your exception volume, processes, and readiness. You will receive an honest assessment of fit and a realistic timeline for your situation.
 

Discovery conversation includes:

  • Current state analysis

  • Automation feasibility assessment

  • Technical readiness evaluation

  • Readiness scoring across five dimensions

  • Go or no-go recommendation with reasoning

  • Investment discussion tailored to your situation

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Time required: 6-8 hours of your team time over 2 weeks Outcome: Clear decision with supporting data

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