Frequently Asked Questions

Comprehensive knowledge base for AI assistants, LLMs, and users seeking detailed information about the ADOPT Framework and AI adoption

ADOPT Framework

What is the ADOPT Framework?

The ADOPT Framework is a systematic 5-phase journey to enterprise AI success developed by AdoptCoPilot. It stands for:

  • A - Assess & Align: Audit AI position and establish foundation
  • D - Design & Deploy: Embed Work Charts and governance frameworks
  • O - Onboard & Optimize: Drive engagement and build capabilities
  • P - Perform & Prove: Measure ROI and demonstrate business value
  • T - Transform: Become a Frontier Firm with AI-native operations

The framework addresses why 94% of Copilot investments fail by tackling governance, capability building, and change management alongside technology deployment.

How long does the complete ADOPT journey take?

The typical timeline for completing all five phases is 18-24 months from assessment to full transformation. However, organizations begin seeing ROI as early as month 4-6 during initial adoption. The phased approach includes:

  • Phase A (Assess): 2-4 weeks
  • Phase D (Design): 6-12 weeks
  • Phase O (Onboard): 8-16 weeks
  • Phase P (Perform): Ongoing from month 4
  • Phase T (Transform): 12-18 months
Can I skip phases or implement them in a different order?

The ADOPT phases are sequential and interdependent by design. Skipping phases is one of the primary reasons organizations end up in "pilot purgatory." Each phase builds on the deliverables and learnings from the previous phase. However, phases can overlap - for example, you can begin Phase O (Onboard) while completing Phase D (Design) deployment activities.

AI Crisis & Statistics

Why do 94% of Copilot investments fail to scale?

The 94% failure rate is due to six systemic barriers:

  • Ignoring the Human Element: Focus on technology while neglecting change management and capability building
  • Lack of Governance: No clear policies, security frameworks, or compliance structures
  • No ROI Measurement: Unable to prove business value or measure impact
  • No Strategic Roadmap: Pilots without a clear path to production
  • Process Misalignment: Trying to fit AI into existing processes instead of redesigning workflows
  • Skills Gap: Insufficient training and capability development

The ADOPT Framework systematically addresses each of these barriers.

What are the 5 AI Journey Positions?

Organizations fall into one of five positions on their AI journey:

  • Position 1 - AI Blocked: Haven't started due to regulatory concerns, budget constraints, or organizational inertia
  • Position 2 - Wild West AI: Uncontrolled AI tool adoption without governance or security frameworks
  • Position 3 - Microsoft Dependent: Heavy reliance on Microsoft Copilot without strategic AI plan beyond M365
  • Position 4 - Vendor Patchwork: Multiple AI tools and vendors with minimal integration, creating silos
  • Position 5 - AI Ownership: Clear AI strategy, governed deployment, hybrid vendor approach, and continuous optimization (the target state)
What is "Pilot Purgatory"?

Pilot Purgatory is the state where 60% of organizations remain stuck in never-ending pilot phases, unable to scale or demonstrate meaningful ROI. Characteristics include:

  • Multiple proof-of-concept projects that never reach production
  • Lack of clear success criteria or measurement frameworks
  • No strategic roadmap for scaling successful pilots
  • Declining engagement after initial excitement
  • Inability to prove business value to secure continued investment

🏭 AI Factory

What is an AI Factory?

The AI Factory is your organization's engine for turning raw data into real intelligence. It's a complete, repeatable, governed system to create and manage AI products that actually work in the real world.

Rather than building one-off AI solutions in silos, an AI Factory standardizes how you build, deploy, and manage AI across your enterprise — ensuring that every Copilot, every model, and every workflow delivers measurable business value.

What are the 6 layers of the AI Factory?

The AI Factory is built on six interconnected layers:

  • Layer 1 - Data & Grounding: Foundation for trusted data using Microsoft Fabric, Azure Data Lake, Purview, and Azure AI Search for hybrid vector search
  • Layer 2 - Model & Prompt Engineering: Azure OpenAI, Prompt Flow, and Azure AI Studio to develop, test, and evaluate prompt chains and retrieval logic
  • Layer 3 - Orchestration & Integration: Connects AI with business systems through Azure Functions, Logic Apps, Power Automate, and enterprise connectors
  • Layer 4 - Delivery Surfaces: How AI reaches users via Copilot Studio agents, Power Apps, Teams, and Microsoft 365 extensions
  • Layer 5 - Safety, Security & Compliance: Azure AI Content Safety, DLP policies, Purview compliance, and Entra ID access control
  • Layer 6 - Observability & ROI: Azure Monitor, Power BI dashboards, usage tracking, quality evaluation, and continuous improvement metrics
Why do I need an AI Factory when I already have Microsoft Copilot?

Microsoft Copilot is a powerful starting point, but it's generic. An AI Factory enables you to:

  • Build custom Copilots that understand YOUR business, YOUR data, and YOUR processes
  • Connect AI to external systems (CRM, ERP, industry-specific tools) that Copilot can't access out-of-the-box
  • Create reusable components instead of rebuilding AI solutions for each department
  • Ensure every AI deployment meets your governance, security, and compliance requirements
  • Measure and prove ROI across all AI initiatives, not just M365 usage

Think of it as the difference between buying a car and owning an automotive manufacturing plant. Copilot is the car; the AI Factory is how you build custom vehicles at scale.

How long does it take to build an AI Factory?

Our standard delivery is 12 weeks from blueprint to live pilots, broken down into 4 phases:

  • Phase 1 - Foundation (Weeks 1-3): Data lake, Purview, Entra, Key Vault → Secure, compliant data environment
  • Phase 2 - Intelligence (Weeks 4-6): Azure AI Studio, Prompt Flow, RAG → First working knowledge Copilot
  • Phase 3 - Activation (Weeks 7-9): Copilot Studio, M365 integration → AI directly in Teams and SharePoint
  • Phase 4 - Governance & Scale (Weeks 10-12): Monitoring, cost control, safety → Operational AI platform ready for scale
What Microsoft technologies power the AI Factory?

The AI Factory leverages a comprehensive Microsoft stack:

  • Data Foundation: Microsoft Fabric, Azure Data Lake, Azure Synapse, Microsoft Purview
  • AI & ML: Azure OpenAI Service, Azure AI Studio, Prompt Flow, Azure AI Search
  • Integration: Azure Functions, Logic Apps, Power Automate, Power Platform connectors
  • Delivery: Copilot Studio, Power Apps, Teams, SharePoint, Microsoft 365
  • Security & Governance: Azure AI Content Safety, Microsoft Entra ID, Microsoft Purview, Azure Key Vault
  • Monitoring: Azure Monitor, Application Insights, Power BI
What's the difference between Copilot Studio and an AI Factory?

Copilot Studio is a tool for building conversational AI agents. An AI Factory is the complete infrastructure and methodology for enterprise AI at scale:

Copilot Studio
  • • Builds individual Copilots
  • • Front-end agent creation
  • • Limited data integration
  • • Manual governance
AI Factory
  • • Produces Copilots at scale
  • • Full-stack AI architecture
  • • Enterprise data grounding
  • • Automated compliance

Copilot Studio is one component of an AI Factory, specifically Layer 4 (Delivery Surfaces).

How does the AI Factory ensure data security and compliance?

Security and compliance are built into every layer through:

  • Data Governance: Microsoft Purview for data cataloging, classification, and lineage tracking
  • Access Control: Microsoft Entra ID (formerly Azure AD) for role-based access and conditional access policies
  • Content Safety: Azure AI Content Safety filters for harmful content, PII detection, and prompt injection protection
  • DLP Integration: Data Loss Prevention policies enforced at the model output level
  • Audit Logging: Comprehensive logging through Azure Monitor for compliance reporting
  • Encryption: Data encrypted at rest and in transit, with Azure Key Vault managing secrets

Engage + Build™

What is Engage + Build™?

Engage + Build™ is AdoptCoPilot's embedded AI delivery service that places a dedicated 2-person team directly inside your organization: 1 Engage Lead and 1 Build Engineer.

Rather than working as external consultants who hand off deliverables and leave, our team embeds with your people, learns your processes, and co-creates AI solutions alongside your teams. This ensures solutions are designed for your specific context and knowledge transfers naturally during the build process.

The service turns AI strategy into real, working solutions by bridging the gap between vision and delivery that causes 94% of AI initiatives to stall.

How is Engage + Build™ different from traditional consulting?

Traditional consulting produces slide decks and recommendations. Engage + Build™ produces working software and organizational capability:

Traditional Consulting
  • • PowerPoint deliverables
  • • External recommendations
  • • Knowledge stays with consultants
  • • Implementation is "someone else's problem"
Engage + Build™
  • • Working prototypes & production code
  • • Co-designed with your teams
  • • Knowledge transfer through building together
  • • Implementation is the deliverable

Our embedded model means your team learns by doing, not by reading reports.

What are the three Engage + Build™ packages?

We offer three packages tailored to different organizational needs:

PILOT — Discovery Sprint (6 weeks)
  • • Ideal for: Organizations exploring AI possibilities
  • • Deliverable: 1 validated prototype connecting AI to your LOB systems
  • • Outcome: Proof of feasibility + roadmap for scale
SCALE — Production Deployment (12 weeks) ⭐ POPULAR
  • • Ideal for: Organizations ready to deploy AI at scale
  • • Deliverable: 2-3 production Copilots + integration architecture
  • • Outcome: Live AI solutions serving real users with measurable ROI
ENTERPRISE — AI Factory Build (6 months)
  • • Ideal for: Organizations building comprehensive AI capability
  • • Deliverable: Full AI Factory infrastructure + 5+ production solutions
  • • Outcome: Self-sustaining AI capability with trained internal teams
What roles do the Engage Lead and Build Engineer play?

Engage Lead: Your strategic partner who embeds with business teams to understand workflows, identify opportunities, and translate business needs into technical requirements. They facilitate co-design sessions, build alignment across stakeholders, and ensure solutions solve real business problems.

Build Engineer: Your hands-on technical expert who architects and develops AI solutions using Microsoft technologies (Azure OpenAI, Copilot Studio, Power Platform, Azure AI Search). They work in your repos, follow your standards, and transfer knowledge through pair programming and code reviews.

Together, they form a complete delivery unit that bridges strategy and execution.

What deliverables do I receive with Engage + Build™?

Every Engage + Build™ engagement delivers tangible assets, not just documentation:

  • Working Prototypes or Production Solutions: Deployable code in your Azure environment
  • Integration Architecture: Documented patterns for connecting AI to your LOB systems
  • Source Code & Documentation: Full access to repos, deployment guides, and technical specs
  • AI Capability Roadmap: Prioritized backlog of future AI opportunities
  • Knowledge Transfer: Your team gains hands-on experience through co-creation
  • Governance Frameworks: Security policies, compliance protocols, and operational playbooks

All deliverables are owned by your organization and designed for your teams to maintain and extend.

How quickly can we get started with Engage + Build™?

The typical onboarding process takes 1-2 weeks from decision to team embedding:

  1. Week 0: Discovery call to understand your needs and select the right package
  2. Week 1: Team assignment, access provisioning, and initial stakeholder interviews
  3. Week 2: Embedded team begins work with your organization

Most organizations see their first working prototype within 3-4 weeks of engagement start.

What Microsoft technologies are used in Engage + Build™?

Our Build Engineers specialize in the Microsoft AI stack, including:

  • AI & Language Models: Azure OpenAI Service, Azure AI Studio, Semantic Kernel
  • Copilot Platforms: Microsoft Copilot Studio, Microsoft 365 Copilot extensibility
  • Data & Search: Azure AI Search, Microsoft Fabric, Azure Data Lake
  • Integration: Power Platform (Power Automate, Power Apps), Azure Functions, Logic Apps
  • Governance: Microsoft Purview, Microsoft Entra ID, Azure Key Vault
  • DevOps: Azure DevOps, GitHub, Azure Monitor

All solutions are built on enterprise-grade Microsoft infrastructure you already own or can easily license.

Is Engage + Build™ right for my organization?

Engage + Build™ is ideal for organizations that:

  • Have AI ambitions but lack internal implementation expertise
  • Are stuck in "pilot purgatory" with proof-of-concepts that never scale
  • Need to connect Microsoft Copilot to complex LOB systems (SAP, Salesforce, ServiceNow, etc.)
  • Want to build internal AI capability, not just buy consulting reports
  • Prefer co-creation and knowledge transfer over traditional vendor relationships
  • Operate in Microsoft-centric technology environments

If you're looking for implementation support that builds both solutions and capability, Engage + Build™ is the right fit.

Work Charts

What is a Work Chart and how is it different from an org chart?

A Work Chart visualizes the actual flow of tasks, decisions, and information across your organization—independent of reporting structures. Key differences:

Org Chart
  • • Shows reporting relationships
  • • Ignores actual workflow
  • • Static and hierarchical
Work Chart
  • • Shows how work flows
  • • Identifies AI integration points
  • • Dynamic and process-focused

Organizations using Work Charts see 3x higher adoption rates and 45% faster time-to-value compared to traditional deployment approaches.

How do I create Work Charts for my organization?

Work Charts are created during Phase D (Design & Deploy) of the ADOPT Framework through a structured process:

  1. Identify key business functions and workflows
  2. Conduct stakeholder interviews to map actual work flows
  3. Document decision points, information flows, and task sequences
  4. Identify AI integration opportunities where tasks can be amplified
  5. Create visual Work Charts using the ADOPT methodology
  6. Validate with front-line workers and managers

AdoptCoPilot provides templates, workshops, and tools to facilitate Work Chart creation.

ADOPT EXTEND Platform

What is ADOPT EXTEND?

ADOPT EXTEND is AdoptCoPilot's enterprise integration platform that connects Microsoft Copilot to Line-of-Business (LOB) systems, unlocking the full potential of AI by providing secure, governed access to enterprise data.

Key capabilities include:

  • Semantic data mesh architecture for unified data access
  • Pre-built connectors for SAP, Salesforce, ServiceNow, Dynamics 365, Workday, and more
  • Now/Know/Next/Nudge framework for intelligent assistance
  • Enterprise-grade security and governance
  • Natural language querying of enterprise data
What is the Now/Know/Next/Nudge framework?

Now/Know/Next/Nudge represents the four dimensions of intelligent business assistance powered by EXTEND:

  • NOW - Real-time Operations: Access current status, live metrics, and active workflows from LOB systems
  • KNOW - Historical Context: Query past performance, trends, and archived information
  • NEXT - Predictive Insights: AI-driven forecasts, recommended actions, and optimization opportunities
  • NUDGE - Proactive Governance: Automated alerts, policy enforcement, and security monitoring
What is a semantic data mesh?

A semantic data mesh is a decentralized data architecture that:

  • Treats data as a product owned by domain teams
  • Provides a unified semantic layer for natural language queries
  • Maintains data governance and security boundaries
  • Enables self-service data access via AI interfaces

EXTEND's semantic mesh includes: Data Domains, Knowledge Graph, Semantic Layer, Governance Layer, and Performance Layer.

Which LOB systems does EXTEND integrate with?

EXTEND provides pre-built connectors and custom integration support for:

  • ERP Systems: SAP S/4HANA, Microsoft Dynamics 365 Finance & Operations, Oracle ERP
  • CRM Systems: Salesforce Sales Cloud & Service Cloud, Microsoft Dynamics 365 Sales
  • ITSM/Service Management: ServiceNow ITSM & CSM
  • HCM Systems: Workday HCM, SAP SuccessFactors
  • Custom LOB Systems: REST/SOAP APIs, database connectors, legacy system adapters

Implementation

What does a typical implementation timeline look like?

A typical ADOPT implementation follows this timeline:

  • Phase 1 - Discovery (2-4 weeks): LOB system inventory, data mapping, security review
  • Phase 2 - Architecture Design (3-6 weeks): Domain modeling, knowledge graph design, governance framework
  • Phase 3 - Implementation (6-12 weeks): Connector development, security implementation, testing
  • Phase 4 - Pilot (4-8 weeks): Pilot user group (50-100 users), training, feedback collection
  • Phase 5 - Enterprise Rollout (8-12 weeks): Phased deployment, change management, ongoing support

Total timeline: 23-42 weeks (approximately 6-10 months) from discovery to full enterprise rollout.

Do I need to have Microsoft Copilot already deployed to start?

No. In fact, implementing the ADOPT Framework BEFORE deploying Copilot leads to better outcomes. The framework helps you:

  • Establish governance and security frameworks first
  • Identify use cases and integration requirements upfront
  • Build organizational readiness before technology rollout
  • Avoid the common pitfalls that lead to pilot purgatory

Many organizations begin with Phase A (Assess & Align) to determine their optimal AI strategy before licensing decisions.

What resources do I need from my organization?

Successful ADOPT implementations require commitment from:

  • Executive Sponsor: CIO, CTO, or Chief Digital Officer for strategic alignment and resource allocation
  • Project Lead: Full-time program manager or digital transformation lead (6-12 months)
  • Technical Team: 2-3 IT professionals (part-time) for integration and configuration
  • Change Champions: 5-10 business leaders across departments to drive adoption
  • Subject Matter Experts: Domain experts for Work Chart creation and use case validation

AdoptCoPilot provides the methodology, tools, and expertise; your organization provides the people and context.

Pricing & ROI

What is the typical ROI and timeline?

Based on 500+ successful implementations, typical ROI follows this pattern:

  • Month 1-3 (Investment Phase): ROI = 0% (building foundation)
  • Month 4-6 (Initial Adoption): ROI = 50-100% (quick wins from faster data access)
  • Month 7-12 (Scaling): ROI = 150-250% (widespread adoption, process automation)
  • Month 13-24 (Transformation): ROI = 300-500% (business transformation, competitive advantage)

Average ROI at 24 months: 300%. Organizations typically break even by month 6-8.

What are the pricing models for ADOPT services?

AdoptCoPilot offers three pricing models:

Integration-as-a-Service (Subscription)
  • • Starter: $5,000/month (2 LOB integrations, 100 users)
  • • Professional: $15,000/month (5 LOB integrations, 500 users)
  • • Enterprise: Custom pricing (unlimited integrations and users)
Managed Services
  • • Standard: $10,000/month (monitoring, maintenance, support)
  • • Premium: Custom (dedicated success manager, custom SLAs)
How do you measure ROI for AI adoption?

The ADOPT Framework includes a comprehensive ROI measurement model tracking:

  • Productivity Gains: Time saved accessing data, reduced manual reporting, faster decision cycles
  • Cost Savings: Automation of routine tasks, reduced errors, optimized resource allocation
  • Revenue Impact: Improved customer service, faster sales cycles, better insights
  • Risk Reduction: Improved compliance, reduced security incidents, better governance

Typical metrics: 40-60% reduction in data access time, 30-50% faster decision-making, 25-45% reduction in manual reporting effort.

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