GenAI pilots are easy to launch—and easy to stall. Many organizations prove value in a small group, but struggle to scale across functions because data isn’t ready, guardrails are unclear, adoption is uneven, and success metrics are not operationalized. To move from novelty to measurable impact, copilots must be productized: connected to real workflows, governed like enterprise software, and measured like any other business program.
An enterprise launched an initial GenAI pilot to assist employees with content drafting and Q&A. Early feedback was positive, but scaling introduced new obstacles.
Key challenges included:
Too many use cases, no prioritization
Every department wanted “a copilot,” but use cases varied widely. Without a structured selection process, ROI and delivery timelines were unpredictable.Fragmented knowledge and inconsistent data access
Relevant information lived in SharePoint, PDFs, CRM records, ticketing systems, and internal wikis. There was no unified approach to retrieval, permissions, or freshness.Security and compliance concerns
The organization needed data protection, auditability, and controlled tool use—especially when copilots interacted with customer data, contracts, or regulated workflows.Inconsistent user experience
Different teams built separate prototypes with different prompts, tools, and UI patterns. Users lacked a consistent experience and trust in outputs.No production operating model
There was no monitoring for quality, drift, usage, or risk. The pilot had no path to enterprise reliability, SLAs, or support ownership.
The goal was to build a scalable GenAI copilot platform and deploy function-specific copilots quickly—while ensuring security, governance, and measurable value.
Maayan Technologies delivered an enterprise GenAI copilot program using a product mindset: platform + reusable components + governed delivery + measurable outcomes.
1) Use-Case Factory and Prioritization Framework
We established a structured intake and scoring model to select and sequence the highest-value use cases across functions. Use cases were scored on:
Frequency and time saved
Business impact and risk level
Data availability and integration complexity
Compliance sensitivity and approval needs
Feasibility to deliver within defined sprints
This created a clear rollout roadmap across 12 functions (e.g., customer support, sales, marketing, HR, finance ops, procurement, legal, IT ops, engineering enablement, compliance, supply chain, and leadership reporting).
2) Shared Copilot Platform (Reusable Core)
Instead of building 12 separate systems, we built a shared copilot foundation:
Retrieval architecture for internal knowledge (search + RAG)
Identity-aware permissions and access control
Prompt and tool orchestration layer
Policy guardrails and content filters
Logging, audit trails, and feedback capture
Monitoring dashboards for usage, quality, and performance
Function copilots used the same platform components, accelerating delivery and ensuring consistency.
3) Knowledge Readiness and “Trusted Sources” Layer
We curated high-value knowledge sources and implemented:
Document ingestion and normalization pipelines
Metadata tagging, freshness policies, and version control
Role-based retrieval so users only saw what they were allowed to see
Grounded response generation with citations to source documents
This improved trust and reduced hallucination risk.
4) Workflow Integration and Tool Use
Copilots were connected to real workflows—not just chat. Depending on function, copilots could:
Draft emails, proposals, and summaries
Retrieve account and case context from CRM/helpdesk
Generate meeting notes and action items
Create structured tickets, work orders, or approvals
Build reports, risk summaries, and compliance checklists
Tool use was governed by allow-lists and approval gates for sensitive actions.
5) Governance, Safety, and Human-in-the-Loop Controls
We established a Responsible AI operating model with:
Policy tiers by data sensitivity
Human approval for high-impact outputs
Red-teaming and prompt safety testing
Usage analytics and exception monitoring
Incident response playbooks for AI failures or policy breaches
This enabled scale without unmanaged risk.
6) Adoption Program and Continuous Improvement
Scaling required change management:
Role-based training and playbooks for each function
“Copilot champions” network and feedback rituals
In-product feedback capture and weekly iteration cycles
KPI measurement aligned to business outcomes, not vanity usage
The program delivered enterprise-scale adoption with measurable benefits:
Scaled copilots into production across 12 business functions with a consistent experience and shared platform.
Improved productivity and reduced cycle times for high-frequency tasks (drafting, summarizing, searching, triage, reporting).
Higher trust and lower risk through grounded responses, access controls, and audit logs.
Faster delivery of new copilots using reusable components and a use-case factory approach.
Sustained adoption via training, champions, and continuous improvement loops.
Operational readiness with monitoring, support ownership, and governance policies.
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