GenAI Enablement

GenAI Strategy and Use Cases

GenAI Use Case Discovery: Identify high-value genAI opportunities across functions (service, sales, HR, finance, legal, operations) grounded in real workflows.
GenAI Use Case Prioritization: Score and sequence use cases by value, feasibility, risk, data readiness, and adoption complexity.
GenAI Value Hypotheses: Define clear hypotheses for impact (time savings, deflection, quality lift, conversion, risk reduction) with measurable baselines.
GenAI Roadmap Design: Build a phased roadmap that balances quick wins with foundational enablement (knowledge, evaluation, governance, operating model).

Content Inventory Mapping: Catalog priority content sources (policies, SOPs, tickets, product docs, contracts, wikis) and establish ownership.
Content Hygiene Program: Remove duplicates, resolve conflicts, deprecate outdated pages, and improve structure to increase retrieval quality.
Metadata and Taxonomy Design: Define tags, permissions, business context, and document types to improve relevance and enforce access controls.
Content Refresh Operations: Establish refresh cadence, recrawl triggers, and governance so knowledge stays current after go-live.

RAG Pattern Selection: Choose fit-for-purpose retrieval patterns (basic RAG, hierarchical retrieval, multi-query, tool-augmented, hybrid search).
Chunking Strategy: Define chunk size, overlap, structure-aware splitting, and citation mapping to optimize grounding and usability.
Embedding Strategy: Select embedding models, multilingual approach, vector store design, and re-embedding triggers.
Relevance Tuning: Tune ranking (hybrid lexical/vector), filters, re-rankers, and query transformations to reduce “misses” and noise.

Prompt Pattern Library: Build reusable patterns (summarize, extract, classify, draft, critique, compare) with guidance on when to use each.
System Prompt Design: Define role, tone, boundaries, and refusal behaviors aligned to brand and policy requirements.
Grounding and Citation UX: Design user experiences that show sources, confidence cues, and “why” explanations without overwhelming users.
Conversation Flow Design: Define clarifying questions, disambiguation behavior, and safe fallbacks for incomplete user inputs.

Hallucination Mitigation Controls: Implement grounding requirements, citation rules, refusal behaviors, and escalation to humans for uncertain answers.
Policy Constraint Enforcement: Encode constraints for sensitive topics, regulated advice, confidential content, and restricted actions.
PII and Sensitive Data Protection: Implement redaction, masking, access gating, and logging controls to reduce exposure and leakage risk.
Prompt Injection Defense: Design defenses against jailbreaks and injection (content boundaries, tool isolation, instruction hierarchy, sanitization).

GenAI Evaluation Framework: Define what “good” means by use case (accuracy, grounding, completeness, tone, safety, latency, cost).
Golden Test Set Creation: Build representative test sets, including edge cases, adversarial prompts, and compliance-sensitive scenarios.
Human Review Protocols: Establish rubric-based human evaluation with inter-rater calibration and sampling strategy.
Automated Regression Testing: Implement automated evals to detect quality drift after model, prompt, or knowledge updates.

Model Selection: Choose models by privacy, capability, latency, cost, and controllability; define a multi-model strategy where appropriate.
Tool Orchestration Design: Enable tool use (search, CRM, ERP, ticketing, workflows) with safe parameter passing and robust error handling.
Context Management Strategy: Define context windows, summarization, memory rules, and retrieval fallback strategies.
Cost Controls: Implement cost monitoring, token budgeting, caching, and routing rules to manage inference spend.

Embed in Daily Tools: Integrate genAI into existing tools (Teams/Slack, CRM, ITSM, email, intranet) to reduce adoption friction.
Human-in-the-Loop Design: Define review steps, approvals, and exception handling where outputs affect customers, finance, or compliance.
Process Redesign Enablement: Redesign workflows to capture benefits (not “old process with a chatbot bolted on”).
Operational Readiness: Define support processes, escalation paths, and incident response for genAI-enabled workflows.

Release Management: Establish release processes for prompt changes, knowledge updates, model upgrades, and feature flags.
Monitoring and Observability: Monitor quality, safety events, latency, usage, cost, and retrieval performance with actionable alerting.
Feedback Loop Design: Capture user feedback, corrections, and failure modes and convert them into backlog and retraining needs.
Incident Response Playbooks: Build playbooks for leakage events, harmful outputs, system outages, and model/provider issues.

User Enablement Program: Create role-based training for effective prompting, verification habits, and safe usage expectations.
Communications Toolkit: Provide launch messaging, FAQs, and leadership narratives to drive trust and usage.
Adoption Metrics: Track adoption by role and workflow (active users, frequency, task completion, deflection, time saved).
Continuous Improvement Governance: Define cadence and ownership for improving prompts, knowledge, evaluation sets, and integrations post-launch.