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Artificial Intelligence Consulting & GenAI Enablement

Artificial Intelligence Consulting and GenAI Enablement
Artificial Intelligence Consulting and GenAI Enablement

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Artificial Intelligence Consulting & GenAI Enablement

Author:
Aykut Cakir · NMS Consulting

Published:
• Updated:

Quick Answer:
Start with a small set of high value use cases, add an internal platform for LLM apps, and govern with NIST AI RMF plus ISO 42001. Use RAG for trusted knowledge with provenance, run safety and security tests against OWASP LLM risks, and measure weekly impact while scaling.

Want a platform blueprint, value model, and a ninety day plan?
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Signals and Benchmarks

  • Generative AI could add about $2.6 trillion to $4.4 trillion in annual value across analyzed use cases. source
  • NIST AI Risk Management Framework 1.0 is the U.S. reference for trustworthy AI practices and risk control. source
  • ISO 42001 defines requirements for an AI management system that organizations can certify against. source
  • OWASP publishes the Top 10 risks for LLM applications, including prompt injection and insecure output handling. source
  • HELM and MLCommons benchmarks support capability and safety evaluation for foundation models. source source

Foundations: Architecture, Data, and Governance

  • Platform: internal developer platform for LLM apps with identity, logging, metrics, cost controls, and safe deployment paths.
  • Knowledge: retrieval-augmented generation to ground answers in your sources with citations. reference
  • Governance: adopt NIST AI RMF profiles for GenAI and align to ISO 42001 for audit-ready program management. reference reference
  • Security: threat model LLM apps and test against OWASP LLM Top 10 with guardrails for inputs, tools, data access, and outputs. reference
  • Evaluation: track task success, groundedness, toxicity, and privacy leakage with periodic HELM or MLCommons evaluations. reference reference

30-60-90 Enablement Playbook

First 30 Days: Use Cases, Baselines, and Guardrails

  • Select three use cases with clear value. Examples: sales assist, customer support assist, knowledge assistant.
  • Capture baselines for time saved, win rate, CSAT, first contact resolution, and ticket deflection.
  • Draft policies that map to NIST AI RMF functions and ISO 42001 controls. reference reference

Days 31 to 60: Platform Rails and Trusted Knowledge

  • Stand up identity, logging, monitoring, and cost budgets in CI/CD.
  • Implement RAG with semantic search, chunking, metadata, and source citations. reference
  • Harden prompts and tools against the OWASP LLM Top 10. Add output filters for PII and policy violations. reference

Days 61 to 90: Production and Scale

  • Ship the first assistants with rollback and runbooks. Track value weekly.
  • Run capability and safety evaluations and publish a model card summary. reference reference
  • Expand with a second golden path and self-service templates for new teams.

Use Case Types and Metrics

Use Case, Metric, and What To Track
Use Case Metric What To Track
Sales Copilot Win Rate and Cycle Time Lead response time, proposal quality, meeting notes to CRM, generated pipeline
Service Copilot Deflection and AHT Containment rate, CSAT, refund rate, policy compliance
Knowledge Assistant Time Saved Search to answer time, groundedness score, citation coverage
Developer Assistant Lead Time and Change Fail Rate PR size, review time, defects, incident restore time
FinOps Guardrails Unit Cost and Waste Idle resources, rightsizing, budget policy pass rate

Frequently Asked Questions

When Should We Use RAG Versus Fine-Tuning?

Use RAG when you need fresh, sourced answers from your content. Consider fine-tuning when you need domain tone or structured outputs from patterns that appear in your data. RAG plus light instruction tuning often delivers balanced results. reference

How Do We Govern GenAI?

Adopt NIST AI RMF profiles for GenAI, assign roles and risks, and align your controls to ISO 42001 so the program is audit-ready. reference reference

How Do We Secure LLM Applications?

Test against the OWASP LLM Top 10. Add prompt isolation, input and tool whitelists, output filtering, and least-privilege data access. reference

Want a Ninety Day GenAI Enablement Plan? We can deliver a platform blueprint, governance pack, and production path with clear roles and weekly value tracking.

Request a workshop

Sources

  • McKinsey. The Economic Potential of Generative AI. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
  • NIST. AI Risk Management Framework overview. https://www.nist.gov/itl/ai-risk-management-framework
  • NIST. Generative AI Profile (AI 600-1). https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
  • ISO. ISO/IEC 42001 Artificial Intelligence Management System. https://www.iso.org/standard/42001
  • OWASP. Top 10 for LLM Applications. https://genai.owasp.org/llm-top-10/
  • Stanford CRFM. HELM benchmark. https://crfm.stanford.edu/helm/latest/
  • MLCommons. AI Safety v0.5 Benchmark. https://mlcommons.org/2024/04/mlc-aisafety-v0-5-poc/
  • Lewis et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP. https://arxiv.org/abs/2005.11401




About the Author

Aykut Cakir

Aykut Cakir

Aykut Cakir, Senior Partner and Chief Executive Officer, has a demonstrated history in negotiations, business planning, business development. He has served as a Finance Director for gases & energy, pharmaceuticals, retail, FMCG, and automotive industries. He has collaborated closely with client leadership to co-create a customized operating model tailored to the unique needs of each project segment in the region. Aykut conducted workshops focused on developing effective communication strategies to ensure team alignment with new operating models and organizational changes. 

 

 

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