At Lumina, we guide companies on digital acceleration journeys that combine data, AI, and technology to generate real results. This guide was written for executives and teams who need to transform Generative AI into business value — with security, governance, and cost control.
What is Generative AI and why does it matter now
Generative AI (GenAI) is the capability of models to create content, answer questions, and support decisions at scale. The differentiator for 2025 is the maturity of use cases: we're moving beyond the hype and entering a pragmatic, ROI-driven cycle. At Lumini, we prioritize implementations with reliable data, clear governance, and measurable business objectives—starting small, proving value, and scaling with confidence.
Use cases by area (with KPI ideas)
Marketing and Sales
- Content and campaign creation assistants; real-time recommendations; prospecting scripts.
- KPIs: conversion rate, proposal velocity, CAC, incremental revenue.
Customer Service and Operations
- Chatbots and agents with RAG connected to your knowledge; autonomous triaging and resolution; living knowledge base.
- KPIs: FCR (First Contact Resolution), AHT (Average Handle Time), CSAT (Customer Satisfaction), Call Deflection, Cost Per Contact.
- Discover our automation platform with user experience monitoring: Austin – RPA + UX
IT and Development
- Code generation, review, and testing; log analysis; backlog acceleration.
- KPIs: lead time, deployment frequency, production defects, team productivity.
- See our offer of Application Development
HR and Internal Communication
- Onboarding, knowledge management, AI-assisted training.
- KPIs: ramp-up time, engagement, employee satisfaction.
- Learn about our solution for Digital Onboarding
Finance and Risk
- Reporting and reconciliations; fraud detection; demand and revenue forecasting.
- KPIs: avoided losses, forecast accuracy, closing time.
- For data and AI services, access Data Science & AI
90-Day Roadmap to Get the Project Off the Ground
- Weeks 1–2 — Discovery and Prioritization
- Align business objectives and map cases by value × complexity matrix.
- Define KPI baselines and success criteria.
Weeks 3–4 — Data and RAG
- Securely connect repositories, organize catalogs, and manage access policies.
- Prepare a semantic base (vector store) and sources of truth.
Weeks 5–6 — Operational MVP
- Deliver an end-to-end flow with quality and cost metrics.
- Structure hallucination mitigation mechanisms (grounding and fallback routes).
Weeks 7–9 — Observability and Cost
- Monitor quality, latency, and cost per interaction.
- Apply cost reduction tactics (caching, prompt compression, model routing).
Weeks 10–12 — Controlled go-live and scale-up plan
- Execute pilots in real-world environments; train teams; document processes and risks.
- Build the operational runbook and the evolution backlog.
GenAI Assessment (2 weeks)
Data diagnosis, case prioritization, and rapid MVP.
Reference Architecture (RAG-first, Guardrails, and MLOps)
- LLM + RAG: prioritize connecting the model to your data to reduce hallucinations and protect sensitive knowledge.
- Data Layer: Lakehouse, Catalogs, Quality, and Lineage.
- Security and Privacy: Access Policies, Masking/Anonymization, Logging, and Auditing.
- Guardrails: prompt filters, factual checks, and risk classification.
- Observability: monitor quality, cost, and latency from the first MVP.
- MLOps/DevOps: prompt and evaluation versioning, CI/CD, safe rollback.
- To accelerate this base, check out our offer of Data Science & AI
Governance, LGPD, and compliance
- Privacy by design: minimize personal data, establish legal basis and retention policies.
- Model governance: define responsibilities, approvals, and audit trails.
- Applied Security: Data Leak Prevention, Threat Protection, and Continuous Monitoring.
- View our services Information Security
Costs and FinOps for GenAI: How to Control TCO
- Cost drivers: tokens, context, latency, call concurrency, reprocessing.
- Tactics: caching, response truncation, model routing, rate limits, and budgets.
- Cost KPIs: cost per interaction, cost per resolution, cost per value unit (e.g., qualified lead).
- Explore our offer of FinOps
- Pair with 24×7 Managed IT Services
PoC RAG (14 days)
Connect your knowledge bases and reduce hallucinations with RAG-first.
Common mistakes (and how to avoid them)
- Agent washing: Renaming simple automations as “agents” without a clear business objective. Start with well-defined and measurable tasks.
- Ignoring data and governance: without data quality and policies, risk increases and value is lost.
- Scaling without observability: without quality and cost metrics, the project surprises in month 2.
- Underestimating change management: Prepare teams, communicate the benefits, and establish new work routines.
KPIs that matter
- Value: Incremental revenue; profit margin; upsells/cross-sells; qualified leads.
- Efficiency: AHT; FCR; cycle time; team productivity; production defects.
- Risk and Compliance: Prevented incidents; violations; policy alerts; audit trails.
- Cost: Cost per interaction; cost per resolution; cost per asset/artifact generated.
Examples and References from Lumini
- Automation with UX monitoring (Autin) reduces response time and provides end-to-end visibility: RPA + UX Case Study in Tourism
- Logistics Optimization with Data Science: Reduction of up to 22% in distance traveled: Route Optimization Case Study
- Vertical markets Insurance e Education
Next Steps
- Take a quick GenAI assessment with our team of Data Science & AI
- Evaluate a RAG proof of concept by connecting your knowledge bases.
- Establish governance and security using best practices: Information Security
- Plan for costs and scalability with FinOps and, if necessary, Managed IT Services.
Talk to Lumini
Want to understand how Generative AI could impact your business over the next 90 days?
Lumini IT Solutions — Innovation to transform businesses through data, AI, and technology.
