Jovis AI
How to evaluate an AI agent for business data
Build a task-specific test set and production acceptance scorecard for correctness, grounding, permissions, usefulness, and failure recovery.
Buyer journey
Implement and evaluate an AI agent
Choose a narrow business job, select an approach, evaluate real tasks, and decide whether an agent is ready to operate.
- For
- Product, engineering, data, and operations leaders moving from an AI-agent idea to a governed pilot.
- Problem
- Teams choose architecture or tooling before defining the job, evidence standard, ownership, and scale-or-stop decision.
- Outcome
- A practical path from use-case selection through evaluation, adoption, and operating value.
- Includes
- Build-versus-buy, architecture, evaluation, adoption, operating models, and ROI decisions.
How to build an AI agent your team will actually use
A practical six-step guide to choosing one business job, defining approved context, testing the agent, and making it part of an existing workflow.
Build vs. buy AI agents: a business data decision framework
Decide which parts of an enterprise AI-agent system to build, buy, or combine using business, data, governance, and lifecycle criteria.
AI agent RFP: 15 questions executives should ask before selecting a vendor
Use an executive AI-agent RFP to compare real workflow fit, data access, human controls, and operating accountability—not just a polished demo.
AI capital allocation: a controlled workflow for executive teams
Use AI to prepare capital-allocation choices from approved business evidence while executives retain investment authority, assumptions, and accountability.
AI succession planning: a controlled workflow for executive teams
Use AI to prepare a confidential, evidence-backed succession review while the board and executive team retain talent judgment, decision rights, and accountability.
AI workforce planning: a controlled scenario workflow for executives
Use AI to prepare workforce-capacity scenarios from approved business evidence while executives retain staffing judgment, employment decisions, and accountability.
AI M&A due diligence: a controlled evidence workflow for executives
Use AI to prepare an evidence-backed M&A diligence review that surfaces open questions, preserves decision rights, and keeps deal judgment with accountable leaders.
AI strategic planning: a controlled evidence workflow for executives
Use AI to prepare strategic planning choices from approved business evidence while executives retain judgment, decision rights, and accountability.
AI Chief of Staff for executives: what to govern before you delegate
A practical framework for executive teams designing an AI Chief of Staff that improves context and follow-through without blurring authority, privacy, or accountability.
AI agent orchestration in the enterprise: a practical architecture guide
Choose between deterministic workflows, single agents, and multi-agent orchestration by matching coordination complexity to a real business job.
How to run a 30-day AI agent pilot without losing control
A practical 30-day plan for testing one AI agent on a real business workflow with clear scope, shadow mode, human review, and a go-or-stop decision.
How to measure AI agent ROI: a practical framework
Build a defensible AI-agent business case using a real baseline, quality-adjusted outcomes, full operating cost, and clear scale-or-stop gates.
What is agentic BI? A practical business intelligence guide
A practical guide to agentic BI, how it differs from dashboards and copilots, and what teams need before adopting it.
How to put AI into your operating model
Define where AI belongs in recurring work, who owns it, which data it can use, and how teams review its answers before scaling adoption.