Discovery, audit and delivery

Jason Reynolds

Chief Operating Officer, Ovidius AI

Jason runs the discovery call and scopes every build. He leads the AI Audit, which ends in a 90-day roadmap you own, then gets each system into production and keeps it running.

Read Jason's articles Jason on LinkedIn

Jason Reynolds, Chief Operating Officer of Ovidius AI

What Jason writes about

Jason writes about what a use case needs before it reaches production: readiness and maturity checks, why pilots stall, the people side of adoption, the safety training each role needs, the data governance and governance tools an enterprise needs once agents read its data, and 25 enterprise use cases by department with what each takes to ship.

The work behind them

AI Audit AI readiness assessment AI managed services AI governance Corporate AI training

Background: New product development and digital transformation across business and go-to-market strategy, information systems, agency operations and product development.
Experience: 20 years in product development and digital transformation

Articles by Jason

Four controls for agents that read company data: the asker's permissions, access checked before retrieval (highlighted), a log of what every answer read, and actions decided one by one.

Data Governance for AI: What Changes When Agents Read Your Data

Agents read on someone else's behalf and can be steered by what they read. Whose permissions they use, where access is checked, what to log per answer, and a launch checklist.

Gartner survey of more than 2,000 CIOs: 71% of organizations expect to deploy agentic AI within 12 months, only 21% have agentic AI governance in place, highlighted.

AI Governance Tools Compared: Which Ones Fit Your Enterprise Stack?

AI governance tools sorted into five jobs, what each vendor documents it does, where each one sits in an agent request, and how to choose for the stack you already run.

Volume, minutes per item and loaded cost feed an orange baseline set before kickoff, followed by the pass mark, adoption and quality, and return checkpoints.

How to Measure AI ROI Before and After Launch

The AI ROI formula, the baseline to record before kickoff, the costs estimates leave out, the metrics that show return after launch, and a calculator that sets the plan against results.

McKinsey's 2026 state of AI survey: 80% say AI improved their own productivity, while only 37%, highlighted, report any positive EBIT contribution from AI.

Organizational AI Readiness: The People Side Nobody Plans For

What organizational AI readiness covers beyond the tech: role maps, a named owner, training on real tasks, a straight answer on jobs, and an eight-point people check.

The five stages of AI maturity Ovidius uses, Awareness, Active, Operational, Systemic and Transformational, with stage 3 Operational highlighted: AI runs inside live workflows, with an owner for each.

AI Maturity Assessment: Where Your Company Sits on Five Levels

The five stages of AI maturity, a self-assessment across data, systems, workflows, people and governance, what MIT research found maturity is worth, and how to move up a level.

20 readiness questions in five areas of four: data, systems, workflow, people and governance.

AI Readiness Checklist: 20 Questions Before Your First Agent

Twenty concrete questions to answer before your first AI agent, a checklist that scores your answers, the four gate questions that block a launch, and where to go from your score.

Four controls applied at retrieval, current versions only, approved documents only, chat left out when a policy answers, and permissions checked before retrieval, so one of five matching passages reaches the agent.

Enterprise Knowledge Management When AI Agents Are the Readers

What changes when AI agents read your knowledge base, the five ways it misleads them, how to make documents machine-readable, and how to keep answers current as policies change.

MIT NANDA's 2025 GenAI Divide funnel: 60% of organizations evaluated enterprise AI tools, 20% reached a pilot and 5%, highlighted, reached production.

From AI Proof of Concept to Production: Why Most Pilots Stall

What the MIT 95% figure actually measured, the six gaps between an AI demo and production, a readiness check for your pilot, and how to end it with a scale, pivot or stop decision.

Five stages of AI adoption, experiment, prove, platform, redesign and scale, with stage 3, the platform, highlighted.

The 5-stage AI adoption framework: how enterprise teams scale from pilots to production

The five stages of enterprise AI adoption, what each one looks like, the gate that ends it, and a ten-question check that tells you which stage your company is in today.

Employees ask in Slack or Microsoft Teams; one agent workflow handles password resets, access requests and HR policy questions for the IT and HR desks.

Internal Chatbots for IT and HR Service Desks: What to Automate and What to Route

Which IT and HR requests an internal chatbot can close in Slack or Teams, which must reach a person, why identity checks come first, and what to measure after launch.

Four stacked requirements every AI use case needs before it ships: data to test on, live system access, a named human checkpoint highlighted in orange, and someone who runs it.

25 Enterprise AI Use Cases by Department, and What Each Takes to Ship

25 use cases across support, IT, marketing, finance and HR, each with what the agent does and what it needs to ship, plus four tests for choosing which one goes first.

A large orange 3 beside the three training tracks that share one policy: business teams, builders and leaders.

AI Safety Training for Enterprise Teams: What Each Role Needs

One policy, taught three ways: what business teams, builders and leaders each need, mapped to NIST and OWASP, plus the guardrail layers that catch what training misses.

More from the Ovidius partners

Each partner writes about the stage of the work they own on client builds. Meet the team

Owen BoesveldChief Executive OfficerArticles and background

Maciej KaczorChief Technology OfficerArticles and background

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben