My team wants to use AI. But where do I start?

Most organizations do not know which workflows are ready, whether their data can support AI, or which tools fit the work. I help teams figure that out before they invest time and money in the wrong solution.

Scenario 1: Is my data ready for AI?

AI cannot reliably use data that is duplicated, inconsistent, outdated, or stored in different formats. I help teams identify where data needs to be cleaned, matched, normalized, and standardized before it is used in an AI workflow.

Scenario 2: Which team should start using AI first? Are my employees ready?

Not every task needs AI. I help teams find the workflows where AI could provide real value, identify the risks and dependencies, and choose a practical place to start. I also consider the very real human impact of introducing this ‘new employee’ and the dangers of AI slop on a group dynamic.

Scenario 3: How do I choose what AI model or agent to build?

A team may know it wants to use AI, but not whether Copilot, Claude, or another tool is the best fit. I help teams compare options, design a small test, and follow practical best practices when building Copilot agents and multi-agent workflows.

What I Help With

• AI readiness interviews

• Workflow selection and prioritization

• Data cleaning and normalization requirements

• Copilot and Claude use cases

• Copilot agent instructions and testing

• Source, access, and human-review decisions

• Multi-agent workflow design

• Failure testing and improvement recommendations

What You Receive

• A recommended starting workflow

• A data-readiness checklist

• Tool recommendations

• A small test plan

• Findings and next steps

• Practical guidance for building safely and reliably

About My Work

I help nontechnical teams translate messy business processes into AI workflows that can actually be tested and used. My focus is not just the prompt. I look at the data, the people, the workflow, the tool, and the decisions that still require human judgment.

Choosing the AI model is the fun part. The real work is preparing your data, workflows, and people to use it well.

Problem: A company wanted several AI agents to gather information from different sources and create an executive brief.

What I did: Tested source restrictions, matching, citations, output consistency, missing fields and human-review points.

What I found: More instructions did not always improve performance; data-source problems could look like AI failures; source-specific agents needed different guardrails; human verification was still necessary.

Result: Clearer instructions, defined failure handling and a more testable workflow.