High ROI Data Science

High ROI Data Science

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High ROI Data Science
High ROI Data Science
AI Strategy KPI Library

AI Strategy KPI Library

This is a living document with a comprehensive list of AI Strategy KPIs. It will be updated regularly.

Vin Vashishta's avatar
Vin Vashishta
Jan 04, 2022
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High ROI Data Science
High ROI Data Science
AI Strategy KPI Library
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This is currently the largest list of AI Strategy KPIs available. Most Big 5 Consulting Companies consider these trade secrets. In my opinion, they must be more accessible.

Remember, if you’re a paid subscriber, you can comment (or reply to emails) and ask questions. I’ll write up posts in the near future based on your needs.

I am also adding lectures on AI Strategy KPIs to my Business Strategy For Data Scientists class. They will provide a deeper dive into each KPI with more background on how I’ve applied them. As a subscriber, you get 15% off with the code SUBSTACK15.

KPI Categories

  • Data Curation

  • Research

  • Model Development

  • Machine Learning Platform

  • Business Integration

  • Literacy

  • Organization and Talent

  • Machine Learning Product

Purpose

Senior leadership needs visibility into the progress, costs, and returns of AI across the business. AI initiatives include:

  • Product and Platform Development

  • Research and Artifact Creation

  • Internal Efficiency and Process Automation Projects

  • Data and Analytics Organization Ramp Up

  • User Adoption

  • User, Decision Maker, and Stakeholder Education

  • Infrastructure Consolidation, Purchases, and Buildout

  • Data Science Lifecycle Improvement and Automation

  • Data Quality

  • AI Strategy Implementation

Everything with a budget, cost savings, or revenue needs KPIs.

This library is a comprehensive list of KPIs, and it is typically unnecessary to implement all of them. Keep the KPIs relevant to the highest priority projects. Use them intentionally to create awareness about the critical contributors to Machine Learning's success.

Before you implement a KPI, ask the team:

  • Does this KPI provide information which will lead to improved decision and outcome quality?

  • Does a change in this KPI accurately indicate progress towards an AI Strategy goal?

  • Is this KPI at the right level of granularity for the leaders it is presented to?

  • Is this KPI going to conflict or contradict another organization’s KPIs?

Each KPI needs a statement of purpose to explain what "better" looks like or how the KPI will change as the strategic goal is met. It will define the starting point and target. There needs to be a connection to tangible value that answers the question, "Why is this KPI important?"

KPIs

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