AI Best Practices for Business Decision Makers and Practitioners

With companies in every industry leveraging artificial intelligence (AI), or at least wanting to, it has never been more important for technical practitioners and non-technical decision makers to understand how AI can benefit their business as well as the associated risks of implementing AI. It is critical for key stakeholders to articulate the business value of utilizing AI for solving their business problems, and to understand the associated costs and benefits of deploying AI-infused applications, the time to value of implementing AI and what success will look like over time.

In this guide, the CompTIA Artificial Intelligence Advisory Council leverages the diverse and expansive domain expertise of its members to provide curated insights on pain points and best practices associated with infusing AI solutions within an organization. To provide effective guidance, the information presented here is organized into best practices for two personas that are frequently involved in the adoption and integration of AI technologies: the practitioner and the decision maker.

Part 1: Decision Makers

Decision makers are focused on the macro-level goals and challenges of the business. They explore ways modern technologies can assist them in addressing current and future challenges in the most cost-effective manner that offers a generous ROI. They have job titles such as founder, CXO, CRO, line of business (LOB) leader, chief data officer, vice president of business intelligence, or vice president/director of engineering/IT. They are tasked with performing cost-benefit analyses and data visualizations that allow non-technical stakeholders to understand the business case for implementing AI solutions. They are also keen to ensure compliance and data standards utilized across the business.

This guide outlines pain points and best practices for decision makers, including:

• Avoid Shiny Object Syndrome and Focus on Business Use Cases
• It’s All About the Data and Modeling
• Budgeting, Proof of Concept, and Rollout
• Workforce Skills Upgrade and Hiring
• Selecting the Right Implementation Partner

Part 2: Practitioners

Practitioners work with data and technology and support decision makers’ strategic goals to achieve desired business outcomes. They have job titles such as data scientist, data engineer, data analyst, or machine learning architect. Practitioners are concerned with the theory, mechanics, and practical applications behind the underlying algorithmic and mathematical approaches. They translate a business problem into a mathematical problem that can take data as inputs and produce outputs in the form of actionable insights. They work closely with IT to build the tools and infrastructure needed to deliver scalable software solutions and data-driven optimizations.

In this section of the guide, you’ll learn:

• Common AI Practitioner Personas
• The AI Lifecycle from Practitioner Standpoint

How to Use this Guide

While every company and use case is different, these principles outlined in AI Best Practices for Business Decision Makers and Practitioners should maximize the efficiency and efficacy of your AI initiatives. As modern technologies and applications are researched and developed, pain points may dissolve while best practices continue to evolve. This guide reflects the current understanding and landscape of available AI approaches and should be interpreted within that context.

Additional AI Resources

Artificial Intelligence (AI) Terminology: A Glossary for Beginners

Business Considerations Before Implementing AI

Emerging Business Opportunities in AI

How to Leverage AI to Get the Most from Your Company’s Data

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