MarTech » Marketing artificial intelligence (AI) »
If you have an artificial intelligence program, you also have a committee, team, or body that is providing governance over AI development, deployment, and use. If you don’t, one needs to be created.
In my last article, I shared the key areas for applying AI and ML models in marketing and how those models can help you innovate and meet client demands. Here I look at marketing’s responsibility for AI governance.
AI governance is what we call the framework or process that manages your use of AI. The goal of any AI governance effort is simple — mitigate the risks attached to using AI. To do this, organizations must establish a process for assessing the risks of AI-driven algorithms and their ethical usage.
The stringency of the governance is highly dependent on industry. For example, deploying AI algorithms in a financial setting could have greater risks than deploying AI in manufacturing. The use of AI for assigning consumer credit scores needs more transparency and oversight than does an AI algorithm that distributes parts cost-effectively around a plant floor.
To manage risk effectively, an AI governance program should look at three aspects of AI-driven applications:
AI governance can be structured in various ways with approaches that vary from highly controlled to self-monitored, which is highly dependent on the industry as well as the corporate culture in which it resides.
To be able to direct to the model development as well as its validation and deployment, governance teams usually consist of both technical members who understand how the algorithms operate as well as leaders who understand why the models should work as they are planned. In addition, someone representing the internal audit function usually sits within the governance structure.
No matter how AI governance is structured, the primary objective should be a highly collaborative team to ensure that AI algorithms, the data used by them and the processes that use the outcomes are managed so that the organization is compliant with all internal and external regulations.
Here is a sample AI Governance design for an organization taking a centralized approach, common in highly regulated industries like healthcare, finance, and telecommunications:
There are several reasons for marketing to be involved in the governance of AI models. All of these reasons relate to marketing’s mission.
Dig deeper: AI and machine learning in marketing: Are you deploying the right models?
I would like to say that your organization’s AI Governance will welcome marketers to the table, but it never hurts to be prepared and to do your homework. Here are a few skills and capabilities to familiarize yourself with before getting started:
No matter what role you play in AI Governance, remember how important it is. Ensuring that AI/ML is deployed responsibly in your organization is not only imperative, but also an ongoing process, requiring persistence and vigilance, as the models continue to learn from the data they use.
Get MarTech! Daily. Free. In your inbox.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
New on MarTech
About The Author
Get the daily newsletter digital marketers rely on.
Start Discovering Now: Fall
Start Training Now: Master Classes
Start Discovering Now: Spring
Start Training Now:: SMX Advanced
November 14-15, 2022: SMX Next
March 8-9, 2022: Master Classes
5 Ways to Make Customer Experiences Drive Conversions & Revenue
Your Guide to Creating Consistent Experiences Across Multiple Websites
5 Ways to Improve your Content Workflows and Strategy in 2023
Enterprise Digital Events Platforms: A Marketer’s Guide
Enterprise Marketing Performance Management Platforms: A Marketer’s Guide
Enterprise Customer Journey Orchestration Platforms: A Marketer’s Guide
The Ultimate Reputation Management Guide for Financial Services
Receive daily marketing news & analysis.
© 2022 Third Door Media, Inc. All rights reserved.