Marketers discuss using AI beyond content creation

December 9, 2024

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Many Opportunities Await Marketers with AI

Many marketers are familiar with using generative AI for its content-creation capabilities, but AI presents opportunities to help marketers natively within applications they use in their martech stack every day.

Beyond generative AI, opportunities await marketers with machine learning and other forms of AI. What keeps many organizations from moving forward with these initiatives are obstacles around team training and data governance.

Other areas of the organization, including security, legal, and compliance will also present challenges to adoption of these new technologies.

This discussion from the Fall 2024 MarTech Conference was led by Craig Schinn, Co-founder and COO, Actable, and Michelle Simone, Principal Consultant, Pepper Foster Consulting.

Topics Covered in the Discussion:

Examples of AI use cases, in customer service and HR.

Why is there a lack of adoption of AI and machine learning at the enterprise level?

AI features in martech applications.

How to think about the governance of AI in your organization.

Is generative AI over-shadowing machine learning and other forms of AI?

Obstacles to AI adoption (and potential solutions).

Primary concerns about deploying AI.

AI-powered applications marketers in the discussion are using.

Use cases for custom GPTs.

Dig deeper: AI in marketing: Examples to help your team today

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About the author

Mike Pastore has spent nearly three decades in B2B marketing, as an editor, writer, and marketer. He first wrote about marketing in 1998 for internet.com (later Jupitermedia). He then worked with marketers at some of the best-known brands in B2B tech creating content for marketing campaigns at both Jupitermedia and QuinStreet. Prior to joining Third Door Media as the Editorial Director of the MarTech website, he led demand generation at B2B media company TechnologyAdvice.

Frequently Asked Questions

1. What are some examples of AI use cases in customer service and HR?

AI can be used in customer service for chatbots and automated responses, while in HR, AI can assist in recruitment, onboarding, and employee engagement.

2. Why is there a lack of adoption of AI and machine learning at the enterprise level?

Challenges such as team training, data governance, and concerns around security, legal, and compliance hinder the widespread adoption of AI and machine learning in enterprises.

3. How can organizations ensure proper governance of AI?

Organizations can establish clear policies, protocols, and oversight mechanisms for the use of AI to ensure ethical and responsible implementation.

4. Is generative AI overshadowing other forms of AI like machine learning?

Generative AI has gained significant attention for its content creation abilities, but machine learning and other forms of AI have unique applications and remain essential in various marketing functions.

5. What are some common obstacles to AI adoption and their potential solutions?

Obstacles to AI adoption include lack of expertise, data quality issues, and resistance to change. Solutions involve investing in training, improving data management practices, and fostering a culture of innovation.