Forrester Reports Marketing Leaders Face AI Scaling Challenges
Marketing leaders must balance AI-driven productivity with quality control, according to Forrester. They face challenges in scaling AI workflows while maintaining accountability.
Original reporting · forrester.com
Original: “Marketing’s Next AI Challenge: Scale What Works Without Losing Control”
Read “Marketing’s Next AI Challenge: Scale What Works Without Losing Control” at Forrester →
How this was written. ABM Fuel wrote this article with a language model from Forrester’s reporting (model: @cf/meta/llama-3.3-70b-instruct-fp8-fast); it describes that reporting rather than adding to it. The original, linked above, is the full story.
Key points from Forrester’s report
- Marketing leaders face challenges in scaling AI workflows while maintaining quality control and accountability.
- Decentralizing problem-solving and standardizing certain aspects can help achieve this goal.
- Governance is crucial in evaluating the risk, reach, and reliance of AI workflows.
Marketing leaders are shifting their focus from individual AI productivity to building repeatable workflows and developing AI agents, Forrester reported. This change requires deliberate choices about what teams can build independently and what should be standardized.
Most organizations have invested in AI tools and training, but these investments do not automatically change how marketing operates, according to Forrester. Marketing leaders must help their organizations move from AI literacy to building workflows.
Decentralizing problem-solving and standardizing certain aspects, such as approved messaging and brand guidelines, can help marketing leaders achieve this goal. Teams can experiment and build workflows that address their specific needs, but they should not independently recreate the foundations that all marketing workflows depend on.
Governance is also crucial, as marketing leaders must evaluate the risk, reach, and reliance of AI workflows to determine the required level of oversight. This includes considering what could go wrong if a workflow produces an inaccurate result, how broadly its outputs affect the organization, and how much other people or processes depend on it.
Content work is an area where these operating model decisions matter, as AI makes content faster and cheaper to produce. Leaders must rethink where human expertise adds the most value and who is accountable for content quality and performance.
Marketing leaders must protect the work that makes content worth creating, such as developing original inputs that AI cannot independently produce. They must also treat content as an investment that requires ongoing care, including sourcing expertise, verifying quality, and improving content over time.
Questions and answers
What is the next phase of marketing AI transformation?
The next phase will depend on deliberate choices about what teams can build independently and what should be standardized.
How can marketing leaders balance AI-driven productivity with quality control?
They can decentralize problem-solving, standardize certain aspects, and evaluate the risk, reach, and reliance of AI workflows.
What role does human expertise play in content creation with AI?
Human expertise is essential in developing original inputs that AI cannot independently produce, such as distinctive expert perspectives and proprietary research.
