What’s interesting is that very few of those conversations are about whether AI works anymore. They’re about how to operationalize it across an organization.

I see a similar pattern playing out across industries. Many organizations have invested heavily in AI to accelerate content creation, for example, yet campaign timelines continue to lengthen because the underlying operating model remains unchanged.

AI has dramatically improved productivity by automating repetitive work and accelerating content creation. Teams are producing more work in less time. But these activities were never really the limiting factor. Content creation was never the slow part of delivering campaigns.

The real delay occurs after the content is created due to broken workflows. Approvals, cross-functional handoffs, compliance reviews, and vendor coordination combine to slow the process of delivering the end product. As AI increases the volume of work entering the pipeline, organizations often struggle to move that work efficiently to completion. The result is a faster front end feeding an increasingly congested pipeline, slowing anything coming out the back end.

The findings of the Typeface Signal Report: The AI Speed Paradox clearly illustrate the scale of the challenge. Ninety-two percent of marketing leaders report that campaigns require 10 or more stakeholders, while 44% involve 20 or more participants. More than half rely on at least nine vendors and tools to complete a single campaign, and 88% say C-suite approval bottlenecks delay launches.

Rather than accelerating execution, this growing network of stakeholders and disconnected AI tools is increasing operational complexity and extending delivery timelines. Although many organizations are experimenting with AI agents, only 16% report being prepared to operate at AI speed. Only 20% have AI-ready workflows.

Campaign timelines reflect this slowdown in delivery. Only half of respondents now consider one to two weeks an acceptable delivery window, down from 85% in the 2025 survey. Two in five organizations now expect campaigns to take three to four weeks, while 34% require one to two months — up dramatically from just 5% a year earlier.

The underlying issue is architectural hurdles, not the speed of the content-generating AI technology. In my experience, the organizations moving fastest aren’t making faster technology decisions — they’re making faster organizational decisions, with marketing, IT, legal, procurement, and executive sponsors aligned around a common operating model.

Most enterprise AI deployments remain collections of disconnected point solutions with little orchestration across systems. Without an integrated operating model, organizations struggle to move beyond isolated pilots and achieve measurable enterprise value.

I also see many organizations begin by asking whether they should build AI internally. But as they evaluate what’s required — governance, security, integrations, workflows, and enterprise scale —they quickly realize they’re solving a much larger operational challenge than simply deploying a model. The question shifts from ‘Can we build AI?’ to ‘How do we operationalize AI across the enterprise?’

The financial implications are significant. Longer delivery cycles and expanding AI technology stacks are increasing costs while making it harder for organizations to realize meaningful returns. 

Rather than deploying additional AI tools or producing more AI-generated content, the organizations seeing the greatest returns are redesigning workflows, governance, systems, and human decision-making into a coordinated operating model. They’re not simply generating more content — they’re removing friction from the decisions that happen after content is created. AI orchestration provides the coordination layer that connects brand intelligence, governed AI agents, and enterprise systems into a unified workflow.

For example, AI-generated content can be on-brand, compliant, and personalized according to predefined rules. Humans set the strategy and creative direction while governance is built directly into the workflow—eliminating unnecessary review cycles without sacrificing brand consistency or compliance. Content moves through the pipeline faster, making speed and governance complementary rather than competing priorities.

To maximize the return on AI investments, marketing leaders should focus on four strategic priorities:

• Align executive sponsorship early. The strongest AI transformations aren’t just supported by executive sponsors — they’re driven by active relationships between executive leaders on both the customer and technology-partner sides, helping to remove barriers long before they become deployment issues.
• Redesign workflows end to end. Automating inefficient processes is paving the cow path. Sustainable productivity gains require reengineering workflows before automating them.
• Embed governance into everyday operations. Integrate policies, security, compliance, and accountability directly into workflows to reduce approval delays and minimize costly rework.
• Measure strategic outcomes — not content volume. Success should be defined by increased organizational capacity, faster execution, reduced bottlenecks, and stronger business performance rather than the quantity and velocity of AI-generated content.

The solution to the AI speed paradox isn’t deploying more AI tools to deliver more and more content. Organizations will realize the greatest value when they redesign the architecture that governs how work flows across the enterprise. The companies that pull ahead won’t be the ones generating the most AI content — they’ll be the ones that remove the most organizational friction.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.