Re:Engage

MARKETING STRATEGY:

Why You Need an AI Operating Model

by Marie Aiello

Recent Posts

For most organizations, the question is no longer whether to use AI. Instead, it’s how to operationalize it in a way that strengthens, not complicates, the marketing function. An AI Center of Excellence (CoE) provides a clear path forward by establishing the frameworks, processes, and cultural foundations needed to turn AI from a promising experiment into a durable, beneficial capability

Why an AI Center of Excellence Matters Now

Most marketing teams are still in the early stages of AI maturity. Tools may be used inconsistently across teams, prompts are often unstructured, and model outputs vary widely. The rapid evolution of LLMs compounds this challenge; what may work one month can break the next.

An AI CoE addresses these pain points by serving as the backbone of responsible and scalable adoption. It provides:

  • Standardized prompt engineering frameworks
  • Clear guidance on model selection and training
  • Best practices for accuracy, compliance, and brand safety
  • Governance and risk mitigation procedures
  • A defined testing environment for new use cases

Without these guardrails, AI adoption becomes fragmented, inconsistent, and in some cases, risky. With them, organizations can accelerate experimentation while ensuring alignment and Quality.

The Role of  Specialized Agency Partners

A common misconception is that building AI capabilities means doing everything internally. In reality, many organizations benefit from partnering with boutique agencies that specialize in prompt engineering, model behavior, workflow automation, and rapid experimentation.

These partners often bring:

  • Deep awareness of how different models behave and evolve
  • Expertise in building reusable prompt libraries
  • Knowledge of retrieval-augmented generation (RAG) and embedding approaches
  • The ability to set up early testing environments and frameworks
  • Quicker iteration cycles compared to traditional consultancies

Because AI changes so quickly, external specialists help organizations shorten the learning curve, avoid missteps, and upskill internal teams more efficiently. With AI model lifecycles of around six months before obsolescence, successful organizations have partners that are immediately testing and identifying changes in model capabilities. They complement, rather than replace, the CoE by supporting the capabilities that take longer to build in-house.

Centralizing Knowledge Through AI “Braintrees”

As AI becomes integrated across marketing, the need for a unified knowledge base becomes critical. Many teams are still slowed down by siloed documents, outdated briefs, scattered research, and inconsistent messaging frameworks. AI-driven knowledge tools such as Notebook LM allow organizations to create what can be thought of as “braintrees” — centralized collections of information that are searchable, explainable, and continuously updated.

These braintrees often contain:

  • Brand guidelines and tone of voice
  • Market research and competitive intelligence
  • Creative frameworks and messaging hierarchies
  • Campaign results, customer insights, and performance data
  • Process documentation and training materials

By consolidating institutional knowledge, marketers can reduce time spent searching for answers, onboard new talent more quickly, and accelerate decision-making with more complete context. These systems ensure AI tools are grounded in real brand and business data, not generic information from across the internet.

Choosing Configurable Platforms Over Bespoke Systems

In the rush to adopt AI, many enterprise vendors have introduced proprietary solutions. While appealing at first glance, these closed systems often lock organizations into a single ecosystem, require continuous custom development, and fail to scale as needs evolve. They can quickly become time and resource intensive as teams need to dedicate energy to keeping them updated and relevant.

A more future-proof approach is to use established AI platforms that are configurable rather than custom-built. These platforms provide a solid, continuously updated foundation while allowing brands to layer in their unique identity.

Configurable platforms allow teams to incorporate:

  • Brand tone, language patterns, and creative preferences
  • Audience segmentation and messaging triggers
  • Visual guidelines and design principles
  • Historical performance insights and predictive signals

The result is a system that generates on-brand, relevant, data-informed outputs without requiring costly bespoke engineering or creating long-term vendor dependency. This approach ensures flexibility, interoperability, and resilience as the AI landscape evolves.

Agentic AI and the Shift in Marketing Workflows

One of the most transformative opportunities for marketers lies in agentic AI, systems capable of taking meaningful actions, not just generating text. These capabilities streamline repetitive workflows such as:

  • Drafting briefs and summaries
  • Completing intake or request forms
  • Organizing and tagging assets
  • Updating trackers and reporting templates
  • Performing QA steps
  • Routing tasks across teams

By reducing manual, repetitive work, teams can focus on higher-value functions: strategy, creative exploration, audience insights, and the orchestration of complex campaigns. Agentic AI does not replace marketers. Instead, it frees them to spend more time where thinking, analysis, and creativity matter most.

The Human Role and the Critical Need for Change Management

AI elevates marketers, but it also changes expectations and workflows. For an AI CoE to succeed, organizations must treat AI adoption as a change management effort, not just a technology rollout. AI reshapes countless aspects of how we work, including how work is initiated and reviewed, the skills teams must develop, and how decisions are made and validated. Additionally, across the team, it impacts how colleagues collaborate with each other and where humans vs. automation and technology contribute value.

Without a structured plan for communication, training, role evolution, and cultural readiness, even the most well-designed CoE risks becoming a silo.

Organizations that succeed typically invest in:

  1. Clear governance and role definition
  2. Ongoing skills training and AI literacy programs
  3. Transparent communication around changes and expectations
  4. A culture that embraces experimentation and iteration
  5. Strategic human oversight to guide AI output

When organizations pair strong change management with technical capability, AI accelerates growth rather than disrupting progress. Those who invest in both will define the next era of marketing.