Your Guide to Implementing a Generative AI Strategy

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The Gist

  • Apply three models when needed most. Each of the generative AI deployment models — centralized, decentralized and open — offers unique benefits. Their combined use allows for flexible, efficient integration.
  • Embrace the “Hack, Pack, and Stack” mindset. Empower your teams to integrate generative AI by balancing experimentation and exploitation while ensuring scalable integration within your tech stack.
  • Customer-centric transformation. Outperformers adapt all three generative AI models to align with customer needs, ensuring integration enhances experiences and efficiency, rather than just following organizational preferences.

As generative AI continues to revolutionize the marketing landscape, organizations face the challenge of integrating generative AI practices into their portfolios. Many companies struggle to define what their deployment model should look like.

Table of Contents

Prioritizing Customer Needs in Your Generative AI Strategy

Across many companies, three popular generative AI deployment models are commonly applied.

  • Open Model: The most flexible approach, with generative AI tools accessible to everyone. It encourages rapid innovation but poses compliance risks. It works best with set boundaries and the guideline: “Don’t do stupid things.”
  • Decentralized Model (Labs): Departments experiment with generative AI independently, fostering agility and rapid iteration. While it enables quick testing, it risks fragmentation without clear design principles.
  • Centralized Model: A single team manages generative AI, ensuring consistent governance and strategy. This model is ideal for regulated industries but can create bottlenecks when distributing concepts across the organization.

Many companies experience challenges with these generative AI models. Often, they feel compelled to choose one of the three models: central, decentralized or open. The assumption is that only one model can be the right one. However, each model has its strengths and weaknesses in serving the customer and the company.

chart of central, decentralized, or open models for genai

Our research shows that industry outperformers take a radically different approach. They understand that it’s not about just picking the right deployment model; it’s about deploying each model right. Outperformers don’t limit themselves to a single model but adaptively leverage all three based on the customer needs. 

Their secret is to make generative AI integration a customer-centric endeavor, rather than focusing on what is best for their organizational chart. They blend the models based on the maturity of the customer proposition, ensuring that the approach is driven by customer needs, not organizational preferences. This nuanced strategy allows them to stay focused on solving real customer challenges with the right tools.

Outperformers recognize that customer propositions are at different stages of their lifecycles, both in terms of product maturity and customer traction. They distinguish three stages: problem-market fit, product-market fit and platform-market fit. At each stage, a different alignment between marketing (technology) and IT is needed.

Related Article: How AI Is Revolutionizing the Customer Journey in 2024

Deploying the Right Generative AI Model: Company-Centric Second

The outside-in approach from industry outperformers is a great starting point, but what does that mean for the marketing and IT departments? How do they align their efforts with the customer and each other? Is that even possible and viable? It can become messy quite quickly.

The Open Model Leads in Finding the Problem-Market-Fit

An open approach encourages rapid innovation with generative AI tools across the organization. This phase allows for quick experimentation but carries compliance and governance risks. Critical IT design principles are applied selectively, with the centralized model providing essential guardrails to ensure responsible use.

The Decentralized Model (Labs) Leads in Finding the Product-Market-Fit

A decentralized approach prepares for the scalability of generative AI solutions across the organization. As solutions become viable, the decentralized model empowers individual departments to independently customize customer propositions (often referred to as “Labs” in some organizations). IT design principles are applied to the prototype, to build a scalable MVP by eliminating all data, feature and integration redundancies. Centralized compliance guardrails ensure alignment across departments.

The Centralized Model Leads in Finding the Platform-Market-Fit

A centralized approach encourages the exploitation of proven and well-performing customer propositions. In this phase, adjustments are only required to remove minor frictions. IT design principles are rigorously applied to scale, aiming for a high-performance, zero-maintenance, legacy-free environment. This model ensures optimal control, governance and compliance while delivering a streamlined, sustainable solution for generative AI initiatives.

Choosing the Right Generative AI Model for Optimal Impact

As your customer proposition matures, the generative AI model should transition to support each stage effectively. It’s about finding and applying the right generative AI model mix to follow the evolving needs of your customers at every step of the customer journey. Refer to the graph for a helpful rule of thumb.

finding right mix of genai model graph

In summary, by aligning the necessary capabilities with the optimal GenAI Model Mix (%) for each customer proposition stage, this is the result.

1. Problem-Market Fit Stage

  • Open model, 70%: A series of iterative stand-alone projects are employed as long as the proposition remains unproven, utilizing design thinking for rapid experimentation, supporting a PoC (Proof of Concept) / Prototype approach to validate early ideas.
  • Decentralized model, 25%: IT design principles are applied opportunistically to save time down the road.
  • Centralized model, 5%: Establish AI compliance guardrails to ensure minimal risks while hacking toward viable propositions.

2. Product-Market Fit Stage

  • Open model, 25%: As the solution moves toward viability, address frictions that surface occasionally.
  • Decentralized model, 50%: Scale using refined IT design principles, eliminating redundant data, features and integrations to create an efficient MVP (Minimum Viable Product) through a Lean Startup methodology, removing exceptions and working toward a standardized process.
  • Centralized model, 25%: Strengthen compliance guardrails to support broader adoption while maintaining control.

3. Platform-Market Fit Stage

  • Open model, 5%: Minimal adjustments (hacks) are made to resolve any remaining frictions and adjusted to competition.
  • Decentralized model, 25%: IT design principles are fully scaled, reinforcing the product’s robustness.
  • Centralized model, 70%: Use a Scrum (Agile) framework to aim for a zero-maintenance, legacy-proof Staging & Production environment, ensuring seamless exploitation within the established product supported.

Related Article: How to Pick the Right Flavor of Generative AI

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