🚀 How Do We Scale AI Solutions Across Marketing Functions?

Build the foundation, overcome barriers, and drive measurable (and ethical) success across teams and campaigns.

This AI Marketing Ethics Digest issue is sponsored by Bizzuka, an AI training, consulting, and strategy agency.

Bizzuka’s AI Strategy Canvas™ is a comprehensive guide for businesses ready to integrate, optimize, and scale generative AI across the enterprise.

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This is the third in a series of weekly issues answering marketing teams' questions about using AI in marketing. It focuses on scaling AI across strategies and campaigns.

Piloting AI in marketing is one thing—scaling it across teams and campaigns is another. Even the best tools can fail to deliver their full potential without a clear roadmap. Learn how to break down barriers, unify data, and strategically expand AI’s role to transform your marketing efforts.

Image generated by Adobe Firefly

Why This Question Matters

Scaling AI across marketing functions matters because its true impact lies not in isolated successes but in its ability to transform entire marketing ecosystems. Many organizations see early wins with pilot programs, such as chatbots or predictive analytics, but fail to translate these successes into broader organizational change.

Scaling AI unlocks its full potential by enabling data-driven decisions, streamlining workflows, and delivering a consistent brand strategy. Companies that achieve scale gain a significant competitive edge, enhancing speed to market, anticipating customer needs, and driving deeper loyalty through hyper-personalization.

However, scaling AI often reveals systemic weaknesses, such as siloed data, resource gaps, and inefficient processes. Addressing these challenges improves AI performance and strengthens the organization’s overall infrastructure.

Scaling AI also requires cultural buy-in, as teams may resist due to fears of job displacement or distrust of the technology. Without fostering a culture of collaboration and trust, adoption rates will plummet, and the full benefits of AI will remain unrealized.

Organizations that fail to scale AI risk being stuck in “pilot paralysis,” where repeated small-scale projects limit their ability to leverage economies of scale, justify investments, and achieve long-term ROI.

Scaling AI also increases ethical risks, such as amplifying biases and privacy concerns and reduced human oversight. Thoughtful implementation ensures these risks are managed effectively without stifling innovation.

In today’s market, customer expectations for seamless, personalized experiences are higher than ever. Scaling AI empowers organizations to predict needs, deliver consistent experiences across channels, and respond rapidly to queries, ultimately driving customer satisfaction and retention.

Organizations can bridge the gap between experimentation and transformation by prioritizing scale and positioning themselves as leaders in a rapidly evolving competitive landscape.

The Answer: Build a Foundation, Then Scale Strategically

So, how do marketing departments go about scaling? By taking these four steps:

1. Ensure Your Data Is Unified and Clean
Scaling AI relies on having high-quality, centralized data. Siloed or fragmented data will hinder AI’s ability to deliver accurate insights.

💡 Steps to Take:

• Implement a Customer Data Platform (CDP) to unify customer data. A CDP is a centralized customer database that builds unified profiles from data it collects across disparate data silos.

• Regularly clean and de-duplicate your data.

• Use AI-powered tools to automate data integration.

2. Create Cross-Functional Teams
Scaling AI requires collaboration across marketing, IT, and analytics teams. Establish a dedicated AI task force to manage integration efforts.

Case Study: Unilever
Unilever has indeed taken significant steps to ensure seamless AI adoption across its organization, implementing several initiatives that align with this concept:

1. Global AI Lab: In November 2023, Unilever opened its first global AI lab, called "Horizon3 Labs," in Toronto, Canada. This lab is designed to accelerate the generation of new AI concepts, designs, and projects that can be scaled and shared across Unilever's business globally.

2. Hybrid Operating Model: Unilever has adopted a hybrid model for its data and AI initiatives. This model includes:

• Global centers of excellence.

• Data and solution factories.

• Local teams in top geographic markets partnering with the business.

3. Cross-functional Collaboration: The company emphasizes co-creation and co-ownership of analytics and AI capabilities with various business functions. This approach likely involves collaboration between marketing, R&D, and IT teams.

4. AI Integration Across Departments: Unilever has implemented over 400 applications of AI across various disciplines, including marketing, innovation, supply chain, and research & development.

5. DataLab: Unilever has established a virtual digital lab called DataLab, which involves 12 technology and analytics partners. This initiative facilitates collaboration between different functions within the company.

While these initiatives demonstrate Unilever's commitment to AI adoption and cross-functional collaboration. The organization’s approach is distributed and flexible, leveraging various centers, labs, and collaborative models to drive AI adoption across the organization.

3. Standardize Tools and Processes
When scaling AI, inconsistency in tools or workflows can lead to inefficiencies.

💡 Key Considerations:

• Choose tools that integrate with your current marketing tech stack.

• Standardize processes for using AI tools across teams.

• Train teams to use AI tools consistently.

4. Start Small and Scale Incrementally
Instead of deploying AI across all marketing functions simultaneously, focus on scaling in phases.

💡 Example:

• Phase 1: Scale chatbots to all customer-facing channels.

• Phase 2: Expand predictive analytics to email campaigns and ad targeting.

• Phase 3: Integrate AI insights into customer journey mapping.

Case Study: Starbucks' Global AI Expansion

Image from AI Expert Network

Starbucks scaled its AI-powered personalization program, “Deep Brew,” across its app and in-store experiences.

• Phase 1: Piloted AI-driven personalized offers in the U.S. app.

• Phase 2: Rolled out the program to international markets.

• Phase 3: Integrated AI into in-store order suggestions and inventory management.

Results:

• Increased customer engagement by 30%.

• Improved operational efficiency through real-time data insights.

Common Challenges When Scaling AI

❌ Challenge #1: Lack of Organizational Alignment
Solution: Secure executive buy-in and communicate the benefits of AI across departments.

❌ Challenge #2: Technical Limitations
Solution: Audit your infrastructure to ensure scalability. Consider cloud-based AI solutions for flexibility.

❌ Challenge #3: Change Resistance
Solution: Offer ongoing training and highlight early wins to build confidence in AI adoption.

Ethical Considerations

When scaling AI, be mindful of ethical issues that may arise with broader implementation:

• Data Bias: Ensure AI models don’t amplify existing biases as they scale.

• Transparency: Maintain clarity about how AI decisions are made, especially in customer-facing roles.

• Over-Reliance on AI: Balance automation with human oversight to avoid losing the personal touch.

Quick Action Plan for Scaling AI

1. ✅ Audit Your Data: Identify gaps in data quality and centralization.

2. ✅ Pilot New AI Functions: Test one new AI function before scaling broadly.

3. ✅ Create a Training Program: Educate teams to ensure smooth and effective AI adoption.

4. ✅ Monitor and Optimize: Track the performance of scaled AI solutions and refine them over time.

Conclusion: Scaling AI for Long-Term Impact

Scaling AI isn’t just about adding tools—it’s about creating a scalable infrastructure, fostering collaboration, and driving measurable results. Organizations can unlock AI's full potential in marketing by addressing data, tools, and team readiness.

Let us hear from you. What is your marketing team or organization doing to scale AI across teams, campaigns, and departments?

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