Before scaling AI, fix your data foundations

Generative AI (genAI) is rapidly transforming the martech landscape, with adoption accelerating and companies of all sizes scaling pilots into fully functional solutions. But for marketers still in the early stages of developing genAI workflows, one factor matters most: data readiness.

That readiness begins with structured, high-quality data feeding into the right workflows. A robust data infrastructure is the foundation for any successful genAI initiative. With a solid data strategy and access to rich, trusted datasets, marketers can identify relevant opportunities, improve effectiveness and drive measurable returns.

Without proper preparation, scaling genAI leads to inefficiencies and underperformance. So what does it take to scale AI successfully — and how do strong data foundations help marketers make more informed, high-impact decisions?

Successful AI implementation relies on data readiness 

There are many ways to implement genAI in marketing and advertising, from content creation to data interpretation and automation. But one size doesn’t fit all. Every organization faces a unique mix of priorities and pressures. You need a clear view of operations and data inputs to identify where AI can drive the most value and what the most effective application is for your business.

We live in a world where businesses have, over time, built a mix of different data sources that exist in different pockets. Even the most advanced AI models can be undermined by:

• Disconnected sources.
• Inconsistent storage systems.
• Unclear structure of ownership. 

A clean and efficient data infrastructure will greatly improve results. This makes it possible for multiple jobs to run simultaneously across datasets, speeding up processes and routes to success.

For generative AI to deliver real value — whether it’s used for content automation, predictive analytics or hyper-personalized marketing — organizations need to start with a comprehensive data audit. That means:

• Understanding how data flows across departments.
• Identifying silos and integration gaps.
• Assessing data quality, access and governance. 

Establishing centralized ownership and a strong data infrastructure is the first step toward scalable AI.

Dig deeper: 4 ways to correct bad data and improve your AI

Fostering a positive AI culture at scale

A recent HubSpot survey found:

• 54% of respondents feel overwhelmed by the prospect of incorporating AI into their marketing workflows.
• 62% of those same marketers said that “generative AI is supposed to be more of their focus this year.

While data forms the foundation of generative AI success, technical infrastructure alone isn’t enough. Once AI tools are in place, it’s essential to invest in AI literacy and upskilling to drive adoption and understanding across the organization.

Identify early AI users who can serve as internal advocates. These individuals can help bridge the gap between curiosity and capability, creating momentum for a smoother onboarding process throughout the business.

To build this buy-in, prioritize targeted AI training for marketers, analysts and decision-makers. With the technology’s growing relevance, role-specific courses are increasingly available and specialized agencies can offer ongoing support.

Beyond training, encourage cross-functional collaboration by sharing knowledge, solving problems across disciplines and exchanging best practices. These habits play a critical role in driving internal AI transformation.

AI growth stems from trust and transparency

As AI becomes more deeply embedded in business operations, building trust is essential. With so many possibilities and multiple stakeholders involved, it’s easy to encounter missteps. Many common issues can be avoided with a more strategic, systematic approach and by following established best practices.

Generative AI tools should be designed and deployed with transparency, especially when they interact directly with customers. Reliable measurement frameworks help track performance, mitigate bias and ensure ethical use.

Regularly monitoring genAI deployments and clearly communicating ROI internally also reinforces confidence. Aligning AI outputs with brand voice and compliance standards ensures AI-generated content integrates smoothly with existing marketing assets.

As regulatory frameworks continue to evolve, it’s critical to involve legal and ethics teams early in the development process. Their guidance helps manage risk and keeps initiatives within current legal boundaries.

Another common misstep is prioritizing AI-driven efficiency at the expense of the customer experience. It’s important to strike a balance between streamlining operations and preserving meaningful, accurate and human-centered interactions. When poorly implemented, AI can lead to frustration by delivering irrelevant content or replacing helpful human touchpoints, ultimately harming the brand experience.

Dig deeper: How to make sure your data is AI-ready

Data foundations drive overall generative AI success

If your organization has the capability to introduce AI, there’s no reason it can’t be used to improve efficiency, freeing teams to focus on high-level strategies that often get deprioritized. GenAI should be viewed as a business support tool, not a replacement for entire processes or roles.

But unlocking genAI’s full potential starts with a strong data foundation. By prioritizing data readiness as the launchpad, martech organizations can set themselves up for success. Investing in that foundation today enables companies to scale AI initiatives with greater confidence:

• Driving growth.
• Improving efficiency.
• Building more meaningful customer relationships.