How to overcome AI challenges in martech to maximize ROI

AI is transforming martech by automating tasks, providing real-time insights and scaling operations more efficiently. However, a number of issues make integrating artificial intelligence into martech stacks very challenging. Here are actionable strategies for solving these and other common AI problems.

Dig deeper: AI Readiness Checklist: 7 Key Steps to Successful Integration

Common challenges in AI integration and how to overcome them

Integrating AI into existing martech stacks is no easy feat. These are the main reasons why:

• The complexity of existing martech stacks: Many of us are already burdened expanding solutions in martech and adtech. Adding AI-driven solutions to already widespread ecosystems can easily create confusion and waste.
• Data quality and integration: AI thrives on clean, well-structured data. Identify AI use cases that can be built on existing clean data sets such as product feeds or digital campaign performance data.
• Resistance to change: Teams may be hesitant to trust AI-driven tools, fearing loss of control or job displacement. Brands can resist a lack of control over brand safety and guidelines, especially in industries with significant regulatory or legal marketing restrictions.
• Skills gaps or resource allocation: Organizations often lack the internal expertise needed to effectively deploy and manage AI. Balancing the initial investment with the long-term return on investment can be daunting.

By addressing these challenges head on, we can facilitate the seamless integration of AI and unlock its full potential.

Start with clear goals

Define and prioritize specific marketing problems that AI can solve, such as improving customer segmentation, analyzing creative performance, or optimizing ad spend.

Review your martech stack

Identify existing gaps and opportunities where AI can improve performance. Prioritize easily actionable opportunities where existing datasets are AI-ready — granular, robust, and relatively well-structured.

Invest in data readiness

For other high-priority AI opportunities, invest in cleaning your data. Prioritize data management, integration and quality to ensure AI models deliver meaningful insights. Create feedback loops where models and algorithms continually learn about what drives your business.

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

Build a cross-functional task force and acceleration partners

Drive collaboration between data scientists, marketers and technologists to ensure AI tools are aligned with business goals. Consider a build-buy-partner framework to identify areas where using agency or technology partners could accelerate without sacrificing data ownership.

Partnering with outside experts can also help organizations pilot initiatives such as predictive analytics and creative optimization without requiring a large upfront internal investment.

Start small, scale iteratively

Pilot AI initiatives in low-risk areas where resource alignment exists. Identify wins and gain buy-in to expand based on learnings.

Dig deeper: 5 Ways to Jumpstart AI Adoption

Tailoring your martech stack for AI success

As AI evolves, marketers need to prepare their martech suites to adapt to the new trends. Here’s how.

Define and measure what matters

Identify key performance indicators associated with AI-driven initiatives, such as cost savings, increased conversions or improved customer retention. Don’t forget to consider the value of time savings or increased production speed.

Explain AI and privacy guardrails

Ensure alignment of marketing, privacy, technology and legal guidance on what the data should be never use as input to train AI models and ensure that these guardrails are clearly enforced.

Hug explainable AI. Enabling tools that provide transparency into AI decision-making will be key to building trust and accountability.

Adopt interoperable platforms

Choose tools that integrate seamlessly with other technologies. For example, platforms that support a flexible API can help marketers quickly adapt to new channels or data sets as the ecosystem evolves.

Invest in talent and partnerships

Upskilling internal teams and partnering with AI-savvy agencies will ensure your organization remains competitive. Use knowledge sharing and recognition to drive AI-powered innovation at all levels and identify new ways of working.

Dig deeper: Laying the groundwork for AI in MOps: How to get started

The question is no longer whether to integrate artificial intelligence into your martech stack, but how to do it efficiently and at scale. Although challenges exist, they can be overcome with the right strategies and tools. You can fully leverage the transformative potential of AI by defining clear goals, investing in data readiness, and iterating constantly.