The ROI of AI in Marketing: Why Most Companies Aren’t Measuring It

Artificial intelligence (AI) is revolutionizing the marketing landscape, offering unprecedented opportunities for efficiency, creativity, and revenue growth.

According to a Jasper's latest survey, 63% of marketing teams are already using generative AI, with 78% having a positive impact. Despite this rapid adoption, only 49% of companies are tracking the return on investment (ROI) of their AI initiatives.

*Increased productivity (28%) and improved marketing ROI (25%) are the top-identified benefits of AI adoption. Jasper*

This gap in measurement represents a critical oversight, as understanding ROI is essential for scaling AI adoption, optimizing strategies, and maintaining a competitive edge.

This article explores the current state of AI adoption in marketing, its benefits, challenges, and the actionable steps companies must take to measure and maximize ROI.

The State of AI Adoption in Marketing

Adoption Trends and Statistics

AI adoption in marketing is accelerating across industries. 63% of marketing teams currently use generative AI, with 27% evaluating its use in the next six months.

Industry-specific adoption varies significantly. Professional services and technology companies are at the forefront, with 34% and 30% respectively implementing formal AI programs.

Larger enterprises are leading the charge, with 62% of enterprise marketing teams (1,000+ employees) having begin tracking AI ROI over 12 months ago, compared to 39% of smaller teams that started within the past year.

Larger companies (>$1B revenue) prioritize AI quality and brand governance, while smaller companies focus on budget constraints, leadership buy-in, and data privacy.

Maturity of AI Use Cases

Despite rapid adoption, AI maturity in marketing remains low. Only 10% of marketers self-report as "very advanced" in AI maturity, with most activity concentrated in early-stage use cases like content creation and idea generation.

*Most are still in early stages, like making content and coming up with ideas.*

Advanced applications such as workflow automation, brand governance, and hyper-personalization are underutilized.

Adoption alone is not enough. Companies must prioritize domain-specific AI to achieve measurable outcomes.

High-maturity teams often use domain-specific AI tools, with 71% of "very advanced" teams leveraging these solutions compared to 21% relying solely on general-purpose tools. This distinction is critical, as domain-specific tools are better aligned with a company’s unique data, brand guidelines, and marketing workflows, enabling scalable outcomes.

Challenges and Barriers to AI Adoption

Despite its promise, AI adoption in marketing faces several challenges.

Data privacy and output quality

These two are the top barriers, particularly in industries like life sciences, retail, and financial services. Leadership buy-in, AI expertise, and budget constraints also remain significant hurdles.

Scalability and Integration

Many companies struggle with scalability and integration. 56% of adopters still rely on ad hoc, siloed applications, limiting their ability to achieve measurable outcomes. This fragmented approach underscores the need for structured AI programs and workflow integration.

ROI Tracking

A critical gap in AI adoption is the lack of ROI measurement. Only 49% of companies track the ROI of their AI investments, with 22% planning to start in 2025. Without measuring ROI, companies cannot assess the effectiveness of their AI initiatives or justify further investment.

CMO-Team Alignment Gap

There is a notable disconnect between leadership and teams regarding AI maturity. 65% of CMOs are confident in their AI maturity, compared to just 37% of managers. This gap highlights the need for better communication, training, and alignment across all levels of the organization.

How to Drive AI Adoption and Maximize ROI

The future of AI in marketing is bright, As barriers lift and roles evolve, marketing is poised to reclaim its role as a true revenue driver.

To unlock the full potential of AI in marketing, companies must take a strategic and structured approach. Below are actionable recommendations to accelerate adoption, enhance value, and improve ROI measurement:

*Key traits of high-maturity marketing teams*

1. Invest in Domain-Specific AI Tools

Generic AI tools often fall short in addressing industry-specific needs. Companies should prioritize domain-specific AI solutions that align with their unique data, brand guidelines, and workflows. These tools are 37% more likely to deliver measurable ROI and enable scalable outcomes.

2. Establish a Marketing AI Council

A dedicated Marketing AI Council can drive alignment, governance, and strategy. This cross-functional team should include representatives from marketing, IT, data analytics, and leadership to ensure cohesive implementation and oversight.

3. Document and Prioritize Use Cases

High-maturity teams document and prioritize AI use cases to focus efforts on high-impact areas. Start with low-hanging fruit like content creation and campaign optimization, then scale to advanced applications such as hyper-personalization and predictive analytics.

4. Integrate AI into Existing Workflows

AI should not operate in silos. Integrate AI tools into existing marketing workflows to streamline processes and enhance efficiency. For example, use AI to automate repetitive tasks like email campaign management or social media scheduling.

5. Provide Advanced Training and Upskilling

Empower teams with the skills needed to leverage AI effectively. Offer advanced training programs on AI tools, data analytics, and ethical AI practices. This investment in upskilling will drive adoption and improve output quality.

6. Measure and Track ROI Consistently

To justify AI investments, companies must measure ROI consistently. Establish clear KPIs such as cost savings, revenue growth, and time efficiency. Use analytics dashboards to track progress and adjust strategies as needed.

7. Experiment and Iterate

AI adoption is an iterative process. Encourage teams to experiment with new tools and techniques, learn from failures, and refine strategies. This culture of experimentation fosters innovation and accelerates maturity.

8. Address Data Privacy and Governance

Data privacy remains a top barrier to AI adoption. Implement robust data governance frameworks to ensure compliance with regulations and build trust with customers. Use AI tools with built-in privacy features to mitigate risks.

9. Secure Leadership Buy-In

Leadership support is critical for scaling AI initiatives. Present a clear business case highlighting the potential ROI and competitive advantages of AI adoption. Engage leadership in decision-making to secure budget and resources.

10. Leverage AI for Hyper-Personalization

AI excels at delivering personalized experiences. Use AI to analyze customer data and create hyper-personalized campaigns that drive engagement and conversions. This approach not only enhances customer satisfaction but also boosts ROI.

AI has the potential to transform marketing, but its success depends on strategic implementation, domain-specific solutions, and leadership alignment. Companies that invest in these areas are better positioned to scale adoption, measure ROI, and maintain a competitive edge.

By embracing AI with a clear strategy and actionable steps, companies can unlock its full potential and drive meaningful business outcomes.