How Marketers Are Measuring AI ROI and Where They Struggle

Reading Time: 4 minutes

Last updated April 2026

Challenges in Measuring AI ROI

Marketers report intense pressure to prove AI’s value even as traditional metrics fall short. In fact, a recent survey found 95% of marketing leaders feel pressure to demonstrate ROI. Yet AI’s benefits often lie in efficiency gains or long-term insights, not immediate sales. For example, one consultant notes AI yields “efficiency, innovation, and risk reduction,” which are “hard to quantify in dollars”.

Likewise, IBM points out that many AI impacts are indirect and long-term, so short-term ROI is often elusive. This mismatch means “traditional analytics ROI metrics…fail to capture AI’s true value proposition”. Marketing veteran Jessica Apotheker (BCG CMO) bluntly observes that “most people are not seeing ROI from [AI] investment yet at scale”.

In practice, marketers struggle with multi-touch attribution (which channel “earned” revenue), defining soft costs (time and talent spent), and avoiding “vanity metrics” (like sheer content volume). In short, AI’s benefits often span multiple campaigns and customer journey stages, making a single ROI number hard to pin down.

Examples of Measurable Success

Despite the challenges, several case studies report clear lifts after adopting AI in marketing:

• Advertising Optimization: A Nielsen and Google study of more than 50,000 brand campaigns and more than 1 million performance campaigns found AI-powered ad solutions significantly outperformed manual campaigns. For example, AI-driven YouTube ads achieved about 17 percent higher ROAS (return on ad spend), AI search Broad Match keywords drove about 15 percent higher ROAS, and Performance Max campaigns saw about 8 percent higher ROAS versus traditional methods. In total, combining AI-powered formats such as video reach and view campaigns boosted sales effectiveness by about 23 percent.
• Email Personalization: Generative AI personalization can dramatically improve engagement. One retail case (Michaels Stores) increased personalized email campaigns from 20 percent to 95 percent of sends. This jump lifted click-through rates by 25 percent for email and 41 percent for SMS. More generally, AI-driven email personalization has been shown to boost revenue up to about 41 percent and click-through rate by about 13 percent. Bloomreach reports plus 41 percent revenue and plus 13.4 percent CTR.
• Lead Scoring and CRM: AI predictive scoring also delivers value. One company using machine learning based lead scoring reported about a 25 percent larger sales pipeline and ultimately a 76 percent win rate on deals. By letting AI rank leads, conversion rates jumped compared to old methods. Pipeline growth and win rate lifts translate into clear revenue gains.
• Process Automation: In related marketing and commerce functions, AI can yield large time savings. For instance, a direct to consumer retailer used generative AI to automate customer support responses, cutting time to first response by 80 percent and shaving about 4 minutes off each ticket resolution. While support is downstream of marketing, this efficiency freed teams to focus on higher value marketing activities.

Each of these examples ties AI investment to concrete metrics, such as higher ROAS or CTR, larger pipelines, and faster process times. This shows how ROI can be measured when the right KPI is chosen.

To see how AI efforts could pay off for your own marketingm try my free AI Content ROI Calculator.

Emerging Best Practices

Experts recommend new frameworks and tools for quantifying AI’s impact rather than relying on old metrics alone. A leading practice is to combine different measurement approaches: use ROI where applicable, but also track efficiency and prediction gains. Instead of just “revenue gained,” measure how AI cuts campaign analysis time, such as reducing a report run from hours to minutes, or improves forecast accuracy.

Gartner advises building an AI “portfolio” of use cases, from quick wins (measured by time or cost saved) to transformational initiatives (valued for long-term growth), and pilot each with clear targets. This might mean setting concrete goals like “increase model-driven ROI forecasts by 25 percent” or “cut data prep time by 50 percent” when testing a new AI tool.

On the tooling side, AI-driven analytics platforms are emerging. Marketers use unified measurement tools that blend marketing mix modeling and multi-touch attribution with incremental lift tests. For example, platforms like Rockerbox ingest all channel data and apply machine learning to allocate credit across touchpoints. Similarly, predictive analytics tools such as Pecan AI let teams forecast a campaign’s future ROAS and customer lifetime value within days. These systems make ROI more visible by simulating outcomes and testing scenarios up front. In practice, marketers are increasingly using “AI for attribution” to allocate budgets more effectively and “AI for prediction” to estimate campaign returns before full rollout.

Other best practices include investing in talent and data. Skilled analysts and clean data often deliver ROI faster than technology alone. As CMSWire notes, companies showing positive AI analytics ROI have built internal capability as much as buying tools.

Finally, incremental testing, such as A/B tests or hold out groups, is recommended to prove AI lift. By comparing similar audiences with and without an AI intervention, teams can attribute real revenue impact to the AI feature, much as Nielsen did for Google’s AI ads. In short, today’s best practice is to pilot AI projects with clear success metrics, both financial and operational, and use advanced analytics such as marketing mix modeling, machine learning attribution, and lift testing to tie AI-driven changes to concrete business outcomes.

References

1. Nielsen study confirms Google’s AI-powered ad solutions drive higher ROI (Adgully summary)
2. How AI is redefining marketing, today and tomorrow (Nielsen Insights)
3. How Bloomreach Delivers True End-to-End Personalization (Bloomreach Blog)
4. How BrewDog increased revenue using personalized email campaigns with Bloomreach (Case Study)
5. River Island’s Email Marketing Success (Bloomreach Case Study)
6. Why Marketing — and Not IT — Must Lead the AI Transformation (CMSWire)
7. From Productivity to Impact: Unlocking the True Potential of AI in Marketing (Gartner)
8. Marketing Teams Are Bringing Their Own AI — And It’s Changing Everything (CMSWire)