AI Advantage Assessment

Across AI Advantage Assessment respondents, the adoption of AI for analytics is trailing other use cases – creating a space where curious and patient leaders can make a difference for their departments and careers. Marketers are:

• Broadly using AI for lightweight, early-stage analytical support, 
• Hesitating on deeper, more consequential analytics that could make bigger revenue and cost-saving impact. 

Most adopted analytics use cases 

The majority of marketing groups are NOT using ANY analytics use case frequently. AI Advantage Assessment data defines frequency as the % of marketers saying they are deploying a use case “Increasingly” or “All the time.”  The analytics tasks with the strongest AI momentum are frequent, low-risk, repetitive tasks that don’t require explaining a model’s conclusion to a boss.

• First Drafts of Analyses & Data Visualization (42%) – Marketers using AI to: generate first-pass charts, summarize data, and identify draft insights. These tasks feel familiar, fast, and low stakes. AI makes analysts more productive, without requiring deep trust in the model
• ICP, Persona & Market Segment Identification (36%) – In B2B, marketers demand gen marketers are the most quantitatively inclined and are used to working with many spreadsheets from diverse sources that make it hard to draw fact-based conclusions. That is proving to be a good fit for marketers to use AI. 
• Lead/Account Scoring & Campaign Effectiveness (29%) – In the demand gen category, this medium complexity, high value use case helps marketers bring together multiple spreadsheets + MAP & CRM system data to improve or automate lead scoring and effectiveness measurement.
• Audience Segmentation for Ads/Social (28%) Paid media marketers have long pushed into automated optimization and this use case is similar to the ICP/market segmentation use case.  

Growing use cases – Tasks matter to revenue and growth—but can involve more complex data, cross-team dependencies.

• Building Richer Audience Profiles (20%) Marketers want this, but it depends on data quality, identity resolution, and cross-platform consistency. Messy real-world data slows adoption.
• Predictive Analytics for Customer Behavior (18%) – This use case requires trust. Predictive models need historical data, validation, and monitoring, things that the marketing team may not always have or be capable of. 
• Pipeline/Waterfall Reporting (18%) – It is surprising this use case has not earned more momentum, given the repetitiveness of the analysis and the need to figure out “what do changes in data mean” so frequently. 

Niche Use Cases – Analytically sophisticated, often technical use cases requiring high-quality data and org maturity. 

• Territory Analysis (13%) – Useful for field sales orgs, but not core for most marketing teams. 
• Pricing Impact Analysis (12%) – This is one of the starkest gaps. Pricing analytics can impact margins, revenue, competitive strategy. 
• Anomaly Detection & Fraud Detection (7%) These are technical use cases require clean, real-time datasets,  modeling capability and integration into risk workflows. Things that few marketing groups feel comfortable with. 

CMO takeaways

• Focus on the Demand Gen for AI Analytics: High-momentum, high-value tasks like Lead/Account Scoring & Campaign Effectiveness and ICP, Persona & Segment ID are good targets to deliver real value in outbound marketing. 
• Data, Not Just AI: Adoption is heavily hampered by foundational data issues. Efforts to build richer audience profiles are slowed by data quality, identity resolution, and cross-platform consistency issues. The most urgent task is to establish “decision-grade” data before attempting complex AI models.