Observed Signal · Apr 8, 2026 · Research Report · Source: https://martechseries.com/feed/ · Impact: 3/5 · Sentiment: Positive
Research: AI Becomes Foundational in Modern Marketing
Callan Consulting’s "State of AI in Technology Marketing 2026" report, produced with 18 B2B and B2C technology companies including NetApp, finds AI moving from experimental projects to enterprise‑wide integration across marketing functions. Based on interviews with CMOs and senior marketing leaders, the study reports AI is now embedded in content creation, research, campaign optimization and analytics. As use expands, data quality, accessibility and governance have moved to the top of marketing agendas. The report notes the rise of "Born in AI" marketing teams, the emergence of Answer Engine Optimization (AEO), increasing interest in agentic AI, and persistent challenges such as measuring AI ROI and maintaining human oversight.
A multi‑company industry study showing AI has moved to enterprise‑wide marketing adoption affects MarTech roadmaps, data governance priorities, SEO/AEO strategies and measurement approaches, but it is not a platform policy or major technical release.
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Key Takeaways & Evidence Grounding
- NetApp participated in a Callan Consulting marketing research study on AI in marketing.
- Callan Consulting published the "State of AI in Technology Marketing 2026" report based on in‑depth interviews with CMOs and senior marketing leaders at 18 participating companies.
- The study finds AI is being integrated across core marketing teams and workflows including content development, campaign optimization and analytics.
- Data quality, accessibility and governance are identified as critical priorities as AI adoption deepens.
- The report highlights emerging trends: "Born in AI" marketing organizations, Answer Engine Optimization (AEO), growing use of agentic AI, and difficulties measuring AI's direct ROI.
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Related Market Signals & Shifts
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Bridging the AI ROI Gap in B2B Marketing
The MarTech article argues that AI adoption in B2B marketing is widespread but proof of business impact lags due to low maturity, disconnected strategy, and weak governance. It cites multiple industry reports showing high tool adoption (91% of marketing teams use AI) while only 41% can prove ROI. Many companies lack roadmaps or shared operating models, and use cases are concentrated in low‑risk tasks like content ideation. The piece recommends mapping AI to revenue bottlenecks, embedding AI into core systems (CRM/MAP/attribution), defining accountability and governance, and lifting organizational literacy to reduce hallucinations and privacy risk. It highlights early measurable wins (personalization, data enrichment, repurposing content) and predicts an evolution toward “agentic workflows” that execute marketing operations under rules and tighter controls.
What CMOs Should Do About AI Now
This MarTech article argues that chief marketing officers must accelerate AI adoption and build AI skills, organizational structures, and measurement to capture marketing benefits. It cites industry research showing high CMO enthusiasm but limited organizational rewiring or skill readiness: a June 2026 McKinsey survey found 96% of CMOs excited about AI but only 28% see fundamental rewiring underway; Gartner research shows only 32% think significant CMO profile changes are needed. The piece recommends CMOs develop AI literacy, reconfigure teams and budgets (Gartner reports an average 15.3% of marketing budgets allocated to AI), create new AI roles, and focus on measurable business results from hybrid human–AI workflows. It cites McKinsey and BCG projections for revenue and speed gains from AI-enabled marketing.
Agentic AI Reshapes Marketing Workflows, McKinsey Says
A McKinsey & Company analysis finds agentic AI — AI agents that execute multi-step marketing tasks under human supervision — is gaining traction and could support as much as two‑thirds of current marketing activities. The report says agentic systems, built on foundation models, can accelerate campaign processes (10–15x) and speed content cycles (up to 4x), and that organisations piloting integrated agentic workflows have seen potential revenue uplifts of 10–30%. Widespread experimentation has produced fragmented, isolated deployments; the primary barriers to scale are systems interoperability, unified data layers, identity frameworks and API-driven activation rather than model capability. Vendors such as Adobe and HubSpot are embedding AI agents into marketing platforms, but McKinsey notes fewer than 10% of firms have deployed end-to-end workflows that generate measurable value.
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