Observed Signal · Sep 11, 2026 · Opinion / Analysis · Source: https://martech.org/feed/ · Impact: 2/5 · Sentiment: Neutral
CMOs Must Decide Buy vs Build for AI Capabilities
This article argues that the key AI decision for CMOs is no longer about choosing the best platform, but about determining which AI capabilities are strategic enough to build and own internally versus those that should be bought from major vendors. As Salesforce, Adobe, and Oracle integrate similar AI agents into their platforms, horizontal marketing tasks are becoming commoditized. The article emphasizes that true competitive advantage lies in proprietary workflows, institutional knowledge, and company-specific processes. It advocates for a structured governance path to promote successful internal AI agents from experiments to core operational infrastructure, involving stages of value proof, validation, and governance review. This approach enables organizations to align buy-versus-build decisions across marketing, IT, security, and finance, ultimately treating governance as a growth enabler rather than a barrier.
Opinion piece on AI strategy for CMOs. Relevant for martech leaders but no concrete news event or data.
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Key Takeaways & Evidence Grounding
- Salesforce, Adobe, and Oracle are converging on similar AI agent capabilities for marketing.
- Major martech vendors are absorbing horizontal marketing tasks such as audience segmentation and content drafting.
- The article recommends a three-stage promotion path for internal AI agents: proving value, validation, and governance review.
- The author suggests that CMOs should focus on organizational design rather than technology selection.
- The content is authored by a contributing writer for MarTech.
Connected Companies & Entities
3 Entities mapped“Salesforce is showcasing agents that can draft briefs, manage leads, and automate campaign activities....”
“Adobe is demonstrating AI coworkers embedded across customer experience workflows....”
“Oracle is walking through role-based agents built into broader enterprise applications....”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
CMOs Can Shape AI to Boost B2B Buying Power
As B2B buying increasingly becomes machine-mediated, AI systems are conducting early vendor research and determining shortlists by reading structured signals (schema, integrations, certifications, pricing). The article argues CMOs can elevate marketing from promotion to infrastructure by shaping how AI evaluates vendors. Recommended actions include auditing structured metadata and schema markup, aligning marketing claims with verifiable documentation, participating in enterprise AI governance, tracking brand presence in AI-generated recommendations (an "AI shortlist share" metric), and standardizing category language across product, sales, and marketing. The piece frames marketing as responsible for consistent, machine-readable representation of product truth and buyer relevance to influence vendor selection before sales engagement.
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.
CMO Guide: Building an AI-First Marketing Team
This MarTech guide explains how CMOs can transform marketing teams into AI-first organizations by prioritizing process, practice, and measurable outcomes. The author argues that leadership must make AI adoption visible, pair every training with a real project, and remove operational blockers (licenses, tokens, messy processes). Recommended training includes three Anthropic courses (Claude 101; Introduction to Agent Skills; AI Fluency). The piece advocates creating internal AI champions, hiring consultants with marketing-stack experience, and formalizing successful experiments into reusable workflows and a marketing AI playbook. A detailed KPI scorecard is provided (e.g., 60% reduction in time on routine tasks, 70%+ weekly usage in pilot teams, 5x content volume without headcount growth), emphasizing that AI must tie to business impact rather than vanity metrics.
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