Why your head of marketing should be your head of AI
AI creates the most business value where customers feel it. That's the app layer: chatbots, on-site search, product recommendations, emails, and video. There's no room for errors there. One glitch can dent trust and revenue in a single afternoon.
IT owns the plumbing, and that matters. But marketing owns the places where AI meets the customer and the brand. That's why the CMO is the natural AI lead. If you steer customer experience, you should steer the AI that shapes it.
Where AI really gets tested: the app layer
Background automation is valuable, but it's backstage. The spotlight is on customer-facing moments. Your AI chatbot, your dynamic content, your triggered emails - that's where judgment comes fast.
Marketing already works with high scrutiny and fast feedback loops. That makes it the best stress test for AI. If it holds up in marketing, you can transfer the playbook to sales, service, and beyond.
Marketing owns the data that makes AI work
AI lives and dies by data quality. The martech stack holds behavioral data, preferences, content, and performance metrics. That's the fuel and the feedback loop.
There's also pressure from both sides. Customers expect speed and relevance. Finance expects lower costs and fewer handoffs. Marketing sits in the middle - and has to get it right.
The new CMO profile
Creative instinct still matters, but it's not enough. The job now demands someone who understands models, data pipelines, and risk - and can explain ROI in plain numbers. Think product mindset, not campaign mindset.
That doesn't mean doing it alone. Build a bench: AI product managers, marketing ops, data analysts, and trusted partners. Your job is to set the guardrails and the bar for customer experience.
A 90-day AI plan for CMOs
- Days 0-15: Pick three use cases with clear value: support deflection with an AI assistant, email subject line and body testing, on-site search with grounded answers. Define success metrics and risk boundaries.
- Days 16-45: Run controlled pilots. Human-in-the-loop review. Set up A/B tests. Add content and data sources that are approved and auditable. Log every interaction.
- Days 46-90: Tune prompts and retrieval. Ship playbooks for brand voice, compliance, and escalation. Publish the results to the board with cost, revenue, and CX impact.
Metrics that prove the case
- Customer: CSAT, NPS, conversion rate, first contact resolution, bounce rate.
- Efficiency: time-to-first-draft, review cycle time, cost per ticket, cost per asset.
- Growth: CAC, LTV, qualified pipeline from AI-assisted programs.
- Risk: hallucination rate, brand incident count, PII exposure incidents.
Guardrails you need on day one
- Ground answers in approved knowledge. No free-text answers without citations.
- PII redaction, data retention limits, and access controls by role.
- Disclosure policies for AI-assisted content and chat.
- Brand voice rules baked into prompts and review workflows.
- Accessibility checks for all customer-facing outputs (follow WCAG).
- Adopt an AI risk framework to keep everyone aligned (see the NIST AI RMF).
Your AI operating model (simple and clear)
- CMO: Owns AI use cases that touch the customer, experience standards, and ROI.
- CIO/CTO: Owns platforms, integrations, model access, and performance SLAs.
- CISO/Legal: Owns security, privacy, licensing, and review policies.
- Data Lead: Owns taxonomy, data quality, and feedback loops.
- AI Product Manager (in Marketing): Prioritizes the backlog, ships pilots, measures impact.
High-value use cases to start with
- AI support assistant: Deflect common tickets with grounded responses and handoff rules.
- Email and ad creative: Multi-variant generation with strict voice and compliance checks.
- On-site search and recommendations: Retrieval-augmented answers tied to your content and catalog.
- Sales prospecting assist: Drafts, enriches accounts, and sequences - reviewed by reps before send.
- Workflow automation: Brief-to-draft-to-approve pipelines with auto-tagging and CMS updates.
Vendor selection checklist
- Grounding and citation support, not just free-form generation.
- PII handling, SOC 2/ISO attestations, and content moderation controls.
- Experimentation features: A/B testing, prompt/version control, and analytics.
- APIs for your CDP, CRM, CMS, and ad platforms.
- Transparent pricing tied to usage and clear rate limits.
How to talk to the CFO
Lead with numbers, not promises. "We'll cut time-to-first-draft by 60%, lift email CTR by 8-12%, and deflect 25% of support contacts - with guardrails that cap risk." That's a conversation that gets funded.
Show pilot results, not projections. Then scale what works and shut down what doesn't. Simple.
Reduce risk without slowing down
- Human review on anything public until metrics prove stability.
- Post-incident reviews and prompt updates within 48 hours.
- Content watermarking/logging to trace issues fast.
- Regular audits of datasets, prompts, and outputs against your standards.
Train your team, then standardize
Upskill your marketers on prompts, grounding, and experiment design. Give them a shared playbook and a safe sandbox. The goal: speed with control.
If you want a structured path, see our AI certification for marketing specialists or browse courses by job for focused upskilling.
The bottom line
AI's promise shows up where customers interact with your brand. That's marketing's backyard. Put the CMO in charge of AI for all customer-facing work, give them the right partners, and measure everything.
Do that, and you'll ship better experiences, spend less, and build a playbook the rest of the company can use.
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