Marketers say AI aspirations get stuck in "pilot purgatory"
Marketing teams are feeling the squeeze. A new report from Typeface shows a widening gap between AI goals and execution, driven by rising content demand and slow scaling beyond tests.
The survey of 200 U.S. marketers signals a clear message: experimentation isn't enough. Teams need production-grade workflows, shared platforms, and measurable outcomes to meet expectations.
The execution gap, in numbers
- 95% of leaders report rising content demand, with an 81-point gap between demand and what teams feel they can deliver.
- 47% cannot deliver true campaign personalization; only 14% feel fully confident they can keep up.
- AI users are 4x more likely to keep up with content demand (66% vs. 14% of non-users).
- 27% of AI users can launch campaigns in two weeks or less - a pace non-users can't match.
- 69% of campaigns take three to four weeks, while 85% of leaders want a one-to-two-week turnaround.
- While 82% use AI for campaigns, 82% of those users remain stuck in pilots. 61% use AI mainly at the individual level, not on collaborative platforms.
Why pilots stall
- No clear business owner or roadmap beyond experimentation.
- Tools live in silos; outputs aren't integrated into DAM, CMS, or MAP.
- Unclear guardrails around brand, compliance, and approvals.
- Limited access to data, templates, and reusable prompts.
- Training focused on curiosity, not repeatable workflows and KPIs.
A 90-day plan to move from pilots to production
- Weeks 0-2: Pick two high-leverage use cases (e.g., ad variants, lifecycle emails). Define owners, SLA, and success metrics.
- Weeks 2-4: Build a prompt and template library. Connect AI outputs to your DAM and campaign tools. Set review gates.
- Weeks 4-6: Launch controlled runs. Compare cycle time, cost per asset, and engagement vs. your baseline.
- Weeks 6-8: Add brand and legal guardrails. Document QA steps and escalation paths.
- Weeks 8-12: Scale to one more use case. Publish a playbook and roll out team training.
Team and workflow shifts that actually work
- Appoint an AI program owner with a shared backlog and clear intake.
- Move from individual tools to a collaborative platform with roles and audit trails.
- Create reusable assets: prompts, tone guides, offer frameworks, and component blocks.
- Stand up a lightweight review layer: brand, legal, accessibility.
- Run weekly enablement: 30-minute reviews of wins, failures, and updates to the playbook.
Metrics that matter
- Cycle time per asset and per campaign.
- Personalization coverage by segment and channel.
- Quality pass rate at first review.
- Compliance and brand adherence.
- Cost per asset and production hours saved.
- Campaign velocity: concept-to-launch time.
Tooling principles
- Integrate with DAM, CMS, and MAP; avoid copy-paste loops.
- Use SSO, role-based permissions, and content provenance tracking.
- Human-in-the-loop review for regulated categories.
- Version control on prompts and templates; test before rollout.
- Document known failure modes and red-team sensitive prompts.
Quick wins you can ship this quarter
- Channel-ready variants for ads, social, and lifecycle emails.
- Subject line and CTA testing at scale with control groups.
- Product descriptions and image variations mapped to segments.
- On-brand landing page blocks generated from approved components.
If your team needs structured enablement and job-specific playbooks, explore the AI Certification for Marketing Specialists or browse courses by job at Complete AI Training.
For report context and definitions, visit Typeface.
