A Practical Playbook for CMOs Navigating the Generative AI Wave
Published July 2023, this white paper sets out to help CMOs translate generative AI hype into measurable marketing results. IBM expects a 4× increase in generative AI investment between 2023 and 2025. McKinsey reports that 40% of enterprise leaders are already using it at least sometimes. Goldman Sachs estimates generative AI will drive a 7% increase in global GDP and lift global productivity by 1.5% over the next decade. For marketing — the function identified by BCG as one of the fastest adopters, with 70% of global CMOs already on board — the question is no longer whether to invest but how to invest without getting stuck between peak hype and disappointing returns.
Authored by Vipul Vyas (Persado VP, Go-to-Market Strategy), Alex Olesen (VP, Vertical Strategy and Product Marketing), and Jodieann Stephenson (Senior Manager, Content Writer, Go-to-Market Strategy). Draws on data from Gartner, McKinsey, BCG, IBM, Goldman Sachs, PwC, and the National Bureau of Economic Research.
What the Data Says About Generative AI in Marketing Today
The paper assembles external evidence and internal Persado benchmarks to frame where generative AI is already working, where the hype is outrunning reality, and where CMOs should focus.
- Adoption is fast and uneven: 70% of global CMOs are already adopting generative AI (BCG), but Gartner still places generative AI near the Peak of Inflated Expectations on its 2022 Hype Cycle. Mainstream adoption is expected within two to five years.
- Synthetic content is about to dominate: Gartner predicts that by 2025, 30% of outbound marketing messages will be synthetically generated — up from less than 2% in 2022.
- Productivity gains are real: The National Bureau of Economic Research found generative AI boosts worker productivity by 14%. More than half of respondents to PwC's 26th Annual Global CEO Survey named labour shortages as a 10-year survival threat, making efficiency a strategic concern rather than a cost line.
- Marketing budgets have not grown: Marketing budgets have barely returned to pre-pandemic levels of roughly 10% of revenue, spread across paid media (25.6%), martech (25.4%), labour (24.6%), and agencies (23.3%) per Gartner 2023. The expectation is that CMOs deliver more growth from the same allocation.
- Generic LLMs have real limits: Hallucinations, outdated training data, and brand-voice drift are recurring risks. Meta's Galactica, trained on medical-journal content, was shut down within days after it mixed current evidence with debunked findings in its outputs.
Invest Against Efficiency and Effectiveness — Not Volume
The paper's central recommendation for CMOs is to separate two questions. First, where will generative AI let my team do more with the same budget? That is an efficiency play — automating manual work, producing derivative outputs, getting first drafts on the page faster. Second, where will generative AI make the work itself produce better results? That is an effectiveness play — generating message variants proven to motivate real customers, optimising for conversion rather than for volume.
CMOs who invest only on the efficiency side risk a familiar failure mode: more content, same results, and a flood of lookalike outputs as competitors use the same off-the-shelf tools. Effectiveness gains require a different class of technology — one trained on business-specific language and customer motivation data rather than the open internet, with experimental design and brand-voice controls built in. The practical implication is to concentrate investment on marketing's biggest growth levers (campaign creation, brand strategy, conversion optimisation) and to choose AI partners whose outputs can be measured against the same KPIs every other marketing dollar is measured against.
Download the full white paper for the complete breakdown of use cases, vendor-selection criteria, and implementation frameworks.
