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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteGenerative AI creates the most value in marketing when it improves an end-to-end workflow—such as developing creative, tailoring campaigns, interpreting customer feedback, or reporting results—and the team measures a business outcome. Producing more copy or images is not, by itself, evidence of value.
Where does generative AI create the most value in marketing?
The clearest near-term opportunities are creative development and campaign work, with further potential in personalization, customer insight, planning, reporting, and repetitive service interactions. The right starting point is not “Where can we add a chatbot?” but “Which workflow has a measurable bottleneck or opportunity, and can AI improve it without adding unacceptable risk?”
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McKinsey’s survey of 1,491 respondents across 101 countries, fielded July 16–31, 2024 and published March 12, 2025, found that 71% said their organizations regularly used generative AI in at least one business function. Marketing and sales were among the functions most often cited. This is a survey response, not a census of companies or a measure of marketing return. McKinsey’s survey findings
Creative development and campaign execution
Marketing teams can use generative AI to develop or adapt campaign concepts, draft copy, create variants for different audiences, and support campaign planning. Gartner’s survey of 418 marketing leaders, fielded July–September 2024, found that among organizations adopting generative AI, 77% had adopted it for creative development. The figure was 84% among Gartner’s high-performing group. Strategy development adoption was 48% overall among adopters and 52% among the high-performing group. These figures describe adoption; they do not show that AI caused higher performance. Gartner’s survey release
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Creative assistance is most useful when a team can define the brief, audience, brand constraints, and approval process. A draft or concept is an input to the creative process, not automatically publishable work.
Personalization and campaign decisions
AI can help teams adapt messaging to segments and use response data to inform subsequent campaign decisions. In a case described by McKinsey, Michaels Stores used a content-generation and decision platform for campaign copy and segment learning. McKinsey reported that personalization increased from 20% to 95% of email campaigns, while SMS click-through rate rose 41% and email click-through rate rose 25%. McKinsey attributed these results to a Persado case article dated March 30, 2022. They are company case claims relayed by McKinsey, not independently audited estimates of what another marketer should expect. McKinsey’s marketing use-case analysis
Personalization depends on usable customer data as much as on generated content. Salesforce’s tenth State of Marketing page reports that it surveyed 4,500 marketing leaders worldwide and that 83% recognized a shift toward personalized, two-way messaging, while one in four were satisfied with how their organizations used data to support it. Salesforce is the survey publisher; the figures are not a measurement of AI’s effect. Salesforce’s tenth State of Marketing edition
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Customer insight and recommendations
Generative AI can help staff make sense of customer feedback and turn it into useful recommendations or ideas. McKinsey describes Stitch Fix using AI to help stylists interpret customer feedback and make product recommendations. It also describes Instacart using AI for recipes, meal planning, and shopping lists. These examples illustrate possible applications; the cited article does not establish a comparable, controlled result for either company.
Campaign evaluation, reporting, and planning
AI can support the work of reviewing campaign results, organizing findings, and preparing reports or planning inputs. Gartner reported that 47% of surveyed marketing leaders saw a large benefit from AI for campaign evaluation and reporting. The release does not specify that this benefit was caused by generative AI alone, so treat it as a reported perception rather than a measured return.
Repetitive customer interactions and process steps
For bounded service workflows, AI may retrieve information from back-end systems, take defined actions, or draft replies in a brand’s voice for an agent to review. McKinsey reports that an unnamed direct-to-consumer retailer achieved an over-80% decrease in time to first response and a four-minute reduction in average resolution time. These are case-study figures reported by McKinsey, not general benchmarks or guaranteed savings.
Finding market and product opportunities
AI can help teams explore concepts and organize signals from trends, comments, or media. McKinsey cites Mattel generating four times as many Hot Wheels concept images, Kellogg’s scanning recipe trends for social campaign ideas, and L’Oréal analyzing online comments and media for product opportunities. These are illustrative company examples, not controlled comparisons showing that AI alone produced a business outcome.
How should a marketing team choose what to automate or augment first?
Start with two or three use cases, not an organization-wide rollout. McKinsey recommends focusing on two or three use cases where off-the-shelf generative AI tools can provide immediate impact in priority areas, then learning before moving toward customized solutions or broader process transformation. McKinsey’s marketing use-case analysis
Compare candidate workflows against the criteria below. A promising use case has a defined outcome, a manageable integration path, and a level of risk the team can govern.
| Question | What to establish |
|---|---|
| What outcome matters? | Specify the intended change: conversion, engagement, speed, quality, service, or better customer insight. |
| Is the task bounded? | Identify the inputs, AI task, existing systems, human handoffs, and what happens when the output is incomplete or wrong. |
| Will the economics work? | Estimate time or cost saved and compare it with review, integration, training, and change-management effort. |
| Does it improve quality or relevance? | Measure whether customers receive more useful or appropriate communication—not merely whether the team produces more assets. |
| Can the team measure it? | Set a baseline, name a KPI owner, specify the measurement period, and account for other factors that may affect the result. |
| What could go wrong? | Assess privacy, factual accuracy, bias, intellectual-property exposure, and the consequences of a mistaken output. |
Favor tasks where staff can verify the output and where mistakes are recoverable. A workflow that touches sensitive data, makes consequential decisions, or sends unreviewed claims directly to customers needs stronger controls than internal brainstorming.
How can a team measure the return on generative AI?
Measure the workflow change and its intended outcome, not access to a model or the volume of generated material. Before a pilot, record the current process and baseline; then compare the same KPI after introducing AI, while documenting changes in staffing, spend, audience, or campaign conditions that could also explain a difference.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →- For creative work: track review time, revision cycles, on-brand quality, and whether useful assets reach launch—not just drafts created.
- For campaigns: choose an outcome such as conversion or engagement, define the audience and comparison, and separate the effect of AI-assisted content from other campaign changes where possible.
- For reporting: measure time to produce a report and whether its findings are accurate and actionable.
- For service workflows: measure response and resolution times alongside answer quality and customer impact.
- For personalization: assess whether relevant segments receive appropriate messages and whether engagement changes, while respecting consent and data-use rules.
McKinsey’s 2025 survey found that more than 80% of respondents said their organizations were not seeing a tangible enterprise-level EBIT impact from generative AI; 17% said at least 5% of organizational EBIT in the previous 12 months was attributable to it. These are respondent reports, not audited financial results. In the same survey, 21% of respondents at organizations using generative AI said at least some workflows had been fundamentally redesigned. McKinsey found workflow redesign had the biggest effect among 25 tested organizational attributes on reported ability to see EBIT impact, and tracking well-defined KPIs was the practice with the most impact on the bottom line among 12 adoption and scaling practices examined. These analyses are associations, not proof of a fixed return from any one intervention. McKinsey’s survey findings
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What risks need controls in customer-facing marketing?
Generative AI can produce inaccurate statements, reflect bias, expose private data, or create copyright and intellectual-property problems. McKinsey cautions that it is typically poorly suited to high-stakes decisions, regulated environments, heavy-volume requests, and numerical reasoning. Customer-facing uses therefore need accountable ownership, technical oversight, and human review appropriate to the consequence and exposure of the output. Review can catch errors, but it does not eliminate risk. McKinsey’s marketing use-case analysis
Review coverage is not automatic. In McKinsey’s 2025 survey, 27% of respondents at organizations using generative AI said employees review all AI-created content before use, while a similar share said 20% or less is checked. The survey also found organizations reporting mitigation of inaccuracy, cybersecurity, intellectual-property, and privacy risks. McKinsey’s survey findings
- Decide which outputs may be used, by whom, and with what review before piloting.
- Keep sensitive or identifying customer information out of tools unless the organization has approved the specific data handling and access controls.
- Require verification of factual claims, offers, product details, and numerical content before publication.
- Document responsibility for approvals, escalations, and correcting a customer-facing mistake.
- Reassess the controls when the workflow, model, data, or audience changes.
What does the evidence say about adoption versus value?
Generative AI is widely used, but adoption alone does not establish financial impact. Gartner’s February 2025 release reported that 27% of CMOs said their marketing organization had limited or no generative AI adoption in campaigns, and quoted concerns that investments had yet to pay off. That survey was fielded in July–September 2024, so it is a dated snapshot rather than a measure of 2026 adoption. Gartner’s survey release
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The practical implication is to treat implementation as workflow design: choose a business problem, fit the tool into real systems and handoffs, set a KPI, and decide how people will check the work. Without those pieces, a rise in AI usage or content output may not translate into better marketing performance.
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