Generative AI (GenAI)

Definition

Generative AI (GenAI) is the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content (for example, images, video, audio, text, and other digital content). In practice, GenAI systems generate outputs from inputs such as prompts, reference assets, and/or retrieved documents, producing content that can be edited, summarized, transformed, or extended.

How it relates to marketing

GenAI is used in marketing to accelerate content production and adapt messaging to channels, audiences, and contexts while maintaining governance controls. Common marketing applications include:

• Content creation and adaptation: first drafts for emails, landing pages, ads, product descriptions, and social posts; rewriting for tone, length, or reading level
• Creative operations: generating image variations, background removal/expansion, and format resizing for channels (where supported by the model type)
• Conversation and support: chat experiences for product discovery, order status, and FAQ-style support, often grounded in approved content via retrieval approaches Amazon Web Services, Inc.+1
• Research assistance: summarizing research notes, competitive intel, and customer feedback themes (with citations/links where your system supports them)
• Experimentation: rapid variant generation for A/B tests (copy, subject lines, CTAs), paired with performance measurement

How to calculate (the term)

GenAI is not a single metric, but teams commonly “calculate” its impact and operational footprint using a few standard measurements:

• Unit economics (token-based LLM usage)
  • If your provider prices by tokens, a per-request cost model is often: $$\text{Cost} = \left(\frac{T_{in}}{1000}\right)\cdot P_{in} + \left(\frac{T_{out}}{1000}\right)\cdot P_{out}$$Cost=(1000Tin​​)⋅Pin​+(1000Tout​​)⋅Pout​ where $T_{in}$Tin​ and $T_{out}$Tout​ are input/output tokens, and $P_{in}$Pin​, $P_{out}$Pout​ are provider prices per 1,000 tokens (or equivalent).
• Quality and reliability
  • Task-level acceptance rate (human-approved / generated)
  • Factuality/grounding rates for knowledge tasks (often evaluated with a retrieved source set in RAG-style architectures) Amazon Web Services, Inc.+1
  • Brand compliance rate (style guide checks passed / total)
• Business impact
  • Lift on downstream KPIs (CTR, CVR, revenue per send, time-to-publish, cost per asset), measured through controlled experiments where possible

How to utilize (the term)

Common GenAI use cases in marketing operations and execution include:

• Drafting and editing workflows
  • Use GenAI to draft copy; route through review for legal, brand, and claims substantiation; store approved variants for reuse.
• Content repurposing
  • Transform a source asset (webinar transcript, blog post) into channel-specific outputs (email copy, social snippets, landing-page sections).
• Customer-facing assistants with grounding
  • Use retrieval-augmented generation (RAG) patterns to ground answers in approved documentation rather than relying on model memory alone. Amazon Web Services, Inc.+1
• Multimodal creative generation
  • For images, many systems use diffusion-model approaches (noise added in a forward process, then removed in a reverse process to generate an image). IBM+1
• Internal enablement
  • Generate enablement collateral drafts (battlecards, messaging frameworks) and maintain version control with approval gates.

Compare to similar approaches, tactics, etc.

Approach	Primary output	Strengths	Limitations	Typical marketing fit
Generative AI	New synthetic content (text/images/audio/video/code) NIST Computer Security Resource Center+1	High variation capacity; fast iteration; supports transformation tasks	Requires governance for accuracy, IP, claims, and brand voice	Drafting, adaptation, creative variants, assistants
Predictive ML	Scores/forecasts (propensity, CLV, churn)	Supports targeting and optimization decisions	Does not create customer-facing content	Segmentation, bidding signals, next-best-action inputs
Rule-based templates	Parameterized content	Highly controlled; easy to govern	Limited variation; manual upkeep	Transactional comms, regulated messaging
Human-only production	Human-created content	High contextual judgment	Slower throughput; higher marginal cost	High-stakes launches, flagship creative, final approvals
Retrieval-based search	Retrieved documents/snippets	Source-linked answers; strong governance	Does not “compose” new content beyond excerpts	Knowledge lookups, policy/FAQ referencing

Best practices

• Define permitted use cases and guardrails
  • Separate “marketing draft support” from “customer-facing automation,” with different risk controls.
• Ground high-stakes outputs
  • For product, pricing, policy, medical/legal/financial claims, use retrieval against approved sources and require human review. Amazon Web Services, Inc.+1
• Establish a content governance workflow
  • Prompt standards, brand voice rules, claims substantiation checks, and approval logs.
• Control data exposure
  • Classify data allowed in prompts; limit sensitive inputs; apply redaction where needed.
• Measure quality continuously
  • Track acceptance rate, defect types (factual errors, tone violations, disallowed claims), and segment performance.
• Version prompts and policies
  • Treat prompts, system instructions, and RAG corpora like production assets with change control.
• Use a risk management framework
  • Map GenAI risks (content integrity, security, privacy, downstream harm) to controls and testing expectations; NIST provides GenAI-specific guidance via its profile companion to the AI RMF. NIST+1

Future trends

• More multimodal marketing systems
  • Integrated text + image + audio/video generation and editing pipelines, reducing handoffs between tools.
• Agentic workflows
  • Narrow, supervised agents that assemble multi-step tasks (research → draft → QA checks → handoff), with audit trails.
• Stronger provenance and authenticity signals
  • Increased use of content authenticity metadata and policy enforcement for synthetic media.
• Higher emphasis on evaluation
  • Standardized quality testing for brand compliance, factuality, and safety as part of routine release processes.
• Model and data localization
  • More options for private deployments, smaller specialized models, and tighter integration with enterprise knowledge bases (often via retrieval patterns). Amazon Web Services, Inc.+1

Related Terms

• Artificial intelligence (AI)
• Data Science
• Citizen Data Scientist
• Data Science for Marketers
• (4) Principles of Explainable AI
• Large Language Models (LLM)
• Prompt engineering
• Retrieval-Augmented Generation (RAG)
• Fine-tuning
• Embeddings
• Vector database
• AI governance
• AI Development Lifecycle
• Supervised learning
• Unsupervised learning
• Feature engineering
• Training data
• Labels (target variable)
• Loss function
• Model evaluation
• Overfitting
• Underfitting
• Concept drift
• Population Stability Index (PSI)
• Machine Learning (ML)
• Machine Learning Operations (MLOps)
• Generative AI
• Data Science
• Citizen Data Scientist

References

Autio, C., Schwartz, R., Dunietz, J., Jain, S., Stanley, M., Tabassi, E., Hall, P., & Roberts, K. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile (NIST AI 600-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.600-1

Booth, H., Souppaya, M., Vassilev, A., Ogata, M., Stanley, M., & Scarfone, K. (2024). Secure software development practices for generative AI and dual-use foundation models: An SSDF community profile (NIST SP 800-218A). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-218A

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33. https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33. https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html

National Institute of Standards and Technology. (n.d.). Generative artificial intelligence. Computer Security Resource Center Glossary. Retrieved January 11, 2026, from https://csrc.nist.gov/glossary/term/generative_artificial_intelligence

Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1