library / lib9f491611b9e89eea
Marketing Artificial Intelligence: AI, Marketing, and the Future of Business
Paul Roetzer, Mike Kaput · 2022
In a sentence
A practical, nontechnical guide showing marketers how to understand, pilot, and scale artificial intelligence to make their marketing smarter, more efficient, and more human.
Marketing Artificial Intelligence demystifies AI for the nontechnical marketer, defining it simply as 'the science of making marketing smart.' Drawing on years of research, dozens of interviews with AI executives and engineers, and the authors' experience building Marketing AI Institute, the book explains the core technologies (machine learning, deep learning, NLP/NLG, computer vision) through three approachable categories—language, vision, and prediction. It then walks readers through how to evaluate AI vendors using the Marketer-to-Machine Scale, how to identify and prioritize use cases via the 5Ps framework, and how to apply AI across ten marketing disciplines (advertising, analytics, PR, content, customer service, ecommerce, email, sales, SEO, and social). With real-world examples, vendor spotlights, and a clear-eyed look at bias, ethics, and responsible AI, the book argues that AI will not replace marketers but will augment them—freeing humans to focus on creativity, empathy, and strategy while machines handle data-driven, repetitive tasks. The result is a blueprint for becoming a 'next-gen marketer' who builds a sustained competitive advantage.
The four lenses
- Science
- Statistics
- Systems
- Strategy
The model
A causal framework in which organizational design levers and contextual conditions (AI understanding, data foundation, vendor selection, leadership support, responsible-AI practices) drive intelligent automation adoption and psychological/behavioral states (human trust, mutual human-machine learning), which in turn produce marketing outcomes (cost reduction, revenue growth, personalization at scale, competitive advantage, more-human brand).
AI Understanding and Educationdesign lever
The degree to which marketers and their organizations possess baseline knowledge of what AI is and what it can do, supported by formal education and training programs that enable identifying use cases and evaluating smarter technology.
Data Foundation and Strategycontextual condition
The availability, quality, structure, governance, and strategic management of first-party and third-party data that AI systems require to learn, make accurate predictions, and continually improve over time.
Vendor Selection and Technology Fitdesign lever
The marketer's ability to find, vet, and select AI-powered technology that genuinely fits business needs based on intelligent-automation level, integration, transparency, and value, rather than overhyped branding.
Use Case Identification and Prioritizationdesign lever
The systematic process of identifying, rating (by value and ability to intelligently automate), and prioritizing narrowly defined, data-driven, repetitive, predictive marketing tasks for AI pilots and scaling.
Leadership Education and Supportcontextual condition
The extent to which executives and the C-suite understand AI, buy into its value, tolerate early pilot failures as investments, and provide resources for talent, technology, and strategy transformation.
Intelligent Automation Adoptionbehavioral pattern
The degree to which an organization has piloted and scaled AI-powered technologies that perform marketing tasks at varying Marketer-to-Machine levels, moving from manual processes toward machine-assisted and machine-led work.
Human-Machine Mutual Learningpsychological state
The systematic, continuous process by which humans and AI systems learn from each other through multiple interaction modes, with organizations changing processes to learn with the machines rather than merely deploying isolated applications.
Human Trust and Willingness to Adoptpsychological state
The psychological state of marketers' enthusiasm rather than fear toward AI, their trust in AI recommendations, and their willingness to give AI time to learn and to oversee/train it rather than reverting to old systems.
Responsible AI and Ethics Practicesdesign lever
The structures, processes, and policies that ensure AI is used ethically and without harmful bias, addressing accountability, transparency, fairness, privacy, security, and human-centered design across the AI lifecycle.
Cost Reductionoutcome metric
The financial outcome of reducing marketing costs by intelligently automating repetitive, time-intensive, data-driven tasks, lowering the resources required to execute marketing programs at scale.
Revenue Growth and ROIoutcome metric
The financial outcome of accelerating revenue and improving return on investment through better predictions, personalization, lead conversion, and targeting enabled by AI across marketing programs.
Personalized Consumer Experiences at Scaleoutcome metric
The outcome of delivering individualized content, offers, recommendations, and experiences to consumers in real time across channels at a scale impossible for humans, improving engagement and satisfaction.
Sustained Competitive Advantageoutcome metric
The strategic outcome whereby early movers who infuse AI into talent, technology, and strategy build a compounding, hard-to-replicate advantage through superior insights, speed, and efficiency over peers.
How they connect
- ai understanding education → predicts intelligent automation adoption
- ai understanding education → influences vendor selection quality
- ai understanding education → influences human trust adoption will
- use case prioritization → predicts intelligent automation adoption
- vendor selection quality → predicts intelligent automation adoption
- leadership support → moderates intelligent automation adoption
- data foundation → moderates intelligent automation adoption
- intelligent automation adoption → predicts human machine mutual learning
- human machine mutual learning → predicts competitive advantage
- intelligent automation adoption → predicts cost reduction
- intelligent automation adoption → predicts revenue growth
- intelligent automation adoption → predicts personalization at scale
- cost reduction → predicts competitive advantage
- revenue growth → predicts competitive advantage
- human trust adoption will → moderates intelligent automation adoption
- responsible ai practices → moderates personalization at scale
- responsible ai practices → influences competitive advantage
A candidate measure
Marketing Artificial Intelligence: AI, Marketing, and the Future of Business — derived measurement candidates
AI Understanding and Education
% staff trained in AI; self-rated AI competency; number of certifications earned
self-report suitability: high
Data Foundation and Strategy
data quality scores; data governance maturity rating; first-party data coverage
self-report suitability: medium
Vendor Selection and Technology Fit
vendor assessment scores; M2M level achieved; integration success rate
self-report suitability: medium
Use Case Identification and Prioritization
AI Score total; value/ability ratings; number of prioritized use cases
self-report suitability: high
Leadership Education and Support
AI budget allocation; executive participation rate; presence of AI strategy
self-report suitability: high
Intelligent Automation Adoption
count of AI use cases in production; share of automated tasks; M2M level distribution
self-report suitability: medium
Human-Machine Mutual Learning
number of interaction modes; frequency of process change; documented learning loops
self-report suitability: medium
Human Trust and Willingness to Adopt
AI recommendation acceptance rate; reported fear level; willingness-to-adopt ratings
self-report suitability: high
Responsible AI and Ethics Practices
responsible-AI maturity score (7 dimensions); presence of ethics committee; number of bias audits
self-report suitability: medium
Cost Reduction
dollars saved; hours saved per month; spend efficiency ratio
self-report suitability: low
Revenue Growth and ROI
ROI %; return on ad spend; conversion rate lift; revenue attributable to AI
self-report suitability: low
Personalized Consumer Experiences at Scale
recommendation acceptance rate; engagement uplift; customer satisfaction score
self-report suitability: medium
Sustained Competitive Advantage
relative growth vs peers; market share change; speed-to-insight
self-report suitability: low
The story
The reader A nontechnical marketer (entry-level to CMO) who wants to stay relevant, build a competitive advantage, and advance their career in an industry being transformed by AI.
External problem
Marketers struggle to understand what AI is, how to assess AI vendors, and how to apply AI to reduce costs and accelerate revenue.
Internal problem
They feel overwhelmed, uncertain, and fearful of being left behind or replaced as technology accelerates.
Philosophical problem
Traditional all-human, all-manual marketing is no longer enough; it's wrong to perform below your potential when smarter technology can free you to do uniquely human work.
The plan
- Learn what AI is via the language, vision, and prediction categories.
- Use the Marketer-to-Machine Scale to assess and vet AI vendors.
- Identify and prioritize use cases with the 5Ps framework and AI Score tool.
- Run quick-win pilot projects that are data-driven, repetitive, and predictive.
- Apply AI across your marketing discipline using proven use cases and vendors.
- Scale AI strategically with data foundations, education, and leadership support, responsibly and ethically.
Success
- Accelerated revenue growth and lower costs through intelligent automation.
- Personalized consumer experiences delivered at scale.
- More time freed for strategy, creativity, and empathy—the uniquely human work.
- A sustained competitive advantage as an early-mover 'next-gen marketer.'
At stake
- Becoming obsolete as peers who adopt AI compound their advantage and leave you behind.
- Wasting money on overhyped tools you don't understand.
- Falling further below your potential and burning out by working harder instead of smarter.
- Damaging your brand through biased, unethical, or manipulative AI use.
Related in the library