Step 1: Workflows
About
The GenAI Growth Framework for Consumer Companies
How to turn experimentation into scalable, measurable growth.
Most companies are testing GenAI — few are scaling it.
This framework shows how to move from pilots to performance by building efficiency, effectiveness, and differentiation across the customer journey.
How GenAI Drives Consumer Growth
Every successful use case ladders up to one of three growth levers.
Efficiency
Automate high-effort workflows to reduce time and cost.
Effectiveness
Use context and data to improve quality, conversion and customer resonance.
Expansion
Unlock new products, audiences, and markets through scale and personalization.
Bottom line:
Every GenAI initiative should move at least one of these levers — driving measurable improvement in acquisition cost, payback speed, and lifetime value.
GenAI Is Evolving Faster Than Organizations Can Absorb.
The tools are moving faster than the teams.
  • New models and apps launch weekly, each promising transformation.
  • Most companies respond with scattered pilots and siloed experiments.
  • The result: inconsistent results, duplicated effort, and fatigue.
Bottom line:
The problem isn't a lack of tools, it's a lack of structure.
The GenAI Reality Check: High Experimentation, Low Impact
90% of companies are using GenAI, 62% report their initiatives are stalled or struggling¹
"Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach" - MIT: The State of AI In Business¹
¹ MIT, "The Gen AI Divide: State of AI In Business 2025"
The Central Question
How can consumer companies move from scattered GenAI experiments to scalable, measurable business performance?
Why The Current Approach Fails
  • Tools chosen without a workflow fit
  • No baselines → can't prove ROI
  • No context → "AI slop"
  • No organizational redesign and low employee trust
The 5-Step Framework: From Pilots to Performance
01
Choose the Right Workflow
Focus on high-impact, measurable workflows.
02
Benchmark Performance
Establish ROI baselines.
03
Architect Your Stack
Match tools to business maturity.
04
Redesign Workflows
Optimize for human-AI collaboration.
05
Drive Adoption
Build organizational trust and capability.
Step 1 — Choose the Right Workflow for GenAI
Start where GenAI can amplify human creativity or analysis—not replace it.
Focus on high-value, judgment-based workflows tied to measurable business outcomes
What to Target
Workflows with:
High manual effort and creative or analytical bottlenecks
Clear connection to a growth metric (acquisition cost, payback speed, and lifetime value)
Human judgment where quality directly impacts results.
Where GenAI Consistently Drives Consumer Growth
Strategy
Customer Research | Competitive Analysis | Trend & Social Listening | Persona Development | Ad Performance Analysis
Creative Ideation & Production
Ad Concepting | Copywriting | Scriptwriting | Voiceover | AI Video & Image Generation
Customer Engagement
Chatbots | Lifecycle Marketing Automation | Personalization
Case Study: Picking the Right Workflow Unlocks Measurable Impact.
How Michaels Focused GenAI on One Judgment-Driven Workflow and Transformed Retention
The Challenge
Michaels served diverse customer segments (quilters, painters, DIY decorators, etc.) but personalized only 20% of emails. The team was overwhelmed by data and creative volume, leading to generic messages that didn't reflect individual crafting interests and flat engagement.²
The Solution
Michaels applied GenAI to a high-effort, judgment-based workflow focused on personalized email copy generation that directly supported retention goals. The system analyzed purchase and browsing data to generate individualized messages at scale, while marketers guided tone, themes, and overall quality.
The Results
Email personalization increased from 20% to 95%. click-through rates rose 25 - 40%, and customer retention improved significantly.²
² McKinsey, "How generative AI can boost consumer marketing"
Step 2 — Benchmark Efficiency and Effectiveness
You can't improve what you don't measure.
Establish baselines before GenAI implementation to prove ROI later.
Measure both efficiency and effectiveness from the start.
Efficiency (doing things faster/cheaper)
  • Time to launch
  • Production Cycle Time
  • Cost / Asset
  • CAC
  • CPM
Effectiveness (doing better/converting more)
  • Reach
  • CTR
  • Conversion Rate
  • Sales Velocity
  • Repurchase Rate
  • LTV
Why It Matters
Creates a baseline for GenAI's effect on the workflow/business
Proves ROI of your GenAI efforts after launch and helps you secure continued investment
Case Study: Baseline Metrics Make Impact Visible.
How Defining Benchmarks Turned Creative Scale Into Proven ROI at Opopop
The Challenge
As an omni-channel CPG startup competing across DTC, Amazon, and retail, we needed to scale performance ad production efficiently without sacrificing performance.
The Solution
Built "The Golden Kernel," a custom Claude copywriting and scriptwriting implementation that pulls from the context of our brand guidelines, top-performing ads, and domain expertise in creative strategy.
The Results
50%
faster time to launch new assets
30%
reduction in cost per asset
25%
decrease in customer acquisition cost
40%
increase in reach
³ Opopop Internal Data, Anthropic Implementation Case Study
Step 3 — Architect the Right GenAI Stack for Your Business
Match tools to your workflows, data maturity, and in-house technical expertise.
Most GenAI failures happen because teams adopt tools before architecting systems.
The right stack connects models, data, and people around measurable outcomes.
How to Architect Your Stack
01
Start With Outcomes, Not Features
  • Define business metrics first (e.g., lower CAC, faster creative cycle).
  • Select tools that can improve those outcomes.
02
Align Stack Depth to Maturity
  • Early-stage teams: Use AI-in-SaaS tools (ex: HubSpot, Canva, Shopify).
  • Scaling teams: Layer in general GenAI platforms (ex: ChatGPT, Claude) for flexible reasoning.
  • Advanced teams: Build context-engineered systems (custom projects, APIs, RAG pipelines) and GenAI Agents.
03
Design for Flexibility and Quality
  • Integrate retrieval and context grounding for accuracy.
  • Keep humans in the loop for brand voice and quality oversight.
The GenAI Ladder: From Models to Full Autonomy
Models
Definition: Base foundation LLM models that power every GenAI tool.
Closed Source: GPT-4, Claude, Gemini, Sora
Open Source: Llama, Mistral
Key Idea: The infrastructure layer for all downstream AI products.
GenAI-in-SaaS
Definition: Existing SaaS tools embedding GenAI to enhance productivity, personalization, or automation.
Examples: HubSpot (AI CRM), Canva (AI design), Shopify (AI commerce)
Key Idea: AI enhances existing workflows within established platforms.
General GenAI Tools
Definition: Platforms built entirely around a model's intelligence, the model is the product.
They provide open-ended reasoning, generation, and conversation capabilities across domains.
Examples: ChatGPT, Claude, Gemini, Grok
Key Idea: The model is the interface.
Specialized GenAI Tools
Definition: Purpose-built tools that apply foundation models to solve specific, high-value workflows using proprietary data, and context.
Examples: Gamma, Higgsfield, ElevenLabs, Context-Engineered Gen AI Systems (ex: Claude/ChatGPT Project)
Key Idea: Turns general AI into proprietary differentiated intelligence.
GenAI Agents
Definition: Autonomous or semi-autonomous systems that can reason, plan, and act toward goals across tools and data sources.
Examples: Typically custom-built for each company (e.g., "Creative Agent," "Lifecycle Marketing Agent," "Growth Ops Agent").
Key Idea: Turns GenAI from a co-pilot into an operator.

Maturity Curve:
Efficiency scales at Layers 1–2 → Effectiveness and differentiation scale at Layers 3–4 → Automation emerges at Layer 5.
Levels of Sophistication
Level 1: Prompt and Augment
Use GenAI to enhance human work, not replace it.
  • Quick wins and momentum come from human-in-the-loop prompting that improves quality and speed.
  • Tech and Tools: GenAI-in-SaaS, General GenAI Tools
Effective prompting combines role, task, context, and output.
Define clear roles
"Act as a growth analyst…"
Describe the task
"Help me develop 5 new personas…"
Provide Context
"Here are my current personas and why each one is successful…"
Output
"I need a ranked list of the 5 most effective new personas we should be targeting…"
Pro Tip: You don't need to start from scratch.
Ask GenAI to help you create the prompt before you start generating.
  • Use GenAI itself to create and refine your prompts.
  • You focus on insight and judgment; AI handles structure and phrasing.
Ex: "Help me write an effective prompt for developing new customer personas, including the ideal role, context, and output format."
Case Study: Prompting accelerates creative output and reduces cost.
A $2,000 Prompt-Driven Ad Achieved 20 Million Impressions in 2 Days
The Challenge
Prediction-market platform Kalshi received six and seven-figure quotes from production studios for an NBA Finals ad, timelines and budgets the startup couldn't justify.
The Solution
Hired GenAI filmmaker PJ Accetturo who used a human-in-the-loop prompting chain to turn an ad concept into a broadcast-ready ad:
  1. ChatGPT turned the core idea into a full script
  1. ChatGPT converted the script into a shot list
  1. ChatGPT refined the shot list into Veo 3-ready prompts
  1. A human editor curated 15 clips from 300+ generations into the final cut
The Results
95% cost reduction vs. traditional production, 20 million impressions across TV and online, 3+ million views on X within one week, became first fully AI-generated ad to air during major sporting event.⁴
⁴ DesignRush, "Kalshi's $2K NBA Finals AI Ad Shows Why Big-Budget Commercials Are Dying"
Level 2: Contextualize and Differentiate
Context engineering prevents "AI slop."
  • "AI slop" = generic, low-quality outputs that erode trust.
  • Ground GenAI in your brand, data, and domain expertise to ensure quality.
  • Tech and Tools: Specialized GenAI Tools
Competitive advantage comes from context, not code.
GenAI base models are trained on the internet — not your business.
Context engineering closes that gap by grounding models in your proprietary brand, domain, and performance data.
Curated context libraries turn GenAI from generic to on-brand, accurate and differentiated.
Design a Context Library That Teaches AI Your Brand
Curate and organize the proprietary knowledge that defines your brand, your domain, and your performance edge.
Brand Context
Feed GenAI the core materials that define your brand identity, audience, and proven creative patterns.
  • Products and unique value propositions
  • Buyer personas: who buys and why
  • Brand tone and vocabulary (words to use / avoid)
  • Scripts from top-performing ads
  • Tested formats and their performance results
  • Customer reviews and testimonials
Outcome: AI speaks in your brand's authentic tone and understands your audience.
Domain Context
Ground the model in expertise and category best practices.
  • Overview of your growth and marketing strategy
  • Popular ad formats
  • Guidelines on writing headlines, video scripts, and creative iterations
  • Frameworks and insights from experts on your team (or experts outside your team)
  • Competitor growth strategies and category benchmarks
Outcome: AI applies expert logic and industry best practices—not generic internet knowledge.
Performance Context
Teach the model what success looks like inside your business.
  • Top-performing ads, scripts, or campaigns (with performance data)
  • Annotated examples showing why they worked
  • Metrics such as CTR, engagement, or conversion rate
  • Insights from past creative testing and iterations
Outcome: AI learns from proven performance data to replicate high-impact growth patterns.
Operationalize Your Context Library
Treat your context library as a living system, not a static upload.
Quarterly Updates
Update quarterly with new winning assets and learnings
Cross-Team Usage
Use across teams for consistent, high-quality GenAI output
The more context you feed GenAI, the more it behaves like your brand's best highly-tenured employee—not a generic assistant.
Case Study: Context Engineering Prevents "AI Slop" and Strengthens Brand Authenticity
How Stitch Fix Engineered Context to Scale Personalization Without Losing the Human Touch
The Challenge
Stitch Fix needed to scale personalized styling for millions of clients without losing the warmth and individuality expressed in the "style recommendation notes" — the short, human-written messages that accompany each clothing box and explain the stylist's outfit choices.
The Solution
The team used context engineering to feed generative AI with stylist-written notes, customer profiles, purchase history, feedback, and trend data. This allowed AI models to draft high-quality "style notes" that mirrored Stitch Fix's brand voice and personalized rationale. Human stylists then reviewed, refined, and approved these AI drafts—preserving authenticity while increasing speed and scale.⁵
The Results
Reduced stylist writing time by over 50%, maintained engagement and satisfaction scores equal to fully human-written notes, and lifted average order value through more cohesive, data-informed outfit recommendations. Context-rich AI became an extension of the stylist, not a replacement.⁵
⁵ digitaldefynd, "25 Generative AI Case Studies"
Level 3: Automate and Scale
Move from projects to systems.
  • Automation compounds returns only after workflows and quality gates are proven.
  • Tech and Tools: Specialized AI Tools, GenAI Agents
From Projects to Systems
Shift GenAI from one-off experiments to scalable, repeatable workflows that compound returns.
Most teams start with GenAI projects, isolated pilots owned by individuals or small teams.
To scale impact, automation must evolve into systems of connected workflows with shared data, context, and quality controls.
Success depends on three enablers: reliable data, contextual grounding, and human review for quality control.
How to Automate and Scale GenAI Workflows
Identify repeatable processes, standardize quality gates, and automate one step at a time.
01
Identify Repeatable Workflows
Target high-volume, high-effort tasks (see top workflows here).
02
Standardize Inputs and Quality Checks
Use context libraries and templates to ensure consistency.
03
Automate the Middle Layer
Deploy Specialized GenAI Tools or custom GenAI Agents to handle generation and first-pass QA.
04
Keep Humans in the Loop
Maintain oversight for review, approval, and feedback to refine automation.
05
Measure and Iterate
Track efficiency (speed / cost) and effectiveness (quality / performance lift) to optimize the system.
Automate what's proven. Measure what matters. Scale what works.
Case Study: Automation Scaled Content Production Without Sacrificing Trust or Quality
How CarMax Used GenAI to Produce Thousands of Expert Reviews in Days Instead of Years
The Challenge
CarMax's digital team wanted to provide detailed, SEO-rich vehicle descriptions and reviews for its massive online inventory. Traditionally, creating expert-style summaries required content teams to research, write, and edit thousands of vehicle pages, a process that took years.
The Solution
CarMax deployed GenAI and grounded it in the company's proprietary database of expert vehicle research, specs, and customer insights.
The system automatically drafted vehicle overviews and buying guides at scale, while human editors reviewed, fact-checked, and approved final content. This automated workflow transformed what was once a bottleneck into a scalable content engine that continuously updated and expanded CarMax's library.⁶
The Results
  • Efficiency Gains: Automation multiplied CarMax's output without compromising accuracy or authenticity.⁶
  • Cost Reduction: Created more content in one day than human teams had produced in several years.⁶
  • Team Redeployment: Freed up creative and research teams to focus on higher-value strategic work.⁶
  • Effectiveness Improvement: Improved SEO performance and on-site engagement by publishing thousands of new, high-quality vehicle summaries grounded in verified data.⁶
⁶ CIO, "CarMax drives business value with GPT-3.5"
Step 4 — Redesign Workflows to Scale with GenAI
Reimagine workflows around GenAI—don't bolt AI onto old ones.
Scaling GenAI isn't about adding more tools — it's about rebuilding how work happens.
How to Redesign and Scale with GenAI
1
Redefine Human Roles
Clarify where people direct, curate, and improve AI outputs — shifting human effort toward creativity, strategy, and judgment.
2
Map Before-and-After Workflows
Document where GenAI reduces manual steps, shortens cycle time, or improves quality — quantify efficiency and effectiveness lift.
3
Redesign First, Then Scale
Automate only after your new workflow consistently produces quality results with human oversight.
4
Embed Feedback Loops
Build systems that learn — each workflow iteration should improve prompts, context, and creative output quality over time.
Why This Matters
Redesigning workflows compounds both efficiency and effectiveness.
Teams evolve from executors to AI directors.
Institutional knowledge is captured and continuously improved, not lost in isolated pilots.
Case Study: Scaling GenAI multiplied both reach and relevance.
How 148 AI-Generated Courses Became Duolingo's Most Effective Acquisition Channel
The Challenge
Paid acquisition costs were rising while organic growth required new language courses that took years to develop - capping the current total addressable market.
The Solution
Duolingo redesigned its content creation workflow around GenAI, compressing course production from years to weeks. Each of the 148 new language pairs became a searchable entry point, community flywheel, and viral "finally, my language!" moment, which turned product expansion into a marketing engine. ⁷,⁸
The Results
  • Efficiency: Built 148 courses in under a year (vs. years per course previously). ⁷,⁸
  • Effectiveness: Maintained educational quality through human-in-the-loop editing and cultural review. ⁷,⁸
  • Expansion: Expanded into previously unreachable markets and each new course became a growth flywheel. ⁷,⁸
  • Business Impact: Duolingo raised 2025 revenue guidance, citing GenAI-driven content scale as primary growth driver. ⁷,⁸
⁷ Duolingo, "Duolingo Launches 148 New Language Courses"
⁸ TechCrunch, "Duolingo launches 148 courses created with AI after sharing plans to replace contractors with AI"
The Rule for Sustainable GenAI Scale
Redesign first. Automate second. Scale last.
That's how GenAI compounds returns over time.
Step 5 — Drive Adoption and Build Organizational Trust
Scaled impact only happens when people trust and use the system.
Sustainable adoption comes from education, transparency, and early proof of value.
  • Train and incentivize adoption through quick wins and transparency.
  • Treat GenAI systems like new hires—onboard and coach them.
Enable Adoption
Build confidence through education, not enforcement.
Train teams on both how GenAI works and why it helps.
Start with quick wins that show real productivity or performance lift.
Incentivize participation and reward creative uses
Outcome: Employees see GenAI as an ally that enhances their craft, not a threat to it.
Humanize the System
Make GenAI part of the team.
  • Treat GenAI systems like new hires — onboard, coach, and evolve them over time.
  • Appoint internal "AI champions" who translate between tools and teams.
  • Build feedback loops to capture user insights to improve prompts, context, and quality.
Outcome: Employees see GenAI as an ally that enhances their craft, not a threat to it.
Measure and Reinforce
Track outcomes, not activity.
Measure effectiveness
show performance impact (lift in growth KPIs)
Measure efficiency
show productivity gains (cost, cycle time reduction).
Measure trust
show cultural alignment (adoption %, NPS, qualitative sentiment).
Outcome: Trust compounds as performance results validate adoption.
The Common Traits of High-Performing GenAI Organizations
Structured Context Libraries
Keep outputs on brand, effective, accurate, and differentiated (no "AI Slop").
Redesigned Workflows
Integrate human-AI collaboration from ideation to automation
ROI Discipline
Link GenAI directly to growth KPIs.
The moat isn't the model—it's your context and capability.
Sustainable advantage doesn't come from access to models.
It comes from how you apply them—your proprietary data, workflows, and the people who know how to use GenAI with judgment and creativity.
The Answer: What Winning Consumer Companies Do Differently
Design GenAI around workflows, not tools.
Measure GenAI against core consumer growth metrics.
Train models using proprietary brand, domain, and performance context.
Build employee trust with education, transparency, and recognition.
Technology levels the field. Context and capability create the edge.
FAQs
Should I try to automate as much of my company's workflows as quickly as possible because my CFO says we need to be more efficient?
Answer: No, that will end in a disaster. Use the step by step approach from this presentation instead. Start small, measure, and scale what works.
Should all of our creative and ads be AI generated?
Answer: No, the goal of using AI isn't to become an AI facing brand, it's to be a better brand. Use AI generated ads as one piece of your creative mix, and use AI to help you improve everything else you do.
The tech and tools seem to be changing every week and I feel like I can't keep up, what should I do?
Answer: You're not supposed to. Think in workflows and tools second. You can use ChatGPT (or any LLM) to help you figure out the best tools for a specific workflow.
Do you know everything about AI?
Answer:
At any given moment, my best estimation is I'm on top of 0.05% of what's happening in AI. Even with daily use and constant learning, it's impossible to "know it all."
The pace of innovation isn't slowing down, but that's okay. The goal isn't to know everything, it's to apply what matters.
Start small. Learn fast. Scale what works. That's how you win with GenAI.
Let's Continue the Conversation
Alex McEvoy
Founding Partner at Opopop, advising consumer companies on how to move from GenAI pilots to measurable, scalable performance.
Interested in applying this framework to your organization? Let's connect.
References & Sources
Industry Research & Reports
  1. MIT, "The Gen AI Divide: State of AI In Business 2025"
  1. McKinsey, "How generative AI can boost consumer marketing"
  1. Opopop Internal Data, "Anthropic Implementation Case Study"
  1. DesignRush, "Kalshi's $2K NBA Finals AI Ad Shows Why Big-Budget Commercials Are Dying"
  1. digitaldefynd, "25 Generative AI Case Studies"
  1. CIO, "CarMax drives business value with GPT-3.5"
  1. Duolingo, "Duolingo Launches 148 New Language Courses"
  1. TechCrunch, "Duolingo launches 148 courses created with AI after sharing plans to replace contractors with AI"