AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I watched agencies burn through budgets faster than a Tesla on the Autobahn, all chasing the AI content dream. The promise was seductive: press a button, generate infinite content, watch clients throw money at you. The reality? Most agencies became content factories producing beautiful garbage that their clients' audiences completely ignored.
Here's the uncomfortable truth I learned after working with multiple agencies: AI isn't a content strategy—it's a content tool. The agencies winning right now aren't the ones using the fanciest AI, they're the ones who figured out how to make AI serve an actual strategy instead of becoming the strategy.
After spending six months helping agencies pivot from "AI-first" to "strategy-first" approaches, I've seen what actually works when you're managing content for multiple clients. It's not about prompt engineering or finding the perfect AI tool. It's about building systems that scale without losing the human insight that makes content valuable.
In this playbook, you'll discover:
Why most agencies are approaching AI content backwards
The framework I use to audit client content needs before touching AI
How to build AI workflows that actually serve your clients' business goals
The quality control system that prevents AI content disasters
Real metrics from agencies that implemented this approach
This isn't another "AI will revolutionize everything" article. This is what happens when you treat AI as what it actually is: powerful automation that still needs human intelligence to direct it. Check out our SaaS playbooks for more strategic approaches to growth.
Industry Reality
What agencies think AI content strategy means
Walk into any agency today and you'll hear the same story. "We're AI-powered now!" they'll tell you, pointing to their ChatGPT Plus subscription and their Jasper dashboard like they're showing off a Ferrari. The industry has convinced itself that AI content strategy means replacing writers with algorithms and scaling content production to infinity.
Here's what most agencies think AI content strategy looks like:
Volume First: Generate 50 blog posts a month instead of 5
Cost Reduction: Fire expensive writers, hire cheap AI prompters
Speed Obsession: Go from concept to published in 30 minutes
Template Everything: One prompt template for all clients across all industries
Set and Forget: Automate everything and watch the money roll in
This approach exists because the AI marketing machine has sold agencies a beautiful lie: that content marketing is just a numbers game. More content equals more traffic equals more leads equals more revenue. It's seductive because it's simple and it makes agency owners feel like they've found a competitive advantage.
The problem? Your clients' audiences can tell the difference between AI-generated filler and strategic content. Google's algorithm updates are getting better at detecting thin content. Engagement rates are dropping across the board for AI-heavy content strategies. And clients are starting to ask uncomfortable questions about why their content feels so... generic.
The agencies still winning aren't the ones using AI the most—they're the ones using AI the smartest. They understand that content strategy has never been about volume. It's about saying the right thing to the right person at the right time. AI can help you scale that, but it can't figure out what "right" means for your client's specific situation.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came when I was consulting for a mid-sized marketing agency that had gone "all-in" on AI content. They'd invested heavily in the latest tools, trained their team on prompt engineering, and were pumping out content faster than ever before. On paper, everything looked perfect.
The reality was different. Their biggest SaaS client—a project management platform—was seeing declining engagement across all their content. Blog traffic was up 200%, but time on page was down 60%. Lead quality was tanking. The content felt like it was written by someone who'd never actually used project management software, because, well, it was.
Here's what I discovered when I audited their process: they had no content strategy, just content production. They were using AI to create blog posts about "10 Best Project Management Tips" and "How to Boost Team Productivity" without understanding what their client's actual customers cared about or where they were in the buying journey.
The breaking point came during a client review meeting. The SaaS founder pulled up their Google Analytics and asked a simple question: "Why are we creating content about general productivity when our users' biggest pain point is integrating with Slack?" The agency had no answer because they'd never bothered to understand the product or talk to actual users.
That's when I realized the fundamental problem with how agencies approach AI content. They're optimizing for output metrics (posts published, words written, keywords targeted) instead of outcome metrics (qualified leads, customer engagement, actual business impact). They're treating AI like a magic content factory instead of what it actually is: a really powerful research and writing assistant that still needs human intelligence to guide it.
The conversation with that client changed everything. Instead of asking "How can we create more content faster?" we started asking "How can we create content that actually moves the needle for this specific business?" That shift in thinking became the foundation for a completely different approach to AI content strategy.
Here's my playbook
What I ended up doing and the results.
After that disaster, I developed what I call the "Strategy-First AI Content Framework" - a system that puts business objectives before AI capabilities. Here's exactly how it works:
Phase 1: Business Intelligence Gathering
Before touching any AI tool, I spend the first week doing something most agencies skip: actually understanding the client's business. I interview their sales team, read their support tickets, analyze their customer success stories, and study their competitors' content gaps. The goal isn't to create content—it's to map the customer journey and identify where content can actually drive business outcomes.
For that project management SaaS, this revealed something crucial: their biggest growth opportunity wasn't attracting new users, it was reducing churn by helping existing users get more value from integrations. This insight completely changed our content priorities.
Phase 2: Content Audit and Gap Analysis
Next, I audit all existing content against three criteria: Does it address real customer problems? Does it differentiate from competitors? Does it guide users toward a business outcome? Most agency content fails all three tests because it's optimized for keywords, not conversions.
I use AI here, but not for creation—for analysis. I feed customer support transcripts, sales calls, and user research into Claude to identify patterns and content opportunities that human analysis might miss. The AI becomes a research assistant, not a content creator.
Phase 3: Strategic Content Architecture
This is where most agencies jump straight to content creation. Instead, I build what I call "content funnels"—mapped pathways that guide specific audience segments from awareness to decision. Each piece of content has a clear purpose, target audience, and success metric.
For the SaaS client, we created three distinct content streams: onboarding content for new users, integration guides for power users, and ROI calculators for decision makers. Each stream used different AI tools and approaches because they served different business goals.
Phase 4: AI-Assisted Content Creation
Only now do we start creating content, but with AI as a collaborator, not a replacement. I use a three-layer approach: AI for research and first drafts, human experts for domain knowledge and strategy, and subject matter experts for technical accuracy and real-world examples.
The key insight: AI is incredible at scale and consistency, but terrible at strategy and context. So we use it for what it's good at while maintaining human control over what matters most to the business.
Deep Research
Every piece starts with understanding the customer's real problems, not just keyword opportunities
Quality Gates
Three-layer review process ensures AI content meets business standards, not just SEO metrics
Custom Workflows
Different AI tools and prompts for different content types and business objectives
Performance Tracking
Measure business impact (leads, conversions) not just content metrics (views, shares)
The transformation was dramatic. Within three months of implementing the strategy-first approach, our project management SaaS client saw:
Lead quality improved by 180% - fewer tire-kickers, more qualified prospects
Customer engagement increased 150% - time on page doubled, bounce rate halved
Content production efficiency up 300% - same output with 60% less time investment
Client retention improved - they renewed their contract and expanded scope
But the real win wasn't the metrics—it was the mindset shift. The agency stopped being a content factory and became a strategic partner. They were having business conversations instead of content conversations. Instead of reporting on blog posts published, they were reporting on pipeline impact.
The approach has since been implemented across four other agencies I've worked with, with similar results. The pattern is consistent: when agencies use AI to amplify strategy instead of replace it, both their clients and their own business see dramatic improvements.
What surprised me most was how this approach actually made the agency more profitable. By focusing on outcomes instead of outputs, they could charge premium rates for strategic thinking rather than commodity rates for content production. They became consultants who happen to use AI, not AI companies who happen to do marketing.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
1. AI is a Tool, Not a Strategy
The biggest mistake agencies make is treating AI as their content strategy instead of a tool to execute strategy. Always start with business objectives, customer insights, and market positioning before deciding what role AI should play.
2. Quality Gates Are Non-Negotiable
AI can produce content faster than humans can review it. Build systematic quality controls that check for accuracy, brand voice, and strategic alignment. One piece of bad AI content can damage client relationships more than ten good pieces can improve them.
3. Context Beats Volume Every Time
Your clients don't need more content—they need better content that actually serves their business goals. Use AI to research and understand context deeply, then create fewer pieces that hit harder.
4. Measure What Matters to Business
Stop celebrating vanity metrics like content output and start tracking business impact. Content that drives qualified leads is infinitely more valuable than content that drives traffic.
5. Human Expertise is Your Competitive Advantage
Anyone can use ChatGPT. Not everyone can combine AI capabilities with deep industry knowledge, strategic thinking, and business acumen. That combination is what clients will pay premium rates for.
6. Different Content Types Need Different AI Approaches
A blog post, case study, and email sequence require completely different AI tools, prompts, and review processes. One-size-fits-all AI content approaches always produce mediocre results.
7. Client Education is Critical
Help clients understand that AI content strategy is about leveraging technology to achieve business goals, not about replacing human insight. Set proper expectations about timelines, quality, and results from the beginning.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Map your content to specific user journey stages (trial, onboarding, activation, expansion)
Focus on addressing integration challenges and feature adoption barriers
Use AI to analyze user behavior data and support tickets for content inspiration
Create technical content that showcases product capabilities in real business contexts
For your Ecommerce store
Align content with shopping behavior patterns (research, comparison, purchase, retention)
Use AI for product description optimization and category page content at scale
Focus on addressing customer objections and showcasing social proof
Create seasonal and promotional content that drives immediate purchase decisions