AI MARKETING STRATEGIES PLAYBOOK
How 5 Marketing Leaders Cracked the AI Code
An Exclusive First Look into AI Strategies from Marketing Leaders (That Actually Work) with a 30-Day Action Plan
My Name is Alex Gluz
- Digital marketing expert with 16 years of experience helping companies grow through effective paid advertising and proven growth strategies.
- Founder of T.A. Monroe - a B2B agency where we turn marketing challenges into success stories.
- We use battle-tested, data-driven demand generation frameworks to help B2B SaaS companies predictably scale their revenue within 6 months, without the risk of wasting budgets.
- Over the last eight years we've built and optimized over 3,500 paid campaigns across Google, LinkedIn, and Facebook.
- Our frameworks that have consistently delivered an average of 53% increase in high-quality leads and 27% reduction in CAC for our clients.
His Challenge:
Mark watched organic search traffic "drop off a cliff" when Google started adding AI-generated results to search pages.
The market was flooded with AI tools, and like many marketers, he faced the question of which ones to actually use.
The Solution:
Instead of mastering every tool, Mark assigns each AI platform one specific job - think of it like hiring specialists instead of generalists.
What Most Do Wrong:
Try every new AI tool, and master none.
What Mark Does:
Instead, he assigns specific jobs:
"The way I chose between different LLMs is that I didn't."
- Research phase: Perplexity (upfront research)
- Writing phase: Claude (long, deep content)
- Polish phase: ChatGPT (smartest and most versatile)
- Collaboration: Gemini (integrates with Google Docs)
His Efficiency Secret:
"It's actually better to get ChatGPT to output things in code instead of text because it just makes it faster to upload to the website."
Your Action
Stop evaluating new tools. Assign your current tools specific roles and stick to them for 30 days.
Her Challenge:
Running marketing for a fast-growing company (now almost 300 employees) with just a 6-person team.
As she puts it: "we're very lean for the size of company that we are, but we do a lot."
The Solution:
They build what she calls a "content flywheel" - a system where one piece of anchor content (like a demo or webinar) automatically transforms into 15-20 different pieces across all channels.
They call it the content flywheel because the key to the flywheel is determining certain types of what they call anchor assets.
What Most Do Wrong:
Create new content for each channel
What Sylvia Does:
Content creation:
From this anchor content, her team creates:
- The video recording
- The transcript of what was said
- Clips for social media
- Posts that go deep on how the product works
- Content about how people use it
The AI Implementation:
Her team then uses a platform called Copy AI which takes the anchor asset and sets up a whole workflow.
It then automatically creates all of the distribution content pieces they could possibly want.
Instead of manually creating 20 pieces of content, her team:
- Creates one great demo
- Feeds it into Copy AI
- AI automatically generates all the derivative content
- Team reviews and publishes
Critical Success Factor:
"We do a lot of work just prioritizing, so that we can make sure we're supporting the company with that small team."
Your Action
Pick your best-performing content type. Map 15 ways to repurpose it. Automate the transformation.
Her Challenge:
With all this "noise" in the market, people don't know who or what to believe.
AI has created so much content that buyers can't distinguish real expertise from AI-generated fluff.
The Solution:
Applies the 80/20 rule strategically. Uses AI to handle 80% of routine tasks efficiently. Preserving human involvement for the critical 20% that builds trust and moves deals forward.
What Most Do Wrong:
Automate everything or nothing.
What Kristin Does:
Let AI Handle (80%):
- Basic insights off of the data
- Routine customer inquiries
- Scheduling and admin tasks
- Standard reporting
- Basic content creation
Keep Human (20%):
- Trust-building moments
- Complex problem resolution
- Strategic recommendations
- Peer validation and referrals
- High-stakes customer interactions
The Trust Factor:
"Knowing exactly which moments require human involvement to build trust. As she states: "Every brand at some point needs to have a human that can address a customer concern."
Your Action
List every customer touchpoint. Mark which ones build trust (keep humans) vs. transfer information (automate).
His Challenge:
70% of B2B sales reps did not make their quota last year. Why? They spend only 28% of their week in customer-facing activities. The rest is consumed by research, CRM updates, and managing "12 different tools."
The Solution:
Stopped using AI to slightly speed up broken processes. Instead, his team completely reimagined the entire sales workflow around outcomes, not tasks. His platform Agentive transforms weeks of work into minutes.
What Most Do Wrong:
Use AI to automate individual tasks while keeping the same inefficient process.
What Usman Does:
Build purpose-built AI that handles entire processes end-to-end.
The Agentive Difference:
Traditional AI (Agentic) Approach:
- Automates individual tasks
- Mimics human activities
- Slightly faster but same process
- Still requires 12+ tools
- Sales reps are still overwhelmed
Agentive (Outcome-Focused) Approach:
- Reimagines entire workflow
- Focuses on results, not tasks
- Compresses weeks to minutes
- Single integrated platform
- Frees up Sales reps to sell
Why It Matters:
By reimagining the entire workflow, sales reps can finally focus on what they're hired to do: "building relationships, orchestrating and moving a deal through."
Your Action
Choose your most painful manual process. Write down what success looks like (the outcome), then work backwards to design an AI-first approach.
His Challenge:
Zefr analyzes millions of content pieces for brand safety across TikTok, Meta, and YouTube. They incur cost per unique piece of content, not per impression. One piece could get 1 view or 100 million views, but the analysis cost is the same.
The Solution:
Created a progressive compute system that matches AI sophistication to business value. Started with cheap analysis for everything, then escalated intelligence (and cost) only for content that matters.
What Most Do Wrong:
Use the same expensive AI for everything, or use cheap AI that misses critical issues.
What Jon Does:
Runs relatively cheap compute on a piece of content, and then runs progressively more expensive compute on it as it gets more important.
His Three-Level System:
Level 1
Basic Screening (Fractions of a cent)
- Simple keyword matching
- Basic categorization
- Handles 90% of content
- Catches obvious issues
Level 2
Advanced Analysis (Cents per item)
- Context understanding
- Nuanced interpretation
- For popular or ambiguous content
- More sophisticated models
Level 3
Human Expert Review (Dollars per item)
- Complex edge cases
- Policy decisions
- Brand-critical content
His Efficiency Rule:
"Spend 10 times as much effort on [the holdout set] than actually the modeling." Define success meticulously before building anything.
Your Action
Map your processes by value and risk. Create tiers of AI sophistication. Use expensive resources only where the business impact justifies it.