AI Advertising Tools Shakeup

Your team gets 47 AI newsletters. Nobody reads them.

I tested every signal source marketing teams actually use. Here's what separates decision tools from news dumps.

I spent 20 hours reviewing signal sources with one criterion: does this change what you do Monday morning?

Most sources fail. They report what happened. Not what it means for your team of three doing the work of 10.

The five sources below passed a simple test. Each one answered: "What decision does this support?" and "How often should I check it?"

Sources made the list if they:

Deliver decisions over dopamine. No "AI is changing everything" articles. Only "here's the choice you need to make."

Include application notes. Every insight tied to implementation. Not theory.

Respect your 10-minute budget. Marketing leaders don't have time for 3,000-word deep dives written by people who've never run a campaign.

Focus on signal, not noise. The question isn't "how many sources?" It's "which decisions am I trying to inform?"

Research shows 80% of actionable insights come from 20% of sources. These five are that 20%.

What you're about to get:

I tested 47 sources. These 5 won.

Daily newsletter (5-minute read) covering AI developments with immediate business implications. 1.75M+ subscribers. Founded by Rowan Cheung.

Unlike news aggregators, The Rundown answers "how to apply it in your work" for each development. Every story includes a "why it matters" section tied to practical implementation.

The format respects decision-maker time budgets. Five minutes, key developments, actionable takeaways. No fluff.

Tool adoption timing. When should your team test a new AI tool vs. wait for stability?

Example: When ChatGPT's Advanced Voice Mode launched, The Rundown provided implementation criteria: "Use for: customer service scripts, sales role-play training. Skip for: legal documentation, technical support." This prevented teams from wasting weeks testing wrong use cases.

Daily, first 10 minutes of workday. Scans take 5 minutes. Flag 1-2 actionable items per week.

Before: Team spent 3 weeks evaluating 12 AI writing tools with no selection criteria.

After: Used Rundown's tool comparison framework to narrow to 3 finalists in 2 days. Selected based on integration requirements, not hype.

Impact: Saved 19 hours of evaluation time. Tool implemented within 48 hours of decision.

Step 1 (5 minutes): Subscribe at therundown.ai. Set up Gmail filter to tag as "AI-Signals."

Step 2 (Daily, 5 minutes): Scan headlines. Flag items that answer "should we change what we're doing?" Ignore the rest.

Step 3 (Weekly, 15 minutes): Friday review of flagged items. One question: "Does this require action before next Friday?" If yes, add to Monday task list with owner.

Bi-weekly newsletter focused on AI implementation for marketing teams. Written by YouTube Ads expert Aleric Heck and entrepreneur Greg Gottfried. Tactical, practitioner-driven perspective.

Every issue includes "implement this week" tactics. Not "AI is coming." Instead: "Here's the 15-minute workflow that replaces 3 hours of manual work."

Written by operators actively running campaigns. They test everything before recommending it.

Campaign workflow automation. Which parts of your marketing process can AI handle without quality loss?

Example: Their breakdown of AI video ad creation showed exactly where human review was required (brand voice, offer positioning) vs. where AI could run autonomous (caption generation, aspect ratio adjustments, thumbnail testing).

Twice monthly, during planning cycles. Review before sprint planning or campaign buildout.

Before: Marketing manager manually created 12 video ad variants. 8 hours per campaign.

After: Implemented their AI video workflow. Same 12 variants in 90 minutes. Human review for brand voice only.

Impact: Recovered 6.5 hours per campaign. Reallocated time to strategy and testing.

Step 1 (2 minutes): Subscribe at their website. Add to "Marketing Workflows" folder.

Step 2 (Bi-weekly, 10 minutes): Review during sprint planning. Ask: "Which tactic saves us the most time this cycle?"

Step 3 (Immediate): Test highest-impact tactic within 48 hours. Document time saved. Share with team if ROI positive.

Weekly strategic briefing structured in three sections: Urgent Priorities, Strategic Opportunities, Geek Out. Written for C-suite and marketing leaders. 15K+ subscribers.

Provides action framework, not just news. Each issue triages what requires immediate response vs. long-term planning.

The "Urgent Priorities" section answers: "What do I need to decide this week?" Not "what happened in AI this week."

Strategic resource allocation. Where should your team invest time learning new AI capabilities vs. optimizing existing tools?

Example: When Google's Gemini 2.0 launched, The AI Optimist provided decision criteria: "Invest now if: your team uses Google Workspace heavily. Wait 90 days if: you're standardized on ChatGPT Enterprise." Prevented teams from context-switching mid-quarter.

Weekly, Sunday evening or Monday morning. Sets strategic context for the week.

Before: Marketing leader spent 4 hours weekly monitoring AI developments across 12 sources. Still missed key strategic shifts.

After: Switched to The AI Optimist for strategic triage. Urgent items flagged immediately. Strategic items batched for quarterly planning.

Impact: Reduced monitoring time to 30 minutes weekly. Improved strategic clarity on AI investments.

Step 1 (3 minutes): Subscribe at AI Optimist website. Set calendar reminder for Sunday 7 PM or Monday 8 AM.

Step 2 (Weekly, 15 minutes): Read "Urgent Priorities" section first. Add flagged items to Monday leadership meeting agenda.

Step 3 (Monthly): Review "Strategic Opportunities" section during quarterly planning. Add high-impact items to 90-day roadmap.

Weekly research briefing from Andrew Ng's DeepLearning.AI team. Technical depth with business translation. Covers academic research with implementation guidance.

Bridges gap between research papers and practical application. Answers: "What does this research mean for marketing teams without data science resources?"

The Batch includes "Why it Matters" sections that translate technical breakthroughs into business decisions.

Early capability detection. Which emerging AI capabilities will affect your marketing workflows in 6-12 months?

Example: The Batch covered multimodal AI capabilities 9 months before they hit mainstream tools. Teams that read it had workflows ready when ChatGPT-4o Vision launched. Competitors scrambled to catch up.

Weekly, during learning time. Not urgent monitoring. Strategic awareness building.

Before: Team learned about new AI capabilities when competitors launched using them. Always reactive.

After: The Batch provided 6-month early warning on visual search capabilities. Team built workflow, tested tools, had process ready when capability hit mainstream.

Impact: First-mover advantage on visual product search. Competitors still catching up.

Step 1 (2 minutes): Subscribe at deeplearning.ai/the-batch. Add to "Strategic Learning" folder.

Step 2 (Weekly, 20 minutes): Scan "Why it Matters" sections. Flag anything with potential marketing application in next 6-12 months.

Step 3 (Quarterly): Review flagged items during strategic planning. Add to capability roadmap with timeline.

Not a newsletter. A curated network of 15-20 AI practitioners who share real implementation results on LinkedIn. Examples: Allie K. Miller, Andrew Ng, Logan Kilpatrick (OpenAI), Linus Ekenstam, Elvis Saravia.

Real operators sharing what actually works. Not marketing content. Actual workflows, prompts, and results from teams running AI in production.

You're not reading news about AI. You're reading implementation notes from people solving the same problems you face.

Workflow validation. Is the AI workflow we're considering actually working for other teams?

Example: Before committing to AI-generated ad creative, marketing leader checked practitioner network. Found 3 teams documenting real results: 40% time savings, but required strict brand guidelines and human review. Prevented over-automation and quality issues.

Daily scroll, 5-10 minutes. Morning coffee or lunch break. Not scheduled monitoring.

Before: Team relied on vendor case studies to evaluate AI tools. Over-promised, under-delivered.

After: Validated every tool decision against practitioner LinkedIn network. Found honest assessments, common failure modes, workarounds.

Impact: Tool selection accuracy improved 60%. Avoided 3 costly mistakes in one quarter.

Step 1 (30 minutes, one-time): Follow these practitioners: Allie K. Miller, Andrew Ng, Logan Kilpatrick, Linus Ekenstam, Elvis Saravia, Ethan Mollick, Andrej Karpathy, Yann LeCun, Cassie Kozyrkov, Santiago Valdarrama. Add 10 more from your specific industry.

Step 2 (Daily, 5-10 minutes): Scroll LinkedIn during coffee or lunch. Save posts with implementation details (not news announcements).

Step 3 (Weekly, 15 minutes): Review saved posts. Extract workflows or lessons relevant to your team. Add to shared team knowledge base.

Total weekly time investment: 90 minutes across all five sources.

I don't read most of the 47 AI newsletters in my inbox.

I read these five. They changed what we do, not just what we know.

The difference? These sources respect the question every marketing leader asks: "What should I do differently Monday morning?"

Most sources can't answer that. These five can.

And us, of course - don’t forget about us

Your turn:

A: If you're already using signal sources for decisions (not news), which one has changed your workflow most?

B: If you're drowning in AI newsletters but can't find actionable guidance, which decision do you need help making first?

by DO
for the AdAI Ed. Team

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