Marketing In The AI Era Has A Marketing Problem
AI debates are loud. The bigger issue is closer to home: marketing has been oversold and overpromised. That gap between promise and outcome has created a trust problem. It's time to reset what marketing can and can't do.
The overpromise trap
We love big swings. The "one big campaign" that saves the quarter, the clever line that flips a market. Then reality hits. No campaign fixes a weak product, bad fit, or broken ops. Marketing gets blamed for things it never owned.
We respond with more dashboards, more attribution, more jargon. CTR, engagement, brand lift - none of it matters if sales and pipeline don't move. If we can't show how work turns into revenue, we're missing the point.
The real limits
Marketing is a magnifier. It makes good things better and bad things obvious. It can't fix a broken product, poor service, or flawed unit economics. Expecting anything else sets everyone up to fail.
Another issue: everyone thinks they're a marketing expert. That leads to quick opinions, inflated asks, and the idea that marketing is fluff. Treat it like what it is - a disciplined growth function with clear inputs, outputs, and trade-offs.
What credible marketing looks like
Stop talking. Listen to the numbers, to customers, and to peers in sales, product, and finance. When work lands, you won't need to hype it. When it misses, raise your hand first and say why.
Run marketing like a lab, not a stage. Set hypotheses. Test. Learn. Every experiment should teach you something about customer, message, or channel - even if it fails.
Operating principles
- Tie every program to revenue, pipeline, CAC, LTV, and payback. Kill vanity metrics.
- Sunset losing campaigns fast; double down on winners. Publish a simple weekly scoreboard.
- Bring unfiltered customer feedback to product and sales. Be the voice of the market inside the company.
- Write down what marketing can't solve without help (product gaps, pricing, capacity). Make dependencies explicit.
- Ship small, frequent experiments. Predefine success thresholds and stop-losses.
- Build a measurement spine: clean CRM data, consistent attribution rules, one source-of-truth dashboard.
Metrics that matter
- Pipeline created and progression by segment and channel
- Revenue influenced with clear multi-touch rules (simple beats clever)
- CAC, LTV, payback period, win rate, sales cycle length
- Retention, expansion, and NPS verbatims
How AI actually fits
AI isn't a miracle; it's a speed and scale booster. Use it to compress research time, multiply creative variants, and tighten ops. Don't expect it to fix a weak offer or vague positioning.
- Synthesize customer calls to surface themes for message tests.
- Generate ad and landing page variants; test with tight control groups.
- Automate reporting, QA tracking links, and content ops workflows.
Want to sharpen the AI skills that actually move revenue? See this practical program for marketers: AI Certification for Marketing Specialists.
A simple 90-day plan
- Days 1-15: Audit pipeline, channel ROI, and data quality. Pick three core revenue metrics and three guardrails. Align with sales and finance.
- Days 16-45: Cut the bottom 20% of spend. Reinvest in the top two channels. Launch five experiments with preset stop dates.
- Days 46-75: Update messaging from win/loss and customer interviews. Enable SDRs/AEs with a tighter narrative and proof points.
- Days 76-90: Publish results. Keep two winners, cut the rest. Refresh plan with updated CAC/LTV, capacity, and channel mix.
The payoff
Credibility comes from clarity. "Here's what worked, here's what didn't, and here's what's next." That's how marketing stops defending its value and starts leading growth.
Your membership also unlocks:
