How often are you reviewing your AI search strategy? Monthly? Quarterly? What's the right cadence?

Great question. Here’s the framework I recommend:

Review cadence:

Weekly reviews (30 min):

• Brand mention counts across platforms
• Sentiment changes
• New negative mentions (alert-worthy)
• Competitor position shifts
• Quick wins to action

Monthly reviews (1-2 hours):

• Trend analysis (week-over-week changes)
• Content performance in AI citations
• What content types are working
• Tactical adjustments needed

Quarterly reviews (strategic):

• Overall strategy assessment
• Competitive benchmarking deep-dive
• Technical implementation review
• Resource allocation decisions
• Goal setting for next quarter

The key is consistency. A reliable weekly check catches issues before they compound.

What we’ve learned after 12 months of tracking:

The volatility reality:

AI visibility fluctuates significantly. Research shows citation patterns can vary 40-60% month to month. This means:

1. Don’t overreact to weekly changes - Some volatility is normal
2. Look for trends over 2-3 months - That’s where real patterns emerge
3. Watch for sudden drops - Those warrant immediate attention

Our cadence:

• Daily: Automated alerts for significant changes
• Weekly: Quick metrics review (15 min)
• Bi-weekly: Content team strategy session
• Monthly: Full performance report
• Quarterly: Executive strategy review

What triggers off-cycle reviews:

• Citation drop >20% in a week
• Major negative mention
• Competitor leapfrogging us
• Algorithm change announcements
• New product launch

The automated alerts are crucial. You can’t manually check everything, so set thresholds that matter.

Analytics perspective on review structure:

The mistake most teams make:

Looking at too many metrics without clear action triggers. Every metric you track should have a defined response.

Our framework:

Metric → Threshold → Action

What this prevents:

• Analysis paralysis
• Overreacting to normal fluctuation
• Missing actually important changes
• Wasted time on metrics that don’t drive action

Define your thresholds BEFORE you start tracking. Otherwise you’ll drown in data.

Agency perspective managing multiple client accounts:

What we’ve standardized:

Weekly client check-in (15 min):

• High-level visibility metrics
• Any alerts triggered
• Quick wins actioned

Monthly performance report:

• Trend analysis
• Content recommendations
• Competitive positioning
• Next month priorities

Quarterly strategy review:

• Deep competitive analysis
• Strategy reassessment
• Goal recalibration
• Budget discussions

The efficiency key:

Standardized reporting templates. We can generate monthly reports in 30 minutes because the structure is consistent and tools are automated.

Client communication tip:

Educate clients that AI visibility fluctuates. Set expectations early that weekly variations are normal. This prevents panic calls every time there’s a dip.

Startup perspective on realistic cadence:

Our reality:

Small marketing team, limited resources, multiple priorities.

What we actually do:

• Weekly: 10-minute check of key metrics (during team standup)
• Monthly: 1-hour dedicated review
• Quarterly: Strategic planning includes AI visibility

What we skip:

• Daily monitoring (not enough resources)
• Deep competitor analysis (quarterly is enough for us)
• Platform-by-platform breakdown (we focus on ChatGPT only initially)

The 80/20 approach:

Focus on:

• Are we visible for our core queries?
• Is sentiment positive?
• Are we keeping pace with top competitor?

That’s it. Everything else is nice-to-have.

When we scale up:

As AI visibility becomes larger share of our traffic, we’ll invest more in monitoring. For now, minimal viable tracking.

Really helpful frameworks. Here’s what I’m implementing:

Weekly (Friday, 20 min):

• Check automated alerts from Am I Cited
• Quick metrics review: mentions, sentiment, competitor position
• Note any anomalies for monthly discussion

Monthly (first Monday, 1 hour):

• Trend analysis: what changed this month
• Content performance: what’s being cited
• Tactical adjustments: what to optimize
• Report for leadership

Quarterly (end of quarter, half day):

• Strategic assessment: is our approach working
• Competitive deep-dive: where do we stand
• Technical audit: any implementation issues
• Resource and budget planning

Automated alerts (continuous):

• Sentiment drops below threshold
• Significant visibility changes
• New competitor appearances
• Negative mentions

The key insight:

Structure prevents both under-monitoring (missing issues) and over-monitoring (wasting time). Define cadence, stick to it, and let automation handle the in-between.

Thanks everyone for sharing your approaches.

Enterprise perspective on scaling review processes:

At scale, you need tiered monitoring:

Tier 1 - Automated (continuous):

• AI-powered anomaly detection
• Threshold-based alerts
• Dashboard updates

Tier 2 - Analyst review (weekly):

• Validate automated alerts
• Deep-dive on flagged items
• Quick wins identification

Tier 3 - Strategic review (monthly/quarterly):

• Cross-functional input
• Budget and resource decisions
• Competitive strategy

The scalability question:

Manual review doesn’t scale. If you’re tracking 100+ queries or multiple brands, you need automation to surface what matters.

Tool investment:

At enterprise scale, the monitoring tool cost is trivial compared to the team time you save. Invest in good tooling.

Looking ahead at review cadence:

AI platforms are changing rapidly.

Algorithm changes, new features, and competitive shifts happen frequently. This suggests:

1. More frequent tactical reviews - Weekly is becoming the minimum
2. Faster response capability - Need to act on changes quickly
3. Continuous optimization mindset - Not set-and-forget

The trajectory:

As AI search becomes larger portion of discovery, AI visibility will require the same attention we give to traditional SEO today. That means dedicated monitoring resources and regular optimization cycles.

Start building the habit now:

Even if AI is small percentage of your traffic today, build the review processes now. When AI grows (and it will), you’ll be ready with established rhythms.