From Prompt Gaps to Growth Projections: New AI Search Forecasting
TABLE OF CONTENTS
Usage rates of both search engines and AI tools have recently reached new highs, showing that the entire search channel is expanding. So are the opportunities, especially in AI search.
This makes it vital for brands to be able to forecast potential impact in both search formats, so that they can prioritize effectively and allocate resources based on expected ROI.
Semrush Enterprise offers powerful forecasting capabilities for SEO. However, for AI search, its non-determinative and volatile nature have made this a challenge.
Until now, as Semrush Enterprise AIO offers new AI search forecasting capabilities. These allow teams to predict uplift from filling prompt gaps, projecting impact across traffic, audience, and mentions.
Why Missing Forecasting Creates Strategic Paralysis
Marketing teams recognize the enormous opportunity presented by AI search as the channel continues to grow rapidly in daily users. But, until now they haven’t had a way to quantify exactly what they're missing or where the real opportunity lies. This creates space for competitors moving first and potentially building visibility moats.
Without forecasting, AI search investments feel like bets. This can cause stakeholders and leadership to question the impact of targeting AI search vs other growth channels. Some persistent challenges slow strategy and potentially paralyse action:
Key metrics are shifting
As search expands, central search metrics like traffic are being affected and rethought. Many businesses are seeing flat or declining traffic as we approach a potential zero-click era.
While mentions, citations, and share of voice are now becoming more emphasized as visibility metrics, without the ability to connect them with organic growth or pipeline, where you might see opportunity, leadership may see vague outcomes.
Questions on value arise
From this missing attribution come the hard questions. "How much pipeline are we losing to AI search?" "What's the expected ROI of increasing headcount to target AI search?" "Which AI platforms should we prioritize?", all are legitimate questions that become extremely difficult to answer without the ability to forecast outcomes.
The problem of randomness
Unlike SEO where you can model traffic based on known rankings, AI search responses are non-deterministic, with personalization and randomness baked into them.
You can't guarantee which brand will be cited or mentioned in every AI response, but you can boost (and now project) that probability.
What this means
An inability to quantify and solve the above problems creates a frustrating cycle:
- 1You suspect you're missing opportunities but can't prove this.
- 2You hesitate to invest resources because you can't show potential returns.
- 3You’re stuck testing at random without an effective way to prioritize.
- 4Leadership remains sceptical because the business case is missing key data.
- 5Competitors move first while you're stuck analyzing.
- 6The gap widens as first-movers build compounding advantages.
Without forecasting, early investments in AI search optimization can be seen as experiments, or a distraction from "real" SEO or growth work.
How AIO’s Forecasting Empowers Teams to Act Confidently
Marketers previously couldn’t confidently answer questions like “What prompts are we missing?”, “Where should we focus?”, or “Will it impact traffic?” when it comes to AI search, but AIO now gives them the framework to quickly do so.
The new Forecasting capabilities allow teams to project potential gains in traffic, audience, and mentions, by analyzing their current prompt gaps. This shows which prompts offer the greatest opportunities, so you can confidently prioritize next steps by expected impact.
While the outcomes can’t be guaranteed (as AI search is non-deterministic), these analysis capabilities give brands a compass and a competitive edge:
- Quantify audience and brand visibility growth potential for planning, so your AI visibility strategies are grounded in data and measurable outcomes.
- Identify the prompt gaps that will drive the highest lift in AI visibility for clear prioritization of SEO and content initiatives.
- Plan confidently with quarterly plans, resource allocation, and performance expectations informed by data-backed projections.
AIO transforms intuition based "we think we should do this" into "here's what we're missing and what we're likely to gain by closing these specific prompt gaps."
Building Your Forecasting-Driven Strategy
Adding AI search forecasts to planning
The first step is integrating forecasts into your planning cycle. Remember: forecasting isn't a one-time exercise, but an ongoing process that consistently informs your strategy and key priorities.
Start with a quarterly planning integration:
- Use forecasts to set realistic AI visibility targets
- Allocate SEO and content resources based on projected ROI of different prompt gaps
- Establish measurement frameworks that track actual vs. forecasted growth
- Adjust quarter-on-quarter based on what initiatives were delivered and which projections proved accurate
Then align team resources and priorities with projected impact.
If wider organizational buy-in is needed, make the stakes crystal clear and contextualize forecasts within shared measurable deliverables:
- We're missing 73 prompts that reach 4.2M potential buyers monthly
- Allocate SEO and content resources based on projected ROI of different prompt gaps
- Competitors capture the visibility in these 25 prompts and here's the traffic cost
- Closing these 15 priority gaps projects to generate 12,300 additional branded searches over 6 months
Measuring performance against projections
The power of forecasting is informed iteration. As AI search is non-deterministic, some variance is to be expected. Therefore, consider setting input metrics you can totally control, alongside the final output metrics that you track against.
Examples for input metrics:
- Delivery of ‘X’ topical hubs with complete internal linking
- ‘X’ velocity of content updates for key pages
- Promotion of all content assets via ‘X’ other channels
Output metrics you can track:
- Number of prompts with brand mentions/citations
- Number of brand mentions
- Number of domain citations
- Overall share of voice
- AI referral sessions
From here, you can refine future forecasts based on learnings and potentially double down on tactics that overperformed.
This systematic approach unlocks a deeper strategy, extending data leadership and helping clarify any organizational challenges to further investment in search.
Quantify Opportunities and Move First
AI is expected to impact $750 billion in revenue by 2028 as consumers increasingly trust and rely on generative AI. But trust and impact mean nothing if your brand isn't visible where these decisions happen.
Semrush Enterprise AIO’s Forecasting automation means that instead of wondering about this opportunity, brands get exact numbers: the specific prompts you're missing, the audience reach they represent, and the mentions you could capture.
With these clear projections in hand, strategy becomes fluid and barriers to action are lessened. Transparency also becomes the new norm, as teams track actual performance against forecasts and can prove ROI to stakeholders.
Ready to get started? Request a demo now.
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