‘On Demand’ Demand Gen: How GTM AI Automates New Business Growth

In the crowded arena of GTM demand generation, most organizations are laser-focused on optimizing what’s already known: nurturing interest in established markets, identifying accounts actively searching for solutions, and meeting familiar challenges head-on. And while these strategies are tried and true, they leave a critical opportunity untapped: latent demand.

Latent demand represents an industry’s underexpressed pain points or business aspirations. This nonobvious demand hides within behavioral data, signals, microtrends, and industry shifts. 

Savvy GTM orgs can continue using traditional — and effective — demand gen methods while also leveraging this powerful latent-demand intelligence to reveal untapped sales and marketing opportunities. (And get there before the competition does.)

Let’s see how.

Making the Case for ‘Latent Demand’ Gen 

The daily lives of B2B sellers and marketers rarely operate on an industry’s fringes — but they should, at least some of the time. Consider the case of generative AI. Before ChatGPT and similar tools became mainstream, few business leaders explicitly sought “AI-enhanced content creation” solutions.

Yet we now know there was a massive latent demand for faster, scalable, and personalized marketing content — and it was there for likely decades. This demand immediately surfaced once AI tools demonstrated what could be accomplished. And now, many of us use generative AI in our daily work.

The lesson: Latent demand exists before the market realizes it. That’s the blue water where your GTM team can swim.

From Demand Generation to ‘Demand Creation’

We’re talking about a shift from demand generation to demand creation. This changes the question of “How do we win more deals in this market?” to “What markets can we create that no one else sees?”

AI plays a crucial role in this new paradigm. Its superhuman capabilities spot patterns that we can’t, operating at an unprecedented scale to uncover, validate, and activate latent demand in ways that were previously unimaginable.

Based on AI-assisted analysis, here’s how uncovering latent demand can drive measurable improvements across traditional metrics:

Metric	How Latent Demand Impacts It	Example Impact
Prioritized accounts and contacts	Expands account ICP and volume by identifying untapped segments.	+20% ICP target accounts and qualified accounts/contacts from emerging segments.
Opportunity Volume	Improves target account quality, increasing conversion rates to opps.	+30% qualified accounts-to-opportunity conversion.
Deal Velocity	Shortens sales cycles through pre-educated, intent-driven accounts.	-15% average time to close.
CAC/ROI	Enhances marketing efficiency with AI-optimized campaigns.	+25% ROI on ad spend.
Pipeline Growth	Creates entirely new sources of accounts and opps.	+40% growth from new verticals.

Let’s dive into how GTM teams might craft processes and integrate technologies to generate some of these results. 

Use Case: From Demand Generation to ‘Demand Creation’

Use Case 1: Advanced Pattern Recognition Across Diverse Datasets

AI analyzes massive unstructured datasets from multiple sources like customer reviews, social media chatter, transaction logs, and competitive intelligence. It can also identify anomalous patterns and correlations that elude human analysis. That’s where new opportunities reside.

For instance: Let’s say an enterprise software provider uses AI to analyze product usage logs. It discovers that small subsets of users in non-core industries (e.g., healthcare) are repurposing the software for compliance workflows. This pattern hints at an untapped demand for a healthcare-specific compliance solution, leading to a new product vertical — and new demand-gen opportunities.

So let’s take a look at how GTM teams can generate these kinds of insights and opportunities.

Technologies Needed	Data integration tools to centralize datasets AI/ML platforms capable of unstructured data analysis Business Intelligence (BI) dashboards to visualize findings
Processes Required	Aggregate disparate datasets, including product usage logs, transactional data, and external data sources (e.g., market reports) Use machine learning models to identify patterns, anomalies, and correlations within the data Validate findings with subject matter experts to confirm business relevance
Complexity Level to Execute	High. Requires cross-functional collaboration between data science, marketing, and product teams. Aggregating and cleaning large datasets can be resource-intensive

Use Case 2: Listening to Customer Frustrations at Scale

Natural Language Processing (NLP) capabilities enable AI to process millions of text inputs, such as support tickets, open-ended survey responses, and even online forums. By categorizing and analyzing this language data, AI can uncover emerging customer pain points that traditional keyword analysis might overlook.

For instance: In analyzing customer support logs, an AI system for a SaaS provider spots recurring user frustration with integrating third-party tools. This insight leads the company to prioritize development of native integrations, creating a new competitive advantage. Demand gen can then engage this previously untapped market segment. 

Here’s a quick breakdown of how a GTM team can generate this market demand.

Technologies Needed	Natural Language Processing (NLP) tools Data storage for unstructured text Survey or customer feedback platforms to capture qualitative data
Processes Required	Gather unstructured text data, such as customer support tickets, survey responses, and online reviews Apply NLP algorithms to categorize feedback and extract recurring pain points Cross-reference findings with customer profiles and product features to identify latent demand.
Complexity Level to Execute	Medium. Implementing NLP is moderately technical, but many off-the-shelf solutions make this process accessible

Use Case 3: Spotting Opportunities from Unexpected Industries

AI is also great at spotting opportunities in which one industry’s solution could be repurposed for another industry. This empowers companies to diversify into adjacent markets.

For Instance: A logistics software firm uses AI to analyze shipping behaviors in the pharmaceutical sector. The analysis reveals a growing need for temperature-sensitive tracking — a capability the company is well-positioned to adapt for a new market. And naturally, marketing can engage this segment with relevant demand gen outreach.

Here’s how a GTM team can spot and seize these kinds of opportunities.

Technologies Needed	Industry-specific data feeds or APIs Machine learning tools for clustering and behavioral analysis Visualization tools to track cross-industry trends
Processes Required	Collect behavioral data from industries adjacent to your target market Train clustering models to identify commonalities between industries Identify transferable use cases or demand signals for your product or service
Complexity Level to Execute	High. Cross-industry analysis requires specialized expertise to interpret data and ensure findings are actionable

Real-Time Optimization of Campaigns for Emerging Segments

Here’s another way AI can bring disruptive change to marketing and demand generation. 

Armed with historical data and manual analysis, GTM teams have mastered A/B testing and performance monitoring to guide incremental improvements. But this process can’t keep pace with nascent, dynamic market segments, where behavior and preferences shift rapidly.

AI transforms campaign optimization into a continuous, real-time process. Instead of waiting for post-campaign analysis, GTM teams can pivot mid-campaign, maximizing performance in fast-evolving market conditions.

Real-Time Campaign Optimization Use Cases

Use Case 4: Dynamic Feedback Loops for Real-Time Adjustments

AI systems ingest performance data (such as click-through rates, engagement metrics, conversion rates) in real time and use ML models to identify trends, anomalies, and areas for improvement. These insights can inform immediate changes to targeting, messaging, or channel allocation — automatically.

For instance: A B2B fintech company targeting early-stage startups uses AI to monitor campaign performance. When engagement dips among seed-funded companies, AI adjusts the messaging to emphasize cost-efficiency, instantly reversing the decline and boosting conversions.

Here’s one way to detect similar opportunities and swiftly capitalize on them.

Technologies Needed	An AI analytics platform for real-time tracking An ML platform to analyze performance data and identify anomalies An ad tech/marketing automation tool to adjust targeting and messaging dynamically
Processes Required	Integrate performance tracking tools with AI analytics platforms to ingest real-time data (e.g., click-through rates, conversions) Train ML models to recognize patterns and detect performance dips or spikes Automate campaign adjustments based on AI insights, such as reallocating spend or refining creative
Complexity Level to Execute	Medium to high. Requires strong integration between data platforms and automation tools. Custom model training adds complexity

Use Case 5: Predictive Modeling for Audience Segmentation

AI doesn’t just react — it predicts. By analyzing emerging patterns in behavior and intent data, AI can suggest new micro-segments to target before they fully materialize. This empowers GTM teams to stay ahead of the curve … and the competition.

For instance: A cybersecurity firm identifies a small but growing group of mid-market businesses engaging with content about “zero-trust architecture.” AI flags this as an emerging segment and reallocates ad spend to target this group, resulting in a significant increase in lead quality. Here’s how you might do the same.

Technologies Needed	An AI-powered Customer Data Platform (CDP) to consolidate and analyze customer dataAn intent data platform to identify emerging customer behaviors and intent signals
Processes Required	Aggregate behavioral, demographic, and firmographic data into a unified CDPApply predictive models to identify emerging micro-segments with high conversion potentialRefine messaging and offers for these segments using insights from intent data
Complexity Level to Execute	High. Requires access to diverse, high-quality data sources and sophisticated analytics tools

Use Case 6: Channel Optimization in Fast-Moving Markets

AI can evaluate channel performance in real time, determining where specific segments are most active — and adjusting ad spend allocation dynamically.

For instance: A SaaS company targeting remote teams notices that engagement on LinkedIn is outperforming email for a new segment. AI shifts budget allocation mid-campaign, resulting in a much higher engagement rate with no additional spend.

Technologies Needed	Cross-channel attribution tools for real-time channel performance measurement AI-powered media buying platforms to dynamically adjust ad spend.
Processes Required	Monitor channel performance across multiple platforms (e.g., LinkedIn, Google Ads, email) Use AI to identify high-performing channels and reallocate budgets mid-campaign Test and implement channel-specific creative for emerging trends or segments
Complexity Level to Execute	Medium. Most tools provide plug-and-play solutions, but aligning insights with creative strategy may require manual input

Additional Use Cases

Automating Persona Development

In today’s dynamic environment, customer needs evolve rapidly and microtrends can emerge and vanish within weeks. Thankfully, AI can uncover hidden, transient, or rapidly evolving personas that traditional methods miss. Combining predictive modeling, real-time data ingestion, and behavioral analysis, GTM leaders can identify and adapt to emerging personas before competitors even realize they exist.

Hyper-Localized Market Creation Opportunities

Marketers have long supported localized products and services with campaigns tailored to specific geographies, languages, and industries. But they can’t uncover truly lucrative micro-opportunities within regions or niche industries. AI can do just that, transforming traditionally broad, generic campaigns into bespoke, hyper-relevant engagement — all at scale.

How Solutions Like ZoomInfo Help

ZoomInfo’s comprehensive B2B sales and marketing solution operationalizes this kind of demand creation through a combination of advanced AI capabilities, vast datasets, and seamless integration into existing workflows. Here are a few ways:

• ZoomInfo’s market insights uses predictive analytics to analyze massive datasets to surface patterns and trends that point to latent demand
• Growth acceleration tools enable GTM teams to create dynamic customer personas and refine their approach based on real-time data
• ZoomInfo’s intent signals identify which accounts are actively searching for or engaging with topics related to a company’s offerings
• And our sales automation suite optimizes lead scoring and outreach workflows, empowering teams to scale their efforts without sacrificing precision

This delivers outcomes that are difficult or impossible to achieve with other approaches, like:

• Quicker time-to-market by delivering actionable insights in real-time to launch campaigns faster and with greater confidence
• Higher ROI on outreach thanks to granular insights and AI-driven recommendations that precisely allocate resources, reducing wasted spend, and improve ROI
• High-quality pipeline and revenue by analyzing trends across industries and geographies to uncover opportunities GTM teams didn’t know existed

Conclusion: The Necessary Shift to Demand Creation

The evolution from demand generation to demand creation represents a fundamental rethinking of how markets are defined, approached, and expanded. And AI is the linchpin in making this happen. Unlike traditional tools that optimize existing processes, AI: 

• Identifies opportunities beyond human reach: AI uncovers patterns, trends, and behaviors that were previously invisible.
• Accelerates actionable insights: GTM teams can dramatically reduce time-to-market and seize early-mover advantages.
• Enhances agility: Leaders can pivot strategies dynamically, effortlessly adapting to market shifts.

Now’s the time to embrace demand creation as a strategic priority. By adopting powerful AI-driven tools, leaders can not only stay ahead of the curve but also shape the curve itself — to drive growth, innovation, and market leadership with unprecedented precision.