Speed is the part of the artificial intelligence (AI) story everyone leads with. Teams that used to wait days for analyst-generated insights can now get answers in seconds. While there is a lot of AI hype and talk about transformational change sometime down the road, we’re seeing tremendous opportunity across key verticals right now.
But with a new world of data value being uncovered, it’s critical to consider who can access that intelligence now and what happens when they act on it without questioning what it’s built on.
Technical expertise and analyst resources have historically been blockers to accessing meaningful value from mobility and location datasets. An analyst would extract the data, normalize and organize it, run the analysis, and produce a report. That cycle could stretch across days or weeks depending on resource availability.
By enabling natural language interaction with complex mobility datasets, AI opens location intelligence to a much broader range of users. Data scientists still need to do the hard work of building accurate models that understand mobility data and can answer real business questions, but once that foundation is in place, anyone can access it. Marketers, strategists, and operations leaders can now ask ad-hoc questions and get decision-ready answers without SQL knowledge or BI tool experience.
AI is Democratizing Access to Insights
For demand gen and marketing operations teams, that means moving from static dashboards and scheduled reports to real-time, iterative analysis. Exploring audience behavior and competitive dynamics without waiting on a data team. Asking a question about campaign attribution or trade area overlap and getting an answer the same day, not next quarter.
This is a structural change in how teams can use data to drive performance. But the value of that access depends entirely on one thing: the quality of what the AI is drawing from. Speed is only an advantage when the underlying data is worth moving fast on.
From Insight into Action Across Verticals
Travel and Tourism. Destination marketers and travel brands are using AI to move beyond standard demographic profiles and surface-level visitor counts. With AI-assisted location intelligence, teams can identify high-value traveler segments through natural language queries rather than pulling custom reports, build targeting strategies around real demand shifts and seasonality, and model scenarios to evaluate spend allocation before committing to a campaign.
The result is a shift from reactive to adaptive strategy: campaigns informed by what’s actually happening right now. The caveat is that visitor segmentation and origin market analysis are only as reliable as the mobility signals powering them. Accurate, consent-driven data creates a targeting strategy grounded in real behavior, not built on noise.
QSR. For QSR brands, AI is accelerating decisions across site selection, operations, and competitive positioning. Teams can rank and evaluate new locations using trade area analysis, surface underperformance signals earlier, and run scenario models before committing resources, all without putting requests in a queue for the analytics team.
That speed matters in an industry where margins are tight and location decisions are long-term commitments. It also means the stakes of acting on bad data are higher. A site selection model built on inaccurate foot traffic signals produces a years-long operational commitment based on a wrong answer.
Retail. With the majority of purchases still happening in physical stores, retail teams are using AI to close the gap between audience insight and real-world store performance. Customer analysis that once took days can now be generated for a specific location in seconds. Trade area composition, digital-to-store attribution, and competitive overlap are now explorable in real time rather than surfaced through scheduled reports.
Teams that can identify patterns faster can act on them before they become problems. But pattern recognition is only useful when the patterns reflect reality. Retail teams that have tried to optimize store strategy on low-quality location data know the outcome: confident analysis pointing in the wrong direction.
The Question Worth Asking Before You Deploy Anything
Think about how quickly you lose trust in an AI tool when it gives you a confident wrong answer. That frustration happens fast, and it’s the same dynamic playing out at scale across every business deploying location intelligence right now. AI amplifies what’s in the data. If the signals are inaccurate or out of compliance with current privacy regulation, the system simply produces wrong answers faster.
In an increasingly regulated data environment, access to high-quality, privacy-compliant mobility data is becoming more constrained. The gap between platforms built on consent-driven, verified behavioral data and those built on whatever signals were available is widening. That gap shows up in the quality of the insights and defensibility of your strategy, and the exposure to regulatory risk when the data provenance doesn’t hold up.
For demand gen and marketing ops leaders, the question to ask before deploying any AI-powered analytics tool: what is this actually built on?
Speed is only an advantage if you’re moving in the right direction
Gladys Kong is the CEO of Azira, a consumer insights platform, where she previously served as COO before stepping into the top role. A seasoned tech entrepreneur with over two decades of experience, she pioneered one of the first geolocation-powered data intelligence platforms during her decade-plus at UberMedia, where she served as CEO and CTO. Gladys holds a B.S. from Caltech and an M.S. in Computer Science from UCLA, and has been recognized by Business Insider as one of the “30 Most Powerful Women in Mobile Advertising.”
