AI in OOH: Can algorithms decide the perfect billboard location?

Location selection in Out-of-Home (OOH) advertising has always been rooted in one fundamental principle: audience movement. Every day, people move through structured yet dynamic journeys commuting to work, running daily chores, visiting retail clusters, socialising, travelling through transit hubs, and navigating countless moments in between. These movements define opportunity.

A significant influx of data around Points of Interest (POIs) – corporate districts, residential clusters, malls, highways, airports, and entertainment hubs – further reinforces the importance of selecting the right locations. Each POI signals intent, behaviour, and motivation.

Artificial intelligence acts as a powerful analytical layer that decodes this vast mobility and POI data at scale. By processing movement patterns, traffic flows, dwell times, time-of-day variations, and behavioural clusters, AI can:

• Identify high-intent movement corridors
• Map recurring audience journeys
• Detect peak exposure windows
• Cluster locations based on behavioural similarities
• Extract strategic locations aligned to real-life moments

Instead of asking, “Which road has the highest traffic?”, AI enables planners to ask, “Which location intersects with the most relevant audience segments and moments?”

However, location selection is only one layer of OOH planning.

Equally important is understanding whether the OOH assets within that location are actually noticeable.

Within the same stretch of road, there may be multiple OOH assets, yet each can deliver vastly different attention spans. Factors such as positioning, height, viewing angle, traffic speed, visual clutter, surrounding competition, and physical obstructions directly impact visibility and cognitive absorption.

This is where AI extends beyond movement intelligence into attention intelligence.

Through computer vision and machine learning, AI simplifies complex visibility dynamics by:

• Mapping attention zones within traffic movement
• Measuring dwell time and exposure duration
• Evaluating obstruction and clutter levels
• Assessing visual saliency of each OOH asset
• Ranking assets within the same location based on noticeability

In totality, an optimised OOH plan should reflect:

1. People – Who is moving? (working professionals, business travellers, affluent residents, students, retail shoppers, decision-makers, families)

2. Moments – Why are they moving? (work commute, leisure, errands, high-consideration purchase journeys)

3. Behaviour – How are they moving? (driving, chauffeured, public transport, walking, speed and dwell patterns)

4. Environment – Where are they moving? (corporate hubs, retail clusters, highways, transit zones, residential pockets)

5. Placements / Asset Selection – What are they most likely to notice? (positioning, prominence, visual dominance)

To summarise, a strong OOH strategy follows a clear progression:

People → Places → Placements

People define the audience segments. Places decode their movement environments and intent-rich corridors. Placements ensure the selected OOH assets are not just present but truly noticeable.

AI does not replace strategic thinking. It accelerates it. It builds recommendations faster, processes complexity at scale, and reduces subjectivity, ensuring campaigns are delivered to the right segments, in the right moments, within the right environments, on OOH assets that genuinely command attention.

That said, AI is only as powerful as the data that fuels it. High data quality, structured representation, and mutually exclusive yet collectively exhaustive (MECE) segmentation are essential. If mobility signals, POI classifications, or audience clusters overlap or lack completeness, recommendations risk missing the real picture.

AI should therefore be viewed as an advanced analytical layer, one that can be meaningfully adopted only when data standards are robust and model accuracy consistently meets high benchmarks. When those foundations are strong, AI does not just optimise OOH planning; it elevates it into a scalable, measurable, and intelligence-driven discipline.

(Mangesh Shinde, Co-founder of Osmo, blends 15+ years in analytics, media strategy & tech to drive change, shaping OOH advertising with AI-driven attention insights.)