Christiana Maxion, Founder and CEO of MAXION

Consumer apps have never been easier to use. With AI improving navigation, personalization, and responsiveness, platforms now offer a far more seamless experience, helping users move through tasks, content, and decisions with little visible effort. But convenience alone is not the same as value. Recent research found that the average adult now spends 88 days a year on their phone, highlighting both the scale of digital dependence and the urgency of building products that deliver something more meaningful than another scroll session.

Concurrently, expectations have changed. McKinsey has reported that 71% of consumers expect personalized interactions, while KPMG’s UAE research shows that integrity has now overtaken personalization as the strongest driver of customer experience. People still want services that understand them, but they also want trust and clarity that technology is working in their interest.

This is the backdrop for the rise of behavioural AI in consumer apps. The next phase of app design will be judged by its ability to predict what a user may click next, and more by how well it turns intent into action with less friction.

The problem with designing for activity, not action

For years, most consumer platforms have optimized for clicks, scroll depth, watch time, and repeat visits. Those metrics are useful, but incomplete. They show that a user remained active, not whether the user made progress.

A person may spend 20 minutes in a fitness app and still not complete a workout. A user may open a finance platform several times and still delay a decision. Someone on a social app may swipe through dozens of profiles and leave with no meaningful connection, no meeting arranged, and no clearer sense of what they are actually looking for. In each case, the platform can still record engagement, even while the user experiences indecision, overload, or disappointment.

That is why the intention-action gap has become such an important issue in consumer technology. Most people do not fail to act because they lack interest. They fail because friction builds up. Too many options, poor timing, and repetitive interfaces make follow-through harder than it should be. Traditional engagement design often worsens that problem because it rewards prolonged activity instead of successful resolution.

How behavioural AI changes the model

Behavioural AI is valuable because it looks beyond isolated clicks and interprets patterns in context. It can identify hesitation, momentum, preference shifts, and likely drop-off points. More importantly, it can respond to those signals in ways that make decisions easier and outcomes more achievable.

That changes the app’s role. Instead of acting primarily as a feed, a storefront, or a passive interface, it starts to function more like an active guide. It can narrow choices when users are overwhelmed, surface the next best action when intent is clear, and adapt when behaviour suggests a mismatch. This can mean recommending fewer but better options, improving prompts, changing timing, refining compatibility logic, or reducing unnecessary steps between interest and action.

The commercial relevance of this shift is growing. SAP reported that 82% of UAE marketers say AI is central to their personalization efforts, yet only 31% of consumers believe brands actually personalize content to their needs. Data and automation alone are not enough. Relevance depends on using insight in ways that feel useful, proportionate, and credible to the user.

From digital engagement to real-world outcomes

Behavioural AI becomes especially powerful in categories tied to everyday behaviour and human relationships. In social discovery, for example, the challenge has never been a lack of available profiles. It has been helping people move from superficial activity to meaningful connection.

That is where a social platform like MAXION sits within a more important conversation about the future of consumer apps. Success should not be measured only by how many profiles a person sees or how long they stay active on the app. It should be measured by whether the app improves the quality of interactions and increases the likelihood of real-world meetings.

Behavioural AI can support that by learning from interaction patterns. It can identify where conversations stall, what kinds of introductions lead to better follow-through, how timing affects responsiveness, and which recommendation patterns create genuine alignment rather than short-lived engagement. That creates the possibility of designing around success signals that matter outside the app.

This is also highly relevant in the UAE, where AI adoption is already part of everyday life. KPMG reported that 97% of UAE respondents use AI for work, study, or personal purposes. That level of familiarity creates a more sophisticated user base.

The broader point is that consumer AI is becoming more outcome-oriented. Whether the category is education, wellness, finance, or social connection, the products that stand out will be those that reduce noise, respect user intent, and drive real-world progress. The next generation of successful apps will be defined by how effectively they help people do something worthwhile with them.

Alexander Merkushev, Head of AI projects, Yango Tech

Business pressure rarely arrives in a neat or predictable form. It builds through overlapping demands, such as customers expect faster responses, regulators expect tighter control, leadership teams need clearer visibility, and frontline staff are asked to deliver all of this through systems that often do not move at the same speed. In stable conditions, organisations can usually work around those gaps. Teams compensate manually, service holds together, and inefficiencies stay partly hidden. In high-pressure environments, that buffer disappears. Slow workflows, fragmented systems, and manual bottlenecks become visible very quickly because the organisation no longer has the time or flexibility to absorb them. That is where the case for AI agents becomes much more practical. AI agents are most valuable when they allow businesses to extend operational capacity, where adding more people alone does not solve the problem fast enough.

This is especially relevant in the UAE, where digital maturity has raised expectations across both public and private sectors, with the UAE ranking 11th globally in the UN’s 2024 E-Government Development Index. This stronger digital environment has also raised expectations. Businesses need tools that can help them move quickly, stay consistent, and maintain control when pressure rises.

From Tools to Agents

With around 84% of GCC organisations adopting AI, it must prove its operational value. This is where autonomous AI agents stand apart from basic assistants. The lesson from digital transformation and automation is that technology creates the greatest impact where work cannot be carried out reliably at scale by people alone. That usually means high-volume, repetitive, rules-based, or time-sensitive tasks that still require consistency and traceability. A conventional assistant can answer a question, retrieve a document, or draft a message. An AI agent can operate across workflows, connect with enterprise applications and data sources, retrieve the information needed for a task, trigger an action, and escalate the case when human judgment is required. AI agents are less like a front-end convenience and more like a digital workforce layer that supports execution inside the business.

Keeping Service on Track

Customer service is often the first area where this becomes visible because it sits at the intersection of urgency, expectation, and reputation. When volumes rise, even strong teams can be slowed by manual routing, repeated verification, inconsistent answers, or language limitations. A customer support agent can handle thousands of routine queries across languages and channels without making customers wait for basic answers.

In fact, enterprise deployment data points to AI agents that can operate in 70+ languages, integrate with core business platforms such as CRM and support systems, and scale to handle 100,000+ interactions per day. Outcomes include 95% first-contact resolution, a 70% reduction in calls, and around 40% lower support costs. In a high-pressure environment, the benefit of an AI agent is that it helps the organisation respond at scale without allowing service quality to collapse under volume.

Compliance Under Pressure

Businesses often wrongly assume AI will automatically make operations faster, but the speed needs to be usable inside a controlled environment. If an agent cannot follow policy, log its actions, flag discrepancies, and escalate exceptions correctly, then it simply moves the risk somewhere harder to see. Well-designed AI agents can reduce delay by supporting documentation checks, rule-based workflows, anomaly flagging, and routing complex issues to the right human decision-maker while maintaining auditability.

For instance, Yango Tech’s AI debt collector agent can support repayment workflows, structure payment plan discussions, apply pre-set compliance rules, and manage routine follow-ups while flagging exception cases. A document analysis agent can review procurement files, compare them against required fields, and flag inconsistencies. The limits of disconnected tools are exposed very quickly in high-pressure environments, and businesses need systems that can work inside the operational environment that already exists.

Why digital workers are becoming relevant

In volatile conditions, where teams are stretched, leaders do not benefit from more dashboards or longer reports. Current industry findings show that organisations can lose 30 to 50% of efficiency to repetitive tasks. Too many skilled employees still spend time gathering updates, moving information between systems, or preparing routine reports instead of focusing on judgment, service recovery, and problem-solving. AI agents can absorb that repetitive load and help teams concentrate on higher-value work. They can surface relevant data from multiple systems, summarize key trends, identify pressure points, and reduce the delay between an operational change and a management response. Their role is to help leadership reach judgment faster, with better operational visibility and less reporting friction.

High-pressure environments reveal which technologies can support real execution. AI agents are most useful where organisations need to operate at a scale, speed, and consistency that people alone cannot sustain manually. But that only works when the system is designed with the right guardrails. Service quality, oversight, escalation logic, and traceability cannot be added later as an afterthought. Companies like Yango Tech create production-ready AI agents for high-pressure and fault-sensitive environments and help organisations deploy them in a governed, resilient, and reliable way under real operational strain.

By Khaled Nuseibeh, Hala CEO

Artificial intelligence has quickly become part of the marketing conversation. New tools promise faster production, lower costs and endless variations of creative output. But for companies operating in real-world services, the technology itself is not the most important question. The real question is whether it helps communicate what actually happens on the ground.

In mobility, that distinction matters. When someone books a taxi, the experience is defined by whether the car arrives when it is supposed to. If it does not, no campaign can compensate for that. That reality shaped how we approached Count on Hala, a recent campaign designed to support new user acquisition while reflecting how the service operates across Dubai every day.

Hala runs hundreds of campaigns each year across different customer segments. In a fast-moving, highly competitive market like Dubai, speed and adaptability are essential. Artificial intelligence provides companies with a way to move faster, scale creative output and respond to changing market dynamics without losing clarity or relevance.

The campaign used AI across the creative execution, generating visuals, layouts and voiceovers for content deployed across out-of-home screens and targeted digital channels. However, the strategic direction, messaging framework and approvals remained firmly with our team.

Rather than positioning AI as the centre of the campaign, we focused on communicating measurable operational insights such as pickup speed, fleet scale and reliability. Messages such as “90% of taxi pickups in under five minutes” or “Meeting in 20 minutes? Taxi in 3” translated everyday service performance into clear, relatable moments.

Early campaign indicators reinforce the impact of this approach. In the first month following the launch, Hala recorded a 27.8% uplift in bookings, 19.2% increase in new users, and a click-through rate approximately 5x higher than previous campaigns, reflecting stronger engagement with the campaign messaging and visuals.

AI allowed these insights to be translated into creative assets quickly across multiple formats. But the technology itself was not the story. Running the campaign highlighted several practical lessons about how AI fits into busy marketing teams today.

1. Build campaigns around operational performance, not creative concepts

AI will amplify whatever information it is given. If the underlying service is inconsistent, the campaign will expose that quickly. For this campaign, the creative concept began with operational data, pickup speeds, fleet capacity and everyday travel scenarios across Dubai. These insights formed the foundation of the messaging rather than an abstract creative idea. In sectors such as mobility and transport logistics or aviation, marketing cannot exist separately from operations. Customers experience the service within minutes of seeing the campaign. If the message and the experience do not match, a brand’s credibility will quickly disappear.

2. Use AI to produce campaigns faster without changing the strategy

The campaign began with a simple idea: reliability. In a city like Dubai, where people are constantly on the move, everyday convenience matters. Artificial intelligence helped the team turn that idea into campaign content much faster than traditional production would allow. Instead of coordinating multiple shoots, locations and long approval timelines, operational insights could be turned into clear messages quickly. Lines such as “Meeting in 20 minutes? Taxi in 3” could appear across digital screens, social media and billboards within hours rather than weeks. The team still defined the message, tone and brand standards, while AI helped speed up how quickly those ideas could be produced and shared across the city.

3. AI creative for billboards and outdoor advertising still needs technical expertise

One common misconception about AI-generated creative is that it removes complexity from production. In reality, it often introduces new challenges. Early AI-generated visuals worked well for digital placements but were not always suitable for large-format outdoor advertising. When scaled for outdoor displays, some images were grainy and lacked the resolution required for high-visibility formats.

Achieving the required quality meant using several paid subscription tools and refining outputs across multiple stages. AI can accelerate creative exploration, but production expertise remains essential to ensure the final output meets the standards expected of large-scale advertising.

4. AI marketing still requires strict legal oversight and brand governance

The faster content can be produced, the more important governance becomes. Before launching the campaign, strict internal guidelines were established around how AI could be used. These covered cultural sensitivity, representation and compliance with UAE advertising standards.

All platforms used were vetted to ensure appropriate commercial usage rights, and every output was reviewed in collaboration with legal teams before publication. Regardless of which tools are used, the brand remains responsible for everything that appears in a campaign.

5. AI allows marketing teams to focus on insight-led storytelling rather than asset production

The most noticeable shift from the campaign was internal. Traditionally, marketing teams spend significant time producing individual creative assets. AI changes where that time is spent, instead of focusing on manual production, the team concentrated on identifying the insights that matter most to our customers; people who are moving around the city, whether its short journeys or tight schedules, their need is for reliable transport in everyday situations.

Artificial intelligence then made it easier to translate those insights into multiple creative executions across different formats. For a platform operating in a competitive market and running campaigns across multiple audiences throughout the year, that shift can make a meaningful difference.

In almost every sector, AI is already moving from experimentation into everyday systems across the region. Airlines use it to manage disruption. Logistics companies use it to anticipate congestion. Governments use it to plan infrastructure and transport networks.

Marketing will inevitably follow a similar path. AI will not replace traditional production or human creativity. Photography, filmed content and real-world storytelling remain essential, particularly when authenticity and emotional connection to your customer matters.

While we continue to embrace AI within our creative processes, it has not and cannot replace the creative agencies we work with. Human intervention, intuition, and creativity remain at the core of everything we do.

What AI can do is remove some of the friction in how campaigns are produced, allowing teams to respond faster while maintaining accuracy. Dubai is often described as a testbed for new technologies. In reality, the city simply demands that systems work under pressure, across different languages, cultures and moments of high demand. If an AI-enabled campaign can operate effectively in that environment, it is likely to work anywhere.

For companies exploring AI in marketing, the lesson is straightforward: focus on operational reality first. Technology should support how the business performs, not distract from it.