AppsFlyer has released its annual analysis of mobile app trends, revealing how AI shaped both consumer behavior and marketing strategy in 2025. GenAI adoption accelerated across the app ecosystem, with installs up 16% and category spend reaching US$824M across iOS and Android. GenAI apps ranked among the fastest-growing categories of the year, no.1 in Android and no.4 in iOS, reflecting their expanding role in creative, productivity, and AI assistant experiences.

AppsFlyer also analyzed AI agent usage for the first time, identifying how marketers are integrating AI into their performance workflows. The data shows that 57% of agent deployments focused on technical automation such as configuration and data-integrity checks, while 32% supported business optimization. Distinct usage patterns emerged across verticals: gaming marketers used agents to improve efficiency and protect margins, while retail and fintech teams relied on them to scale traffic and volume. These trends point to an early but meaningful shift toward supervised automation, where AI supports decision-making while marketers maintain strategic oversight.

“Many marketers say they are still struggling to measure clear ROI from AI, yet the adoption curve tells a different story,” said Inna Weiner, VP Product, Data and AI, AppsFlyer. “GenAI apps are accelerating in consumer adoption, and behind the scenes marketers are increasingly deploying agents to simplify workflows and improve efficiency. AppsFlyer remains committed to helping teams navigate this rapidly evolving landscape with the clarity and confidence they need to grow.”

Beyond the rise of AI in both apps and marketing workflows, the report outlines several broader trends shaping the app economy in 2025.

Additional Marketing Trends of 2025

• Global UA spend rises 13% to US$78B, driven entirely by iOS and mostly by investment from non-gaming apps: iOS user acquisition spend grew 35% while Android remained flat. Non-gaming increased 18% to US$53B, and gaming grew only 3% to US$25B.
• Remarketing expands as retention gains importance: Remarketing spend grew a significant 37% to US$31.3B, now representing 29% of all app marketing investment (up from 25% in 2024). iOS remarketing rose 71%, with notable gains in Transportation (+362%), Travel (+145%), and Finance (+135%).
• Shopping reshapes global UA spend distribution: Shopping investment to acquire new users rose 70% overall and 123% on iOS, driven by China-based ecommerce budgets that materially shifted category and regional share.

Authored by: Dr. Anurag Byala, CEO, Techies Infotech, a global digital transformation & commerce company

Everyone’s talking about ChatGPT. Big corporations are hosting AI transformation workshops. Consultants are selling million-dirham roadmaps. But here’s what nobody’s telling you: while enterprises are still forming committees to discuss AI adoption, UAE startups are already winning the race. And the best part? This might be the most level playing field we’ve seen in decades.

The great equalizer. Think about it. AI is still evolving. We’re on the ground floor. Whether you’re a billion-dollar conglomerate or a three-person team working out of DIFC’s Hub71, you’re accessing the same GPT-4, the same Claude, and the same open-source models. The technology doesn’t care about your market cap. Every business—big or small—is starting from the same line. The difference? Speed. And in the UAE’s startup ecosystem, speed isn’t just an advantage—it’s a necessity. It’s the entire game.

1.  AI-powered customer service that actually works

While corporates are negotiating year-long contracts with enterprise chatbot vendors, UAE startups are spinning up customer service agents in days. They’re using platforms like Voiceflow and Stack AI to build conversational interfaces that actually understand Arabic dialects—crucial in a market where your customer might switch between English, Arabic, and Urdu in the same sentence.

A fintech startup in Dubai recently told me they handle 87% of customer queries without human intervention. Their secret? They’re not waiting for perfect. They’re iterating weekly based on honest conversations, something a corporate legal team would take months to approve.

2.  Spec-driven development is killing traditional coding

Here’s where it gets interesting. Tech startups in the UAE have already started replacing traditional software development cycles with AI agents. Platforms like Cursor, v0 by Vercel, and Replit’s AI pair programmer aren’t just helping developers code faster—they’re letting non-technical founders build products.

You write the specification. The AI writes the code. You test it. You refine it. What used to take a team of developers three months now takes a founder with vision three weeks. And when you’re bootstrapping in Dubai’s Internet City or Abu Dhabi’s ADGM, that timeline difference isn’t just convenient—it’s survival.

3.  The incubator advantage nobody talks about

Dubai has an AI Campus, a Center of Artificial Intelligence, and the Mohamed bin Zayed University of Artificial Excellence. Abu Dhabi has Hub71 and ADGM’s RegLab. Sharjah has Sheraa. But here’s what makes these incubators special in the AI era: they’re knowledge accelerators.

When a startup in Hub71 figures out a clever AI workflow, they share it over coffee. When someone cracks multimodal search for e-commerce in a market with three languages, it spreads through the

ecosystem in days. Try getting that knowledge transfer in a corporate tower where departments don’t even share the same floor.

The UAE’s startup ecosystem isn’t just about funding anymore. It’s about collective intelligence moving at WhatsApp speed.

4.  Fail fast, fix faster—no corporate theatre required

Big corporations have a problem: failure is documented, analyzed, presented, and archived. Startups have a different relationship with failure—they expect it, learn from it, and move on before lunch.

Testing an AI cold email sequence? A startup tries five variations this week. A corporate marketing department schedules a meeting next quarter to discuss testing parameters.

Building an AI voice agent for bookings? A startup launches a beta to 50 customers on Monday—a corporation waits for legal, compliance, and brand approval, and three VP signatures.

The UAE’s regulatory environment, especially in free zones, enables this experimental velocity. You can test, iterate, and scale without navigating the bureaucratic maze that slows down established companies.

5.  AI sales teams that work while you sleep

UAE startups are deploying AI SDRs (Sales Development Representatives) that operate 24/7. These aren’t basic bots—they’re sophisticated systems that research prospects, personalize outreach, qualify leads, and book meetings.

A SaaS startup targeting regional enterprises told me their AI SDR reached out to 2,000 decision-makers last month, personalized each message based on LinkedIn data and company news, and booked 47 qualified meetings. Their human sales team of two focused entirely on closing deals.

Try getting that level of automation approved through a corporate sales enablement process. You’d still be filling out the business case template.

6.  Compliance is their moat—and your opportunity

Big corporates love to talk about their “robust compliance frameworks.” But here’s the dirty secret: compliance frameworks designed for 2019 don’t know what to do with AI in 2025.

Can we use LLMs to process customer data? That requires a privacy review, a security audit, a risk committee meeting, and a sign-off from three departments. By the time they get approval, the technology will have advanced two generations.

Startups, especially those in UAE free zones with clear regulatory sandboxes, can move faster. They’re building compliance into their AI workflows from day one, not retrofitting it into legacy systems.

7.  Small teams are the competitive advantage

Here’s the paradox: in the AI era, being small is better. A five-person startup can adopt a new AI tool across the entire company in a team meeting. A 5,000-person corporation needs change management, training programs, and a year-long rollout plan.

When GPT-4 launched, UAE startups rebuilt their products in weeks. Corporations formed AI steering committees that are still meeting quarterly to discuss strategy.

The hierarchy that once signaled strength—layers of management, specialized departments, approval chains—is now dead weight. AI rewards agility, and agility lives in small teams.

The race you didn’t know you were winning. If you’re running a startup in the UAE right now, you’re sitting on an asymmetric advantage that won’t last forever. Big corporations will figure this out eventually. They’ll hire the consultants, restructure their teams, and deploy AI at scale. The question isn’t whether you can compete with corporates using AI. The question is: how much ground can you cover before they even get started? The starting line is level. The finish line hasn’t been drawn yet. And in the UAE’s startup ecosystem, you’ve got everything you need—the infrastructure, the community, the regulatory environment, and most importantly, the speed—to win this race. The only question left is: are you running fast enough?

More about the author: Dr. Anurag Byala is a business leader with 15+ years of experience in technology, helping eCommerce and digital businesses scale across the GCC. His journey—from working at a multinational corporation to founding Techies Infotech—has been centered on building solutions that genuinely move the needle for customers. Along the way, he completed his Doctorate in Business Administration at ESC Clermont Business School in France, where he focused on consumer behavior in

e-commerce. Under his leadership, Techies Infotech has grown into a global player, delivering

AI-powered software solutions that enable faster time-to-market, greater cost efficiency, and compliance with international quality standards. The company serves enterprises across Digital Transformation, Tech Consulting, eCommerce, and Software Development in MENA and beyond. Dr. Byala has been recognized as one of the Top 50 Business Growth Leaders in Technology at the BizzTalk World Conference. He is also a mentor to startups and an active contributor to industry forums, passionate about scaling businesses and building future-ready teams. Outside work, he enjoys playing cricket and padel, exploring new places, and spending time with family.

Mala Pillutla, Vice President of Sales for Log Management, Dynatrace

Without logs, it would be almost impossible to keep modern applications, cloud platforms, or customer-facing services running efficiently. Some might argue that logs are one of the most critical but least celebrated sources of truth in the digital era.

At its core, log management is about turning raw system logs — unprocessed, detailed records of a system’s activities, including server actions, user interactions, and error messages — into actionable insights. As digital systems grow in scale and complexity, logs have evolved from a backroom tool into a critical driver of reliability, performance, and security across an entire business.

From a website crashing or pages loading too slowly, to customers encountering errors or even early signs of a cyberattack, logs provide teams with a clear view of what’s happening inside their digital systems. Within an observability platform, they present the detailed “story” behind these events, helping teams move from simply knowing something is wrong to understanding why it’s happening and how to fix it before it impacts users.

Research has found that 87% of organizations claim to use logs as part of their observability solutions. That number shows how universal log usage has become. The question now is whether businesses are unlocking their full value. Collecting logs is one thing but interpreting them is another.

For too long, logs have been treated as clutter, something to store, sift, and forget. The reality is that they’re one of the clearest signals of how a business is running. Modern log management makes those signals impossible to ignore.

The limits of traditional log management

As business digital estates grow more complex, the volume of logs generated across applications, infrastructure and business services has exploded. However, more logs do not automatically mean more insight. In fact, many teams are overwhelmed by sheer volume, struggling to separate meaningful signals from background noise. This overload creates noise that makes it difficult to identify urgent issues, leaving IT and Security teams on the back foot during critical incidents and proactive response.

The problem is as much about cost as complexity. Storing and managing log telemetry without a clear purpose often leads to escalating expenses that outpace the value delivered. Traditional licensing and infrastructure models add to the problem. They often make log management feel like a financial liability than a strategic advantage.

Another common constraint is fragmentation. Logs often live across multiple tools, with different interfaces and storage models, slowing root cause analysis and complicating cross-team collaboration. In a cloud-native world where speed and scale are vital, this siloed approach is out of step with modern business needs.

Together, these shortcomings point to the need for a smarter approach—one that focuses on clarity, efficiency, and value.

Turning logs into actionable intelligence

Taking a smarter approach to log management starts with a shift in perspective. Rather than treating logs as an endless stream of technical data, leading organizations use them as a lens to understand how their digital ecosystems truly perform. The real value lies in not collecting everything but in knowing what matters and identifying which logs drive resilience, security, customer experience, or compliance, and filtering out the rest.

AI is becoming an essential part of this process. Modern techniques can detect anomalies, trace issues back to their root cause, and even trigger automated fixes. This reduces manual investigation and accelerates recovery, allowing teams to move from firefighting to foresight.

Equally important is being selective. Forward-thinking organizations decide which logs to capture, which to discard, and how to route them most effectively. This helps control costs and ensures that attention is focused on the telemetry that delivers the greatest value. When organizations find this balance, log management evolves from a tactical task to a strategic capability that strengthens both performance and resilience.

Observability and the bigger picture

Log intelligence on its own is valuable, but it is only part of the story. The next frontier is AI powered observability, uniting logs with metrics that track performance, traces that map interactions, and events that reveal key system changes. Combined in a single platform, these data types give teams a complete picture – connecting technical performance with genuine business impact and moving from a view of what happened to an understanding of why it happened and how to respond quickly.

Consider a global telecommunications provider that recently re-evaluated its log strategy. Managing more than 15TB of logs every day, stored for long periods and spread across thousands of dashboards, the team was buried in dashboards and redundant data. By consolidating logs within a broader observability framework and replacing static alerts with intelligent detection, they cut through the noise across its systems. Able to focus on the signals that mattered most, the organization improved uptime, speed, and overall resilience.

This example shows that observability delivers its greatest value when it helps teams cut through complexity. With logs feeding into a single platform, data becomes easier to interpret and act on, transforming technical insight into business intelligence.

Unlocking the true value of modern log management

Modern log management gives organizations the context they need to turn massive volumes of data into meaningful insight. Organizations that harness AI, automation, and broader observability, gain a clearer view of how their technology is supporting their goals. Enterprises can analyse faster, automate smarter, and innovate with confidence.

True modernization comes from changing how teams think about data. Now is the time to review current strategies, identify gaps, and adopt modern platforms that integrate AI, context, correlation, and smarter telemetry management practices because organizations can no longer afford to treat log management as a background IT task. The companies that thrive will be those that treat logs not as exhaust from their systems, but as evidence of how their business thinks and performs. By bringing intelligence to the data they already have, they will turn observability into a source of continuous advantage and understand their business like never before.