Across the GCC, the real question is no longer whether organisations are using AI, but whether AI is actually doing the work. Most deployments still sit at the surface, assisting employees without changing how execution happens. AI is now moving beyond individual task support into structured workforce roles, where it carries responsibility across workflows, follows business logic, and executes within real enterprise systems. Gartner projects that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.

In the GCC, organisations are under pressure to scale faster, maintain service continuity, and improve cost discipline without adding unnecessary operational complexity. Digital Dubai recently launched the AI Workforce Transformation Program (AI+) to help train 50,000 government employees for an AI-ready workforce.

Shaffra, an AI research and applications company building autonomous AI teams for enterprises and governments, is already deploying this model across the region. The company highlights five business functions where AI is actively executing work inside organisations.

1. Customer service

One of the first functions to absorb AI as a workforce layer is customer service due to high-volume, time-sensitive, process-intensive requests every day. Autonomous AI Teams can handle routine queries across chat, email, WhatsApp, voice, and ticketing platforms while classifying urgency, routing cases, escalating exceptions, and updating records in real time. They can also pull customer history and identify recurring patterns linked to churn, complaints, or policy friction. Customer service teams have handled up to five times more queries through autonomous execution. This shifts customer service from a reactive support function into a continuously operating system that can absorb demand without linear increases in headcount.

2. Revenue operations

A more meaningful transformation is now happening in the commercial engine. Autonomous AI Teams can continuously monitor pipelines, detect stalled deals, flag procurement delays, identify pricing sensitivity, and improve forecast quality using live activity signals rather than backwards-looking updates. They can also support CRM hygiene, proposal workflows, approval chains, and internal coordination between multiple departments around account progression. PwC’s 2026 findings show that 45% of UAE CEOs are already using AI in demand generation across sales, marketing, and customer service. Leadership gets a clearer view of where revenue is genuinely at risk, where process friction is slowing conversion, and where intervention is needed before exposure turns into loss.

3. Human resources

In HR, recurring administrative work, policy enforcement, documentation, and employee support often follow structured paths that can be executed better when properly designed. Autonomous AI Teams can screen applicants, coordinate interviews, manage onboarding steps, answer routine employee questions, and flag missing approvals or documentation before delays compound. They can also support review cycles, workforce planning, and identify bottlenecks and process gaps early. Recruitment timelines are reduced from weeks to hours, while HR leaders review high-impact decisions.

4. Finance and accounting

In the financial department, AI needs to operate reliably within structured processes without compromising strict governance. Autonomous AI Teams can process invoices, support AP and AR workflows, follow up on missing information, review expenses against policy, and coordinate reconciliation and month-end close activities. They can also surface anomalies, identify unusual transaction patterns, and flag control exceptions for review. AI helps increase throughput while preserving auditability, approval discipline, and visibility across the finance operation. This allows finance teams to increase processing capacity without compromising control, shifting their role to oversight from execution.

5. Business operations

The most strategic application sits in business operations – where delivery, dependencies, handoffs, service levels, and internal performance come together. McKinsey’s finding that 84% of GCC organisations have adopted AI in at least one business function suggests the region is already moving into broader integration. Within operations, Autonomous AI Teams track workflows across systems, detect bottlenecks, monitor KPIs and SLAs, identify resource overload, and trigger interventions before issues become delivery failures. They can also support oversight by summarising status, escalating likely delays, and coordinating cross-functional execution in real time. Across Shaffra deployments in the Gulf, organisations have reported up to 80% reductions in operational costs and more than 2 million manual work hours saved monthly.

Walk into any modern content setup today, whether it’s a podcast studio, a corporate webinar room, or a hybrid event environment, and you’ll see a familiar pattern, one that reflects how fragmented the content production stack has become.

A microphone connected to an interface.
An interface connected to a laptop.
A laptop running multiple layers of software to mix, switch, stream, and record.

It works, but it’s rarely seamless.

Because the biggest challenge in content creation today isn’t access to tools, it’s understanding how they all fit together.

The Real Problem: Too Many Tools, Too Little Clarity

The rise of podcasting and video content has created a new kind of friction. Users are no longer asking what they can create; they are asking how to make the tools work together.

Recording audio separately, syncing video later, transferring large files to high-end machines, and relying on multiple software layers have become the default workflow. It works, but it is inefficient, expensive, and prone to failure.

The expanding ecosystem of devices, features, and formats has made even basic setup decisions unnecessarily complex.

When it comes to products from RØDE, users & creators already recognize the product’s potential to simply clarify and help elevate the overall workflow experience.

From Tools to Unified Systems

This is where the shift begins to stand out.

What we are seeing is not simply the addition of new features, but the consolidation of functions.

Mixer. Recorder. Audio interface. Video switcher. Stream encoder.

What traditionally required a stack of hardware and software is now being brought into a single console environment.

For creators, that simplifies production.

For enterprises, it changes how content infrastructure is designed.

As this shift gains momentum, it is also being acknowledged at a leadership level.

“Real innovation isn’t about adding more; it’s about removing friction and enhancing workflows.

With the introduction of platforms like the RØDECaster Video, we’re starting to see audio and video unified in one system, unlocking faster, more focused creative output.”

Kalinda Atkinson,
Global Marketing Director, RØDE

Why This Matters Beyond Creators

This shift is not limited to podcasters or streamers. Enterprises are increasingly building in-house content studios, executive communication channels, internal video platforms, and hybrid event capabilities as part of their broader communication strategy.

In these environments, complexity quickly becomes a bottleneck. Multiple tools often translate into longer setup times, increased points of failure, and a growing dependency on technical operators to manage what should ideally be straightforward workflows.

A unified system begins to reduce that friction, allowing teams to focus less on managing the process and more on the output itself.

The End of the Laptop-Centric Setup

One of the most significant changes is subtle: the laptop is no longer central.

With recording, streaming, and switching built directly into the console, content can now be produced without relying on external software or intermediary platforms. Audio and video routing happens natively within the system, removing the need to manage multiple layers of tools.

This, in turn, reduces reliance on tools like OBS Studio and lowers the need for high-performance machines in the production chain.

Broadcast Capabilities, Simplified

Features that were once limited to broadcast environments are now being integrated directly into compact systems. Capabilities such as multi-camera switching, ISO recording with separate tracks for each input, audio-based automatic switching between speakers, and network-driven video workflows like NDI are no longer confined to high-end production setups.

For enterprise teams, this translates into professional-grade production without the need for dedicated control rooms or complex broadcast infrastructure.

Modularity Signals Long-Term Thinking

Another important shift lies in how these systems evolve over time.

With expansion options such as adding video capabilities to existing audio consoles, RØDE is enabling a more modular approach to production. Instead of replacing entire systems, users can extend them based on their needs.

This becomes particularly relevant for organizations that may begin with audio-first content using consoles such as the RØDECaster Duo or RØDECaster Pro II, gradually expanding into video production with consoles such as RØDECaster Video, RØDECaster Video S, or even the RØDECaster Core, and scaling internal media capabilities over time. The result is a more flexible investment model that reduces upfront costs while supporting long-term growth.

A Shift in the Competitive Landscape

On the surface, this still appears to sit within the audio hardware category. In practice, however, it competes with something far broader.

As these systems begin to handle capture, processing, and output within a single environment, they start to overlap with production software ecosystems, video switching platforms, and content workflow tools.

The implication is clear: when orchestration happens within the system itself, the need for external layers begins to diminish.

The Opportunity Ahead

As the layers of complexity fade, creators will have more time for creative storytelling and less time worrying about the setup.

The new products and technology from RØDE not only remove setup barriers, but they also enable creators & enterprises to operate at a full professional standard, accelerating both the creativity and innovation ecosystems.

Srijith KN covers enterprise technology, media infrastructure, and digital transformation across the Middle East.

By Chris Erasmus, Country General Manager, AWS United Arab Emirates & RoMENA 

For years, Earth observation workflows required building specialized models for every task — a labor-intensive process that presented significant scaling challenges. Transformer-based vision models are rewriting the rules of planetary monitoring.

Geospatial foundation models (GeoFMs) — including Clay, Prithvi-100M, SatMAE, AlphaEarth, OlmoEarth and SatVision-Base — transform this paradigm through self-supervised learning, pre-training on massive unlabeled datasets to master the fundamental patterns, textures, and spatial relationships embedded in geospatial data. The result? Models that understand what “Earth” looks like can be fine-tuned for specific applications using a fraction of the data and time previously required.

Amazon Web Services (AWS) provides the specialized infrastructure necessary to handle the unique demands of GeoFMs. These transformer-based vision models offer a new way to map the earth’s surface at continental scale.

The Shift to Foundation Models

Historically, analyzing satellite imagery required supervised learning, where experts manually labeled thousands of images to teach a model to identify specific features. This approach is often brittle, as models trained on one geographic area frequently fail when applied to another.

GeoFMs leverage masked autoencoders (MAE) to pre-train on unlabeled geospatial data sampled globally. This self-supervised approach ensures diverse ecosystems and surface types are represented, creating general-purpose models that understand Earth’s fundamental patterns without requiring extensive labeled datasets for every new application.

Scaling Earth Observation with AWS

AWS is designed to provide specialized infrastructure to handle the unique demands of GeoFMs, which involve massive file sizes and complex coordinate systems. Data at Scale: Through the Registry of Open Data on AWS, users access petabytes of imagery (like Sentinel-2) without moving it. This “data-gravity” approach minimizes latency and egress costs. Purpose-Built Tooling: Amazon SageMaker offers integrated environments to build, train, and deploy these models. SageMaker AI Pipelines supports the automated “chipping” of raw imagery into manageable 256×256 pixel segments for analysis. Compute Power: Training GeoFMs requires intense GPU resources. AWS GPU instances are designed to provide distributed computing capabilities to process global-scale datasets efficiently.

Core Use Cases for Planetary Intelligence

The integration of GeoFMs on AWS supports three core capabilities:

• Geospatial Similarity Search: GeoFMs convert imagery into high-dimensional vector embeddings. This allows for “image-to-image” searching where a user can select a reference area—such as a specific crop type or an area of urban sprawl—and instantly find similar patterns across vast territories.
• Embedding-Based Change Detection: By analyzing a time series of embeddings for a specific region, analysts can pinpoint exactly when and where surface disruptions occur, such as identifying early signs of forest degradation before they expand into large-scale clearing.
• Custom Machine Learning: Organizations can fine-tune a lightweight “head” on top of the GeoFMs. This allows for high-accuracy tasks like semantic segmentation (classifying every pixel in an image) with significantly less training data than traditional models.

Real-World Impact

The practical application of these models is already driving innovation. In the Amazon rainforest, researchers are using the Clay foundation model on AWS to detect subtle signatures of selective logging and new access roads. This early detection allows environmental protection agencies to deploy resources precisely to prevent major forest loss.

The solution is highly adaptable; while current examples focus on the Amazon, the same pipeline architecture works seamlessly with various satellite providers and resolutions to address challenges across industries like agriculture, insurance, energy and utilities, disaster response, and urban planning.

The Future of Earth Observation

While geospatial data pipelines remain essential, GeoFMs on AWS dramatically reduce the burden through shorter training cycles with fine-tuning or zero-training approaches like embedding-based similarity search. This enables organizations to focus on solving pressing environmental and economic challenges. The technology is ready. The question now is how quickly organizations will adopt these tools to address these challenges that demand immediate action.