6 Critical AI Trends for Executives

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Artificial intelligence (AI) is no longer a futuristic concept—it is the force reshaping industries, economies, and the very way businesses operate. As AI fuels a trillion-dollar economy, are you prepared for its impact? How will emerging breakthroughs in automation, AI agents, and quantum computing redefine leadership and decision-making?

This article uncovers the most critical AI trends of 2025, offering practical insights for executives navigating this fast-changing landscape.

1. Transformers and Their Evolution

Transformers, introduced in 2017, are the backbone of modern AI, revolutionizing natural language processing (NLP). Self attention, a core mechanism in transformers, allows models to focus on the most relevant parts of a sequence, such as understanding the relationship between words in a sentence. This innovation enables transformers to process entire sequences simultaneously, making AI applications faster, more accurate, and scalable.

In 2025, Google unveiled "Titans," an advanced transformer model that enhances processing speed, efficiency, and scalability through improved attention mechanisms and memory handling. Titans power real-time applications such as language translation, personalized AI assistants, and large-scale data analysis. This evolution in transformers has also paved the way for their application in multimodal systems.

Transformers in Multimodal Models

By extending their capabilities, transformers now power multimodal AI, integrating text, images, audio, and video seamlessly. Google's Gemini is a prime example, processing and generating multimodal content for richer, more interactive applications. This enables richer, more interactive applications that bridge different types of data for a unified AI experience. For example, Google's Gemini enables seamless cross-modal understanding, while Generative AI tools like Midjourney generate high-quality images from text, and CLIP links images with their contextual meaning. AI video generation is also advancing rapidly, with OpenAI's Sora, RunwayML Gen-2, and Kling producing ultra-high-quality video from text inputs, revolutionizing media production. These advancements are transforming industries like education and healthcare, media and firm making, music production, marketing and content creation, gaming, virtual reality, and customer engagement, offering more dynamic and versatile AI solutions that drive personalized user experiences and automated content generation.

2. Open vs. Closed Models and Small Language Models (SLMs)

The distinction between open-source and closed AI models is becoming more pronounced. Open models, such as LLaMA and Deepseek, offer transparency, flexibility, and cost efficiency, making them ideal for organizations with in-house AI expertise and specific customization needs. They are best for businesses looking to innovate, experiment, or build domain-specific applications.

Closed models, like OpenAI’s ChatGPT, prioritize ease of use, reliability, and vendor-managed security, making them ideal for businesses seeking rapid deployment and ready-to-use solutions. Closed models are a great fit when time-to-market, compliance, and predictable performance outweigh the need for deep customization.

For efficiency and adaptability, small Language Models (SLMs) are gaining traction. With fewer parameters compared to large language models, SLMs require less memory and computational power, making them ideal for resource-constrained environments like edge devices and mobile applications. Techniques like pruning, quantization, and knowledge distillation further optimize SLMs without sacrificing performance, offering practical solutions for specialized tasks where resources are limited.

3. Multi-Agent Systems and Test-Time Compute

AI agents are autonomous systems designed to perceive, reason, and act toward achieving specific goals. Unlike chatbots, which follow predefined scripts to respond to user queries, AI agents can independently analyze their environment, make decisions, and take actions. This ability to adapt and learn makes them the backbone of the trillion-dollar "AI worker" economy that many executives, including those from Nvidia, Google, and Microsoft, are calling the future of business.

Why Agents Matter

Top executives highlight that these AI agents will be managed alongside human teams, transforming how businesses operate. Agents are already driving innovations in automation, customer service, and logistics, and they are poised to become indispensable "co-workers" for handling repetitive, high-volume tasks.

Multi-Agent Systems and Test-Time Compute

AI is advancing into multi-agent systems, where specialized agents collaborate to solve complex problems. These systems integrate multimodal capabilities—processing text, images, and audio simultaneously—to enhance adaptability and performance. For example, agents in customer service can combine visual analysis and natural language understanding to deliver richer, more contextual responses.

What us Test-time Compute?

A critical challenge for these systems is test-time compute: the processing power required for AI models to generate outputs in real-time. As models grow larger, their computational demands increase, affecting cost, speed, and energy efficiency. Innovations such as model distillation, sparsity optimization, and hardware acceleration are helping reduce these requirements without compromising performance.

4. Generative AI and Synthetic Data

Generative AI continues to grow in influence, with applications spanning creative content, scientific innovation, and beyond. First introduced by Ian Goodfellow in 2014 with Generative Adversarial Networks (GANs), this technology has evolved to create photorealistic images, deepfake videos, and even entire movie scenes. Recent cases like the Brad Pitt deepfake scam and others highlight both its potential and risks.

Tools like Midjourney, Sora, Runway ML Gen-2, and Kling now enable high-quality image and video generation from text, Suno enables generating music from simple prompts, while ChatGPT and Perplexity excel in natural language applications. This revolution is transforming jobs in content creation—from text and music to film, design, and advertising—by automating traditionally manual processes and enabling more creativity at scale.

5. The Data Peak and Rise of Synthetic Data

AI development is approaching a critical threshold known as "data peak," where high-quality real-world data is becoming scarce. Ilya Sutskever, co-founder of OpenAI (currently co-founder of Safe SuperIntelligence) recently warned that pre-training as we know it will soon become obsolete due to this limitation. As AI models continue to scale, they require new approaches to sustain their progress.

Synthetic data—artificially generated datasets designed to train AI—has emerged as the primary solution to this challenge. By producing realistic yet privacy-compliant data, synthetic data ensures AI can continue evolving without being restricted by real-world data limitations. It is already fueling advancements toward AGI (Artificial General Intelligence) and even ASI (Artificial Superintelligence), allowing for more efficient, scalable, and ethical AI training.

6. AI Infrastructure: Chips, Energy, and Quantum Computing

The backbone of AI’s explosive growth lies in its infrastructure—from specialized chips to energy solutions and quantum computing. As AI becomes central to global economies, the race to build better infrastructure has intensified.

NVIDIA's AI Superchips
NVIDIA’s latest generation of AI chips delivers supercomputing capabilities at more affordable prices, democratizing access to advanced AI technologies. These chips enable faster training and deployment of AI models, making high-performance AI accessible to more industries and researchers worldwide.

Quantum Computing's Leap
Google’s state-of-the-art quantum chip, Willow, recently achieved a milestone by solving mathematical problems in seconds that would take today’s supercomputers septillions of years (i.e. trillion trillions of years). This breakthrough opens new frontiers in fields like cryptography, drug discovery, and materials science, potentially transforming the way AI systems process complex computations.

Energy Sustainability
The growing demand for compute power has made energy a critical factor in AI infrastructure. Innovators like Sam Altman are advocating for significant investments in nuclear energy (and other energy innovations) to meet AI’s massive energy needs. Meanwhile, the U.S. is exploring nuclear microgrids to sustainably power AI data centers, ensuring the industry’s growth doesn’t come at the expense of the planet.

Geopolitical Factors
Export controls on advanced GPUs and other hardware components are reshaping the global AI landscape. These restrictions aim to limit access to cutting-edge technologies but have also spurred innovation in regions like China, where models are achieving competitive performance with less compute. This dynamic highlights how constraints can drive creativity and efficiency.

Conclusion

AI is no longer a tool; it is the foundation of economic and strategic power. Embracing advancements in transformers, generative AI, multi-agent systems, and synthetic data is not optional—they are the new building blocks for leadership and growth in the AI era. Failing to adapt now means risking irrelevance in a world rapidly shaped by AI. It’s like migrating to a new planet where the rules, culture, and tools are entirely different. As an executive, you must master this shift or risk being left behind in the future economy.

• Are you ready to manage AI agents as seamlessly as human employees?

• How will your business leverage proprietary data alongside synthetic data to unlock new competitive advantages and drive innovation?

• Is your organization’s AI strategy aligned to gain a competitive edge, reduce costs, and create new revenue streams?

• And are your products and services prepared to meet the demands of the new AI economy and evolving customer expectations?

The next decade will belong to those who embrace AI’s potential strategically, navigating its risks while capitalizing on its opportunities. The question isn’t whether AI will redefine your business—it’s whether you will lead or follow in the transformation.

About the Author

Sam Obeidat: AI Strategy Expert, Technology Product Lead, Angel Investor, and a Futurist.

Sam Obeidat is an internationally recognized expert in AI strategy, a visionary futurist, and a technology product leader. He has spearheaded the development of cutting-edge AI technologies across various sectors, including education, fintech, investment management, government, defense, and healthcare.

With over 15,000 leaders coached and more than 31 AI strategies developed for governments and elite organizations in Europe, MENA, Canada, and the US, Sam has a profound impact on the global AI landscape. He is passionate about empowering leaders to responsibly implement ethical and safe AI, ensuring that humans remain at the center of these advancements.

Currently, Sam leads World AI X, where he and his team are dedicated to helping leaders across all sectors shape the future of their industries. They provide the tools and knowledge necessary for these leaders to prepare their organizations for the rapidly evolving AI-driven world and maintain a competitive edge.

Through World AI X, Sam runs a 6-week executive program designed to transform professionals into next-gen leaders within their domains. Additionally, he is at the forefront of the World AI Council, building a global community of leaders committed to shaping the future of AI.

Sam strongly believes that leaders and organizations from all sectors must be prepared to drive innovation and competitiveness in the AI future.

Connect with Sam Obeidat on LinkedIn

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