Edge computing is revolutionizing the digital landscape by enhancing speed, scalability, and intelligence. Traditionally, cloud computing centralized vast amounts of data and processing capabilities, but growing numbers of connected devices and the need for immediate responsiveness expose the limitations of remote servers.

The shift toward edge computing signifies a rebalancing rather than a rejection of the cloud, as processing tasks increasingly occur closer to data creation points. For instance, technologies like autonomous vehicles, wearable devices, and industrial robotics rely on local processing to minimize latency. In autonomous vehicles, such as those developed by Waymo, real-time data from sensors informs instantaneous braking decisions, crucial for safety. Here, edge computing processes data locally, ensuring rapid reactions while the cloud assists with fleet updates and data analysis.

Moreover, edge computing benefits media streaming services like Netflix. By implementing its Open Connect program, Netflix distributes content via local servers (Open Connect Appliances) positioned within Internet Service Providers (ISPs), drastically reducing buffer times and enhancing user experience. This model allows immediate access to content while the cloud manages user accounts and recommendations.

Similarly, Apple Watch exemplifies personal edge computing. Many critical functions, like detecting falls or processing health data, are executed locally on the device to guarantee speed and privacy, with sensitive information processed without network intervention. This hybrid model—leveraging local edge capabilities with cloud support—ensures data security and operational efficiency.

As the edge computing market is expected to grow to $424 billion by 2030, the interplay between edge and cloud technologies will shape the future of computing, providing essential applications and benefits across various industries.

Salesforce’s AI Fluency Playbook outlines how businesses can effectively integrate AI, revealing that 85% of their employees confidently utilize AI systems. This confidence stems from a holistic cultural shift rather than just implementing technology. Salesforce's transformation into an "Agentic Enterprise" has resulted in a remarkable 64% increase in productivity among workers who use AI daily.

The key takeaway is that AI should not merely function as a tool, but as a teammate. As Chief People Officer Natalie Scardino emphasizes, success lies in how organizations incorporate AI into workflows. The strategy encompasses three pillars: Engagement, Activation, and Expertise. Engagement focuses on overcoming fears surrounding job loss and encourages teams to experiment with AI. Activation promotes habitual use of AI in daily tasks, while Expertise develops the necessary human skills to leverage AI's potential fully.

A notable example of AI's effectiveness can be seen in Salesforce's customer support. The introduction of the Service Agent to handle over 2 million requests didn’t just reduce costs; it transformed roles within the company, allowing support engineers to transition to more advanced positions. As a result, 50% of recent hires were internal movements, showcasing the potential for talent growth and retention.

However, companies must address existing process inefficiencies before implementing AI. If workflows are flawed, AI will only magnify these issues. As Pierre Mattouche suggests, organizations should standardize processes to ensure a smooth integration of AI. Ultimately, the future workplace, projected for 2030, will be both faster and more human, emphasizing the importance of a workforce that can effectively work alongside AI to enhance customer experiences.

In 2025, artificial intelligence emerged as a transformative force, shifting from hype to tangible results across various sectors. Notable advancements included Mexico's launch of the largest public AI school in Latin America, emphasizing the global push for AI education and literacy. AI's ability to bridge the gap between vision and actualization is enhancing productivity and redefining enterprise technology.

As we look ahead to 2026, several key AI trends stand out:

Agents: The year will see a proliferation of AI agents operating at scale within enterprises. Organizations will manage numerous agents developed by different teams, necessitating enhanced focus on monitoring and optimizing these workflows. Effective policy enforcement will become crucial for governing the operations of autonomous agents.

Infrastructure: The end of one-size-fits-all AI infrastructure is on the horizon. Companies will require specialized infrastructure designed for specific AI applications, where considerations like latency and cost will be paramount. This shift will lead to the creation of smarter hardware and software tailored for distinct use cases, setting a foundation for AI-driven businesses.

Security: The rise of autonomous agents poses new security challenges, as these entities can handle sensitive data without human oversight. Tracking data interactions and agent activities will become critical for organizations to safeguard sensitive information.

ROI: By 2026, businesses will increasingly leverage AI as a competitive advantage. Success will hinge on integrating AI more deeply into corporate strategy, fostering a culture of innovation that combines technological capabilities with human insight.

Ecosystem: Firms will standardize strategies for AI orchestration, fostering ecosystems where agents collaborate across platforms, paving the way for scalable solutions.

As AI continues its rapid evolution, collaboration between people and technology will be essential for driving progress and innovation. The future of AI is bright, and everyone has a role in shaping its trajectory.

AWS' Steve Blackwell's 7 Strategic Takeaways for the Industrial Cloud

As we reflect on 2025, AWS re:Invent marked a pivotal transition from theoretical discussions about Generative AI to tangible applications in the industrial sector. Steve Blackwell, Leader of the Worldwide Manufacturing Center of Excellence at AWS, joined me to discuss the key strategic announcements made at the event, which have the potential to reshape manufacturing.

1. Graviton5 Processors for Sustainability: A highlight was the energy-efficient Graviton5 processor, offering up to 60% improved performance. This advancement not only drives down costs but supports manufacturers in achieving critical sustainability goals.

2. Amazon Nova 2 Foundation Models: Steve introduced the Nova 2 family, emphasizing model distillation that allows companies to create smaller, more efficient models tailored to specific industrial tasks, saving both time and resources.

3. "Frontier Agents": Kiro & Friends: The emergence of specialized "Frontier Agents" like Kiro provides operational support, enabling teams to troubleshoot problems and streamline workflows, thereby compensating for the skills gap facing the industrial sector.

4. AWS Transform for Legacy Software: This tool autonomously modernizes aging software systems, eliminating technical debt that often hampers innovation in factories.

5. Trainium3 UltraServers for Research: For enterprise research, Trainium3 UltraServers deliver supercomputing capabilities, allowing industrial firms to conduct massive simulations and deep scientific investigations efficiently.

6. Amazon Bedrock AgentCore: AgentCore enhances agent reliability in manufacturing environments by providing necessary capabilities like persistent memory, enabling agents to learn and adapt over time.

7. Strands Agents for Edge Intelligence: The Strands initiative facilitates running agents on edge devices, enabling rapid local decision-making essential for modern manufacturing processes.

Bonus: Physical AI Fellowship: Steve also stressed the importance of investing in talent through the Physical AI Fellowship, ensuring that the next generation of engineers can effectively apply AI technologies in the physical world.

These advancements signal a new era for the industrial cloud, grounded in practicality and innovation.