Hanan Ouazan’s Thoughts on GenAI topics

The journey from simple AI assistants to powerful, cross-system agents reflects a profound shift in how businesses leverage generative AI. In 2023, the rise of embedded GenAI promised seamless integration into daily workflows, enabling users to adopt advanced capabilities without disrupting habits. Yet, high costs per user and limited customization left many questioning the value proposition of these black-box solutions.

By 2024, the narrative had shifted toward professionalization and industrialization. Tools like Microsoft Copilot and Salesforce’s Agentforce began to blur the lines between passive assistance and active execution, hinting at a future where agents could do more than just assist—they could act autonomously. However, these systems were still bound by the ecosystems in which they operated.

2025 may mark a pivotal year: the emergence of industrialized agents capable of automating processes across multiple systems. This evolution presents a critical challenge: most workflows span several platforms, and embedding agents in isolated tools inherently limits their potential. Companies face two key options. They could open their platforms to allow broader control of external tools, enabling their agents to orchestrate processes across systems. Alternatively, they might allow their agents to operate outside their own ecosystem, enabling clients to call on these agents externally—a bold shift that would require new business models but could expand the agents’ utility dramatically.

This trajectory also forces a reconsideration of SaaS business models. As automation takes center stage, interfaces lose their primacy, and the true value shifts to the intelligence and data that underpins these agents—data that ultimately belongs to the client. How SaaS platforms navigate this tension between control and openness will define the next era of innovation.

Generative AI is profoundly reshaping the way we work, introducing tools that automate a wide range of tasks—from coding and data analysis to content creation. These tools don’t replace human expertise; they redefine it. Experienced professionals—whether engineers, analysts, or creatives—are becoming indispensable, not for execution, but for supervising, refining, and integrating the outputs produced by these technologies.

A concrete example: industrial automation. Manufacturing goods in factories has become faster and more cost-effective thanks to technology. This didn’t eliminate workers but transformed their roles—shifting many to quality control, performance monitoring, and process management. At the same time, demand for engineers capable of redesigning production systems and envisioning new solutions has skyrocketed.

The same shift is happening across sectors. Automation doesn’t eliminate existing jobs; it evolves them, moving value creation toward two critical areas:

• Supervision and optimization: expertise to monitor and continuously improve automated systems.
• Rethinking processes: talent capable of integrating these technologies into a broader vision that fundamentally transforms how value is created.

However, simply layering automation onto existing processes isn’t enough. It risks overloading systems without making them meaningfully more efficient. The real challenge lies in rebuilding these processes from the ground up, leveraging these tools to redefine workflows and unlock entirely new productivity drivers. Without this foundational shift, the gains will remain marginal, and true transformation will remain out of reach.

Automation doesn’t eliminate jobs—it shifts where value is created. Tools alone won’t transform organizations; it’s how they’re embedded into a reconstruction of the fundamentals that matters. Those who successfully evolve their processes and talent around these technologies won’t just gain a competitive edge—they’ll redefine the rules of the game.

As AI agents increasingly take over tasks like email drafting or resolving customer service issues, we face a pivotal challenge: how do these agents communicate effectively, not just with humans but with the software ecosystems they rely on?

Today, AI agents operate in a world fundamentally designed for human interaction—and, in many cases, designed to exclude automation. Security measures like CAPTCHAs and anti-bot protocols were created to block malicious robots, such as scrapers or spam generators. While well-intentioned, these safeguards also reflect a deeper reality: websites and applications are optimized for human experience, not AI efficiency.

This creates significant inefficiencies:

• For example, if your AI agent needs to interact with multiple software systems—say, for resolving an issue with a car company—it must navigate APIs, authentication layers, and human-centered designs, none of which are natively optimized for agent use.
• The result is a patchwork of integrations and redundant steps, requiring the agent to “mimic” human workflows rather than operate in a streamlined, machine-native way.

Initiatives like Anthropic’s Model Context Protocol (MCP) aim to address this by creating a universal standard for agent-to-software interactions. Instead of relying on custom APIs and connectors for each tool, MCP provides a simplified framework:

1. Unified communication: Agents can interact with diverse software platforms through a standardized protocol, eliminating the need for bespoke integrations.
2. Scalability and maintenance: By abstracting away the complexity of individual APIs, MCP allows systems to scale more efficiently.
3. Focus on tasks, not translation: Agents can execute actions directly, bypassing human-centric layers like forms or natural language interfaces.

But the challenge goes deeper. To truly unlock AI efficiency, we need a paradigm shift in how digital systems are designed. Historically, websites, applications, and workflows were created to maximize human experience—a logical choice in a world where humans were the primary users. However:

• These systems are fundamentally incompatible with the needs of autonomous AI agents, forcing them to work around barriers like CAPTCHA or fragmented APIs.
• The result is a design philosophy that unintentionally penalizes efficiency and automation, even as AI becomes central to modern workflows.

Looking forward, the emergence of vector-based communication protocols could revolutionize how agents interact with software. Instead of relying on human-readable formats or embedding layers, agents could communicate directly in a machine-native “language,” enabling:

• Automation-first ecosystems: Systems designed to optimize AI workflows rather than human interaction.
• Seamless integration: Agents operating natively within digital ecosystems, reducing friction and improving scalability.

This transformation also raises strategic questions for businesses. How can organizations balance the need for AI-driven efficiency with the importance of security, transparency, and control? Transitioning from human-centric systems to machine-native ecosystems isn’t just a technical challenge—it’s a rethinking of how value is created, delivered, and protected.

The future lies in a hybrid approach: systems that embrace automation to streamline workflows while maintaining safeguards for trust and human oversight. Those who adapt to this shift won’t just keep pace with change—they’ll set the standard for the next generation of digital innovation.

The rise of AI agents in enterprises unfolds at two levels: enhancing individual productivity through Task Agents and redefining collective workflows via Workflow Agents. While these innovations promise efficiency gains, they also introduce structural challenges. Without a well-orchestrated strategy, organizations risk an uncontrolled proliferation of agents and critical operational dependencies.

• Level 1: Task Agents – A New Invisible Workforce Layer

Task Agents (or Interface Agents) integrate directly into workstations, accelerating execution by 5 to 30% depending on the role and task. This creates an implicit “n-1” hierarchy at every workstation, effectively forming an invisible yet impactful workforce augmentation.

However, without governance, enterprises will face a chaotic explosion of agents with no oversight on quality, resource consumption, or redundancy. This results in a shadow management phenomenon—akin to allowing employees to hire assistants freely without HR oversight simply because the cost is negligible and deployment is instantaneous.

• Level 2: Workflow Agents – Rewiring Business Processes

Workflow Agents go beyond individual task optimization, re-engineering entire processes that span multiple teams and functions. These multi-agent systems are designed to enhance process speed, reduce costs, and improve reliability.

Yet, this introduces a new risk: delegating critical processes to autonomous agents may lead to operational vulnerabilities. If these agents fail or become inoperable due to technological shifts, regulatory changes, or internal misalignment, the entire business process may break down.

To prevent chaos and ensure sustainable adoption, enterprises must focus on two core pillars: a centralized agent management platform and a robust governance framework.

• A Centralized AI Agent Management Platform

Enterprises must establish a dedicated AI agent platform—the “HR system” for this new workforce. This platform must ensure:

• Data and API access centralization: A structured, secure environment for agents to operate efficiently.
• Monitoring and performance oversight: Tools to track agent reliability, detect failures, and manage resource consumption.
• Asset reusability: A framework that prevents redundant agent creation by leveraging existing models before developing new ones.
• A Governance Model Fit for the AI Era

Jensen Huang (CEO of NVIDIA) envisions IT becoming the “IT department for AI agents,” but governance must extend beyond IT. Just as HR is a shared responsibility across managers, agent governance must be distributed.

Key questions need resolution:

• Who oversees agent performance and lifecycle management?
• How can organizations ensure scalability without bloating IT teams?
• What decision frameworks govern agent deployment and evolution?

Striking a balance between agility and control is critical. Over-regulation stifles innovation, while unchecked proliferation leads to inefficiencies and security risks.

Integrating AI agents isn’t a mere technological upgrade—it’s an organizational shift. Without structure, enterprises face digital anarchy with disconnected agents operating in silos. By implementing a centralized management platform and a scalable governance model, businesses can harness AI’s full potential while maintaining control. The future of enterprise efficiency isn’t just about AI—it’s about mastering the orchestration of an intelligent, agent-driven ecosystem

The evolution of AI is heading towards a seamless experience where models no longer require manual selection or mode switching. Instead, AI systems are learning to dynamically adjust their reasoning depth based on the complexity of the task. This shift marks a move away from fragmented AI offerings towards a single, adaptive intelligence that optimizes responses in real-time.

Claude 3.7 Sonnet is the first implementation of a unified model capable of handling both basic and reasoning-based tasks. While the transition between modes is currently manual, it embodies the ambition of simplifying user experience by reducing the need for multiple models.

OpenAI is following the same logic with GPT-5, as illustrated by Sam Altman’s statements. In ChatGPT, the model already decides on its own when to search the web, though users can manually enforce it. This similarly reflects the broader push toward automation and simplification.

In the future, this unification could extend beyond reasoning modes to model sizes. Simple tasks could be handled by smaller models, complex reasoning by advanced models, and search-dependent queries by dedicated retrieval mechanisms. This evolution would enable AI to dynamically allocate resources, optimizing both efficiency and intelligence without requiring users to make technical decisions.

As AI continues to evolve, the focus is shifting away from model selection and towards process engineering through agent and multi-agent logic. The model itself is becoming a commodity—easily accessible with minimal configuration. The key differentiator will no longer be which model is used, but rather how intelligently workflows are designed to leverage AI capabilities. This transformation will redefine AI interaction, making systems more autonomous, adaptive, and seamlessly integrated into real-world applications. The future of AI will be defined not by choosing between models, but by constructing dynamic, intelligent processes that harness their full potential.

Learn more on the latest evolution of AI by subscribing to our Gen AI newsletter.