Specialist vs Generalist — The Agentic Disruption: 7 Experts Weigh In

In the race to integrate artificial intelligence into enterprise workflows, a major debate has emerged: should businesses rely on specialist AI agents, built for specific tasks and industries, or generalist AI agents, designed to handle a broad range of functions? As AI-driven automation reshapes workflows, this question is becoming more pressing than ever.

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A striking example of specialist AI agents in action comes from JPMorgan Chase, which has developed proprietary AI-powered financial agents to assist with investment analysis, risk assessment, and fraud detection. These agents are finely tuned to banking regulations and market behaviors, enabling them to make informed decisions with a level of accuracy that a generalized system might struggle to match. 

On the other side of the spectrum, companies like OpenAI are championing generalized AI agents. OpenAI’s upcoming ‘Operator’ project aims to create AI agents that can perform diverse tasks—like filing expense reports, booking travel, or managing emails—by navigating digital interfaces much like a human assistant. While these agents offer broad flexibility, concerns remain about their ability to deliver the same level of depth and reliability as their specialist counterparts, especially in enterprise contexts doing high-stakes tasks involving sensitive actions like in investing, banking, and even healthcare.

Adding to the debate, Petr Baudiš, CTO of Rossum, has argued that specialist AI agents are ultimately more impactful in enterprise environments. In his op-ed, he critiques the growing hype around generalist AI agents, asserting that businesses require agents deeply integrated into their specific workflows rather than a one-size-fits-all solution. 

As enterprises continue experimenting with AI agents, the question remains: Where can AI agents deliver the most value, and which approach—specialist or generalist —will define the future of enterprise AI? To answer this question, we’ve asked 7 AI experts to share their perspectives on the impact and potential of AI agents in the enterprise world. 

Brent Wesler: Agentic AI is about bots that can reason and take action like a human would

VP of Strategic Technology & Digital Automation, VRC Companies

Agentic AI makes it possible for software bots to learn, understand, self heal, and make rational, logical decisions without human intervention. AI Agents have solidified themselves as a viable tool to reduce labor costs by automating repetitive common business processes. With Agentic AI at just the precipice of being practical in business and AGI still not a realization, it’s crucial to implement use case-specific specialist bots, not generic bots. Unlike RPA, Agentic AI is about bots that can reason and take action like a human would, where logic and decisions are critical. Take an insurance claim process, for example. A trigger point retrieves information, like a claim number, the bot then checks multiple, disparate data sources, makes a decision on the data, and can actually underwrite insurance polices that are not structured but rather require many variables in the output.

Ahmed Zaidi: This opens up a whole new set of use cases that can now be automated using agents

Chief Automation Officer | CEO at Accelirate Inc.

I believe that AI agents have tremendous potential in the enterprise. AI agents have the ability to chart out a course of action as well as execute on that course utilizing tools that are made available to it. There are ample use cases within the enterprise where the requirements to make a decision reside within text in documents and guidelines and is not a simple if this then that type of a decision tree. This text needs to be read, understood and then put in context of the data and task that needs to performed, and agents are very good at interpreting the rules hidden within contracts, or standard operating procedures, or regulatory guidelines etc. I believe this opens up a whole new set of use cases that can now be automated using agents.

Olivier Gomez: It’s about strategic orchestration of agents

Co-Founder and CEO, IAC.AI

AI’s evolution isn’t just about building bigger models—it’s about strategic orchestration. While large generalist AI will always have a role, the real transformation lies in smaller, specialized AI models working together. Recent research, including DeepSeek’s findings, highlights why bigger isn’t always better. The next phase of AI development will focus on:

• Efficiency & Precision – Specialized models deliver more accurate, domain-specific results than a single massive AI.
• Lower Energy Consumption – Smaller AI agents reduce computational waste while maintaining high performance.
• Scalability & Adaptability – AI deployment will be faster, more cost-effective, and easier to maintain through modular, orchestrated intelligence.

This isn’t about replacing general AI—it’s about enhancing it with intelligent synergy.

Dan Lucarini: Specificity is key

Senior Analyst, Deep Analysis

AI agents could deliver impressive business value by automating repetitive document and data entry tasks and supporting the next-step decisions. With the ability to read and analyze documents and to automate workflows based on the data, agents will become indispensable for enterprises aiming to stay competitive.

When it comes to agents, specificity is the key. Generalized agents lack the precision needed for complex business workflows. I lean toward specialist agents because they can deliver higher accuracy and trust for specific tasks like fraud detection, medical diagnosis and treatment, or customer support. I expect they will also be far easier to control, regulate and audit – all critical requirements for regulated industries such as government, healthcare and finance. In the future, I think a hybrid approach – where specialist agents are orchestrated by a generalized uber-agent – could combine the best of both worlds.

Nandan Mullakara: Future of enterprise AI lies in specialist agents

Founder, Bot Nirvana

The future of enterprise AI lies definitively in specialist agents, though the path there is gradual. While we’re seeing success with generalized agents like Devin for coding, the real transformation will occur as organizations deploy millions or billions of specialist AI agents working alongside their teams and team members. The evolution is clear: we are starting with general-purpose agents, right now progressing to cloud-based specialized solutions for functions like sales, customer service, etc, and ultimately we will advance to highly customized agents for specific organizational roles, business units, and industry verticals. We would know that Ai agents as a concept has been successful when we have a seamless integrated billions of these specialist agents throughout the business ecosystems.

Sascha Cutura: Use AI where it makes sense and a difference

AI & Automation Expert

I see it all the time since AI is so hyped up. Everyone is chasing the next best LLM, the most powerful AI tool, the vastest capabilities. But that is not what moves the needle for businesses. What works is using AI where it really makes sense and a difference. And there are many businesses out there who are not even ready for what they believe they need. When I work with clients, I don’t start with, “How can we implement the latest AI models and agents?” I ask, “What’s the actual problem we’re solving here?” And that’s why I’m a huge believer in specialised AI Agents focused and purpose-built, that does one thing exceptionally well, but of course let the agents required, work together to create that powerful outcome we need.

Kieran Gilmurray: The real magic is in orchestrating multiple AI agents

Chief AI Officer, Kieran Gilmurray

AI agents are game-changers in enterprise contexts—but only when they work together. The real magic isn’t in a single model making isolated decisions; it’s in orchestrating multiple AI agents to handle complex workflows end to end. Think of it like a well-run football team: individual talent matters, but coordination wins the match. Enterprises that invest in AI orchestration—tying automation, LLMs, and decision-making models into a cohesive system—will see the biggest efficiency and accuracy gains. Those that don’t? They’ll struggle with fragmented solutions and half-baked automation. AI’s future isn’t about lone agents; it’s about seamless, intelligent collaboration.

The debate between specialist and generalist AI agents is far from settled. While some argue that broad, flexible generalist agents will redefine enterprise automation, others maintain that deeply integrated, specialist agents will be the real game-changer. Each approach has its advantages, but the reality is that businesses may not have to choose just one. Instead, the future of AI agents could lie in orchestration—leveraging multiple agents, each with distinct strengths and focus, to work together seamlessly and deliver the most impact.

Each approach comes with trade-offs in adaptability, efficiency, and risk management, and businesses must weigh pragmatism against innovation to find the right balance. What’s certain is that AI agents are evolving at an unprecedented pace—what seems like the prevailing model today may be upended within months, if not days. 

Enterprises navigating this shift must consider not just technological advancements, but also competitive dynamics, compliance risks, and long-term business viability.

Rossum’s new suite of specialist AI agents for document processing automation was revealed in a public event in February. Diving deeper with industry experts Dan Lucarini (Senior Analyst at Deep Analysis), Kirill Sadovnikov (Senior Consultant at KPMG Netherlands), and Rossum CEO Tomas Gogar debating where AI agents will deliver the most impact. Watch it on demand now.