The Pragmatic Rise of Agentic AI: From Hype to ROI in 2026
After a flurry of demonstrations and prototypes, enterprise technology leaders are shifting their focus in 2026. The emphasis is now on practical implementation – governance, orchestration, and integration – as organizations seek measurable returns on their AI investments.
From Prototypes to Production: The ROI Imperative
The initial excitement surrounding AI agents is giving way to a demand for tangible results. Enterprises are moving beyond experimentation and prioritizing the deployment of agentic systems that demonstrably improve productivity and deliver business value. Which means focusing on workflows that can be integrated into existing systems, rather than standalone AI tools.
The Platform as the Foundation for AI Governance
Scaling AI safely and effectively requires robust governance and orchestration. Leading organizations are increasingly relying on enterprise platforms to manage the lifecycle of AI agents, ensuring they operate within defined guardrails and adhere to compliance standards. Here’s crucial to avoid the pitfalls of “shadow AI” – ungoverned, potentially risky AI applications developed outside of IT oversight.
Luis Blando, CPTO of OutSystems, emphasizes the demand to “give users guardrails,” acknowledging that AI adoption will happen regardless. Successful companies are leveraging AI to govern AI, creating a framework for enterprise-grade scalability.
The Evolving Role of the Developer and Architect
The rise of AI-powered code generation is reshaping the software development landscape. Whereas accelerating delivery, it similarly demands a new skillset. The most valuable technical profiles in this era are the generalist developer and the enterprise architect – professionals who possess systems thinking, understand complex business problems, and can effectively integrate AI into existing infrastructure.
Eric Kavanagh, CEO of The Bloor Group, notes that a deep understanding of enterprise and business architecture is paramount for success in this new environment.
Thermo Fisher Scientific: A Real-World Example of Agentic Systems
Thermo Fisher Scientific provides a compelling example of how agentic systems are being deployed in the real world. They’ve moved beyond single-task AI assistants in customer service to a coordinated team of specialized agents. When a support case arrives, a triage assistant classifies the request and routes it to the appropriate specialist – an intent and priority agent, a product context agent, a troubleshooting agent, or a compliance agent.
Rajkiran Vajreshwari, senior manager of app development at Thermo Fisher Scientific, highlights the benefits of using a unified platform like OutSystems Agent Workbench: “We don’t have to reckon about what will work and how. It’s all pre-built. Each agent has a narrow role and clear guardrails. They stay accurate and auditable.”
Orchestration: The Key to Unlocking AI Value
While selecting the right large language model (LLM) initially garnered significant attention, the more enduring challenge is orchestration. This encompasses routing tasks, coordinating workflows, governing execution, and seamlessly integrating AI into existing enterprise systems. Organizations need the ability to easily swap between different LLMs without disrupting the underlying agentic system.
Scott Finkle, VP of development at McConkey Auction Group, explains that LLMs are components of larger workflows, not complete solutions. A robust platform with orchestration capabilities ensures processes remain reliable, even as the AI reasoning layer evolves.
The Economics of Enterprise AI: Incremental Wins and Long-Term Savings
Investment in security, compliance, governance, and platform-level AI capabilities will increase in 2026, particularly as AI becomes integrated into core workflows. Enterprises are advised to prioritize incremental wins over ambitious, immediate gains. Focusing on delivering tangible value through production deployments is crucial for realizing long-term savings.
Finkle advocates for a “base hits” approach, emphasizing the importance of getting solutions into production and demonstrating their impact.
FAQ
Q: What is “shadow AI”?
A: Shadow AI refers to AI applications developed and deployed without proper IT oversight, potentially leading to risks like data leakage, policy violations, and inaccurate results.
Q: Why is orchestration important for AI?
A: Orchestration ensures that AI agents work together effectively, follow defined processes, and integrate seamlessly with existing enterprise systems.
Q: What skills are becoming more valuable in the age of AI?
A: Systems thinking, enterprise architecture knowledge, and the ability to integrate AI into existing infrastructure are increasingly valuable skills.
Q: What is Agent Workbench?
A: Agent Workbench is a capability within the OutSystems platform that allows enterprises to design, deploy, and govern AI agents at scale.
Did you realize? Enterprises are increasingly prioritizing AI governance to mitigate risks associated with ungoverned AI development.
Pro Tip: Focus on integrating AI into existing workflows rather than attempting to overhaul entire processes.
Want to learn more about the future of agentic AI? Explore the full webinar discussion.
