Orchestrating for outcomes: The new model for AI-driven enterprise transformation

Enterprises are investing heavily in AI agents, but many find that return on investment (ROI) stalls after initial pilots. The reason is simple: agents aren’t magic fixes. Without rethinking workflows, building the right orchestration and setting strong governance, deployments will plateau.

Agentic AI, which is AI that can learn, adapt and act autonomously, can transform business operations. But to move from promise to performance, enterprises need to understand why ROI stalls and how to correct course. 

For enterprises, the transformation journey starts with identifying end-to-end workflows where the biggest transformation value lies. By consciously simplifying and redesigning workflows with an AI-first mindset, enterprises set themselves up for a successful transformation that unlocks value.

We often see enterprises stall when they apply agentic AI automation to their existing workflows. Many organizations layer gen AI into brittle structures without addressing the underlying complexity. 

For example, a lengthy and complicated sales workflow might impact speed to market. While AI might potentially help generate faster quotations, coupling it with a conscious simplification of existing complex approvals processes can be more impactful.  

And at a foundational level, enterprises have long used Robotic Process Automation (RPA) to map and automate fixed workflows. With the advent of large language models (LLMs), enterprises began surgically embedding gen AI into these processes, enhancing automation within rigid boundaries. These systems remain fragile, requiring significant rework as business needs shift.

Watch the video: ‘How to onboard an AI agent the right way’

Real transformation begins when enterprises consciously reimagine workflows with key business outcomes in mind and apply AI agents that decompose and solve the workflows step by step. And the greatest change happens when swarms of such AI agents collaborate dynamically to solve complex workflows. At this stage, enterprises move beyond isolated tasks and begin orchestrating outcomes across the business.

Enterprises that want to capitalize on agentic AI must prepare their foundations. That includes establishing robust data pipelines and governance structures that prevent agents from operating in silos. 

Part of the governance includes defining where human judgment is essential. AI agents should operate within clearly defined boundaries, with humans providing oversight in areas requiring contextual understanding, ethical reasoning or regulatory compliance. While vertical AI agents offered by specialized vendors have value, they remain constrained by the data they can access. 

True enterprise-wide transformation comes from horizontal solutions that cut across silos and incorporate vertical agents as needed. In a sales workflow, a vertical AI agent integrated to a customer data platform can help with prioritizing leads and providing insights. 

In contrast, a horizontal solution (orchestrating across several agents that operate across customer data, marketing analytics, product data and email) identifies high potential leads more precisely. It also helps sellers reach out to those leads with the right products and personalized messaging, which results in more converted leads.

Assets and governance must be centralized to accelerate innovation. A central orchestration system enables agents to integrate diverse datasets and address broader workflow steps while helping to ensure standardization, auditability and control. Also, enterprises must invest in upskilling employees. This process prepares them to adapt to new modes of working within the necessary technology, data and governance infrastructure.

Watch the video: “AI Agent solutions: Vertical or Horizontal?”

The future of AI in business isn’t just about automating tasks; it’s about shifting toward outcome ownership. AI is a catalyst that augments human intelligence to achieve wanted business results. This approach requires deliberate thought about where human oversight should remain—particularly in high-risk decisions and processes—while allowing agents to take on more executional responsibility.

Agentic AI offers a compelling vision for the future of enterprise workflows. Enterprises that embrace this technology and approach implementation strategically stand to unlock striking value. The journey might seem daunting, but the shift toward autonomy, efficiency and innovation ensures that the payoff far outweighs the challenge.