From Raw Agentic AI to Orchestrated Enterprise Systems
AI orchestration in the enterprise is the coordinated design, control, and governance of multiple agentic AI systems so they can work together on real business outcomes, rather than running as disconnected, standalone models that perform impressive tasks in isolation but fail to scale in production environments. That distinction is at the heart of Pega AI Lab Director Peter van der Putten’s argument that orchestration matters more than raw agentic power for customer engagement. Pega’s new Customer Engagement Studio sits on top of its Customer Decision Hub and coordinates specialist agents spanning marketing strategy, creative work, data science, compliance, and performance through a single conversational interface. Van der Putten notes that for clients such as Wells Fargo, Pega already supports six billion next-best-action decisions every month in under 250 milliseconds; the real problem was not decision speed, but orchestrating enough content, offers, and actions to feed that engine.
Why So Many Agentic AI Projects Fail in Enterprises
The current wave of enterprise AI deployment is full of ambitious demos that stall before they reach scale. Gartner predicts that more than 40% of agentic AI projects will be canceled, and van der Putten links this to misplaced expectations about raw model power. Many teams assume they can throw a large model at a problem and watch a workflow sort itself out, without designing guardrails, roles, or escalation paths between agents and humans. This leads to brittle solutions that are hard to govern and hard for business users to trust. As Anaïs Ghelfi from Malt explains in a separate context, technology that no one uses, or that does not help people do their work better, is useless even if it is advanced. Orchestration forces teams to think through who will use the system, what problem it solves, and how it fits into daily work.
Kantata’s Expertise Agent: Orchestrating Work Through a Services-Native Graph
Kantata’s Expertise Agent shows what successful AI orchestration enterprise patterns look like in professional services. Built into the Kantata Expertise Engine, it is framed not as a single clever chatbot but as a coordination layer across project management, resource planning, financial data, and external tools. The services-native knowledge graph is the key: it connects data about projects, people, systems, documents, communications, and meetings, then maps relationships between skills, delivery patterns, outcomes, and workflows. According to Kantata, 87% of professional services organizations plan to use AI agents as part of their workforce, while 89% of leaders say future revenue growth will depend more on how effectively they scale AI than headcount. Expertise Agent addresses that pressure by closing the gap between insight and execution, identifying at-risk projects, proposing mitigations, matching resources to work, and generating project plans that can be turned into self-executing workflows.
Agentic Enterprises: Coordination Infrastructure, Not Isolated Bots
The idea of an agentic enterprise, as described by Malt’s Anaïs Ghelfi, is less about deploying many independent agents and more about building the shared infrastructure that lets them coordinate work with humans. In such organizations, knowledge, processes, and playbooks are codified so they are accessible to every employee and every agent. Everyone becomes a builder, assembling workflows that combine human judgment with AI agent coordination instead of working in silos. This demands orchestration across backend platforms, frontends, developer tools, and data platforms, because AI development extends beyond engineers to business users who design automations. In practice, that means tight alignment between technical and business teams, iterative delivery against real use cases, and agents that can hand off tasks, escalate decisions, and reuse institutional knowledge. Work changes from manual execution of steps to supervising and improving coordinated AI systems.
The Enterprise Payoff: Better Planning, Risk Sensing, and Automation
When AI orchestration enterprise patterns are in place, the business gains go beyond fancy interfaces. In customer engagement, orchestrated agents can move marketers from brief to live, personalized campaigns in minutes while staying within compliance and performance constraints. In professional services, Kantata’s Expertise Agent uses its knowledge graph to improve resource planning, highlight at-risk projects before margins erode, and automate routine project setup and reporting. Across both domains, the common thread is AI agent coordination around shared context and outcomes, not standalone tools fighting for attention. This makes workflows more consistent, speeds up decision cycles, and preserves institutional knowledge during handoffs. As more organizations pursue agentic AI systems, those that invest in orchestration layers, governance, and accessible knowledge will see agents amplify human work, while those chasing raw model power alone are likely to join the canceled-project statistics.
