Feel like you’re falling behind in the AI implementation rush? Two pieces of good news: First, you probably aren’t. Based on recent survey data, only about 15% of organizations are seeing genuine productivity from AI agents in production. Second, if you are a bit behind … it probably doesn’t matter that much.
The next phase of AI and automation investment won’t be defined by how many agents an organization deploys, or how quickly, but by how well those agents are orchestrated alongside people, workflows, and systems to drive measurable outcomes.
Orchestration of automation and AI is widely acknowledged as the next evolution of enterprise automation. Forrester calls the concept Adaptive Process Orchestration (APO). Gartner categorizes it as Business Orchestration and Automation Technologies (BOAT). At Nintex, we call it Agentic Business Orchestration.
The underlying insight from all three concepts is that, thanks to rapidly advancing AI capabilities, the coordination challenges of the near future look fundamentally different from what traditional automation platforms were built to handle and require fundamentally different approaches to undertake.
Where automation once handled tasks within a process, agentic business orchestration transforms the execution of that process entirely. It unites process, systems, and AI agents into a single layer so work can flow directly into efficient outcomes for organizations, not into silos or more tabs.
It’s one thing to grasp the concept of this evolution, but an entirely different thing to understand what to do about it. To help, we’ve identified six principles that can help you determine where and how to start:
1. Start with the process, not the technology
Every expert we talk to says some version of the same thing: You cannot automate what you do not understand, and you must not automate a bad process. Before selecting tools, before deploying agents, and before writing a single workflow, invest in understanding how work actually gets done in your organization, and make sure the process you choose to automate is optimized. Document it. Make it visible. Map the connections.
2. Select use cases with discipline
Le Clair says the factor most correlated with success in AI agent deployments is selecting the right use case. Run ideation with department leaders and relevant employees. Prioritize problems where the value of solving them is clear, where the process is well enough understood to automate responsibly, and where success will be visible and measurable.
3. Start small and scale to build confidence
True agentic systems — self-optimizing, conflict-resolving, runtime-creating — do not exist in production yet. The practical opportunity right now is in worker agents and solver agents: automating specific steps, augmenting specific decisions, connecting specific systems. Get the basics right and you’ll build the organizational confidence that lets your adoption mature alongside agentic capability.
4. Build governance in from the start
The control plane isn’t something you can add later. Auditability, data governance, permissions and guardrails need to be architectural decisions made at the beginning, not retrofitted after the first production failure. Organizations that govern well can move fast with confidence.
5. Build an internal adoption capability
Technology that nobody uses delivers no value. Build an internal advocacy function, whether a formal center of excellence or a small team of dedicated champions, that can demonstrate value, build success stories, and create the momentum for organic adoption. Top-down mandates help, but bottom-up culture helps more.
6. Choose a platform that scales with you
The organizations doing AI right aren’t adopting it just to keep up with the crowd. They are using it to solve specific problems, in governed ways, on platforms designed to support the entire journey — from process documentation through workflow automation to agentic orchestration. The platform matters. Choose one that won’t make you restart as your capacity and ambitions grow.
Want to dive deeper into how AI-driven orchestration unlocks what automation alone can’t? Breaking through the automation ceiling explores the expanded complexity that accompanies expanded capabilities and possibilities, drawing on insights from conversations with Forrester VP & Principal Analyst Craig Le Clair and Nintex customers GM Financial and Hawaii Employers’ Mutual Insurance Company.