AI is everywhere on the enterprise agenda – but not always as it should be. While 78% of organizations now use AI in at least one business function, according to McKinsey, many struggle to scale those pilots into measurable business outcomes.
AI projects often fall short because adopting AI isn’t only about deploying models or agents. It’s even more important to design the right system for the job – and know when to use automation, when to embed AI, and when to let agents take the lead.
Too often, these concepts get confusing. Is it just simple automation or is AI involved? What separates an AI-enhanced process from an autonomous AI agent? And what connects all of these approaches into something reliable enough to scale? The answers lie in understanding both the differences between these models, and the orchestration strategy required to make them work together.
Understanding the spectrum of machine work
There’s no one-size-fits-all model for automation in the age of AI. Instead, there’s a spectrum of machine work – from deterministic rule-based automation to fully autonomous AI agents. Most enterprises will need a mix to support their business processes, systems, and risk tolerances.
Deterministic automation: Precision at scale
This is the most traditional approach to process automation and orchestration. Processes are modeled in advance with predefined logic, conditions, and decision models.
Deterministic orchestration offers a high degree of predictability and control. It’s auditable by design and works exceptionally well in environments where rules are fixed, compliance is required, and outcomes must be repeatable.
This approach shines in high-volume, low-variance processes. For example, consider customer onboarding in a regulated industry: the steps are consistent, deviations are risky, and oversight is essential. Deterministic orchestration ensures every task is completed in the right order, by the right system or person, and in line with established rules.
AI workflows: Intelligence within guardrails
In the middle of the spectrum lie AI-enhanced workflows. These are predefined processes that include specific touchpoints where AI tools make bounded decisions. The process remains orchestrated and predictable, but includes AI-powered moments – like scanning documents, flagging anomalies, or classifying content.
A bank’s Know Your Customer (KYC) process is a prime example. The overall workflow is governed by strict regulatory logic, but you can embed AI to assess document authenticity or risk. These models operate within constraints – and human review remains a key part of the loop. This approach scales AI safely without losing oversight.
AI agents: Autonomy within constraints
At the far end of the spectrum are AI agents. These systems don’t just complete tasks – they pursue goals. Agents can decide what needs to be done and in what order, adapting based on context, results, or shifting business priorities.
Rather than relying on a predefined sequence of steps, this approach allows AI systems – often powered by large language models (or LLMs) – to interpret context and determine which task should happen next, in real time. This approach supports runtime flexibility, enabling processes to evolve based on data, user behavior, or unexpected edge cases.
This autonomy unlocks powerful use cases. Think of a lost luggage scenario in travel. An AI agent might initiate recovery steps, contact vendors, and update the customer. But if confidence falls or timelines are exceeded, escalation to a human is triggered.
But with autonomy comes risk. AI agents consume more compute power, introduce variability, and are harder to audit. Many agentic AI initiatives are already faltering – Gartner predicts that more than 40% of these projects will be abandoned by 2027 due to lack of integration, oversight, or scalability.
Orchestration: The missing strategy layer
To avoid AI project failures and get the best outcomes, it’s important to choose the best mix of AI and automation to meet your business goals.
From there, you need to coordinate end-to-end business processes through process orchestration, regardless of the mix you choose. Whether you’re automating a deterministic workflow or embedding AI agents into dynamic, evolving processes, orchestration provides the strategy layer that ensures it all runs smoothly.
Process orchestration coordinates work across people, systems (including AI tools and agents), and devices. It manages state, ensures governance, and connects tasks across process endpoints.
By using process design specifications like BPMN (Business Process Model and Notation) and DMN (Decision Model and Notation), orchestration brings order and transparency to even the most complex or adaptive process automation.
Many organizations are choosing agentic orchestration – a hybrid model that blends the structural reliability of deterministic workflows with the adaptive decision-making of AI agents.
In this model, agents operate within a structured process but retain enough autonomy to make decisions, manage subprocesses, or adapt based on real-time context.
Agentic orchestration is ideal when organizations want to increase autonomy without sacrificing control, compliance, or auditability.
Think of it as a middle path: letting AI operate within guardrails, or keeping it tethered to business rules and outcomes.
Designing for the machine-human future
As AI becomes a true colleague in the enterprise, the question changes from: “Where can we use AI,” to, “Where should we?” What level of flexibility, oversight, and complexity does the process require? What kind of machine work is appropriate – and how will you orchestrate it?
The modern enterprise will blend deterministic execution, AI assistance, and agentic autonomy. But that blend won’t scale on its own. Process orchestration is what keeps it auditable, governable, and aligned to real business outcomes.
AI might change how work gets done – but orchestration determines whether it works the way you want it to.
By Daniel Meyer, CTO of Camunda
