What Is Adaptive Process Orchestration? Forrester’s New Category and What It Means for Insurers

Forrester has defined a new analyst category called adaptive process orchestration (APO), and its arrival is timely. Enterprises are discovering that deploying AI agents is only the first step. The harder challenge is running them safely, transparently, and at scale within complex operations.

This challenge is especially significant for AI in regulated industries such as insurance, banking, and healthcare. In these sectors, automation must do more than improve efficiency. It must support accountability, auditability, security, and human oversight.

Neutrinos is named in Forrester’s The Adaptive Process Orchestration Software Landscape, Q2 2026, which examines 35 vendors across a nascent but rapidly developing market. The report describes how organizations are moving beyond isolated task automation toward coordinated, enterprise-wide execution. This is what the category means, why it matters for insurers, and how governed AI is changing the future of insurance operations.

What does adaptive process orchestration mean?

Adaptive process orchestration is Forrester’s term for automation platforms that combine AI agents and nondeterministic control flows with traditional deterministic control flows. These platforms are designed to handle complex tasks, make autonomous decisions, and coordinate people, systems, data, and processes across the enterprise.

Forrester defines APO software as: 
“An automation platform that uses AI agents and nondeterministic control flows in addition to traditional deterministic control flows to meet business goals, perform complex tasks, and make autonomous decisions.”  
- Forrester, The Adaptive Process Orchestration Software Landscape, Q2 2026 

The distinction from traditional automation is important. Conventional automation typically follows predefined rules and fixed sequences. That approach remains valuable for predictable activities, but it can become fragile when a process encounters missing information, exceptions, or changing business conditions.

APO combines different forms of control:

• Deterministic AI and rules-based execution provide consistency for decisions that must follow defined policies.
• Nondeterministic AI-driven flows allow the process to adapt to context and changing conditions.
• Human-in-the-loop AI enables the system to route decisions to an employee when confidence is low, the case is unusual, or the risk level requires human judgment.

For insurers, this could mean using enterprise AI agents to gather information, validate documents, identify discrepancies, and recommend next steps, while maintaining structured controls around decisions that affect customers, coverage, and regulatory obligations.

The result is not automation that simply replaces people. It is an operating model that coordinates technology and people around complex business outcomes.

Why does APO matter for regulated industries?

Regulated organizations cannot treat AI as a black box. Every recommendation or action must be explainable, traceable, and subject to appropriate controls. That is why governed AI is central to the value of APO, particularly across AI in regulated industries.

Forrester identifies several dynamics shaping the category:

• Governance: Organizations need policies, access controls, and safeguards that determine what AI agents can and cannot do.
• Auditability: Business and regulatory stakeholders need visibility into the data, rules, context, and actions behind an AI-supported decision.
• Hybrid execution models: AI should handle appropriate tasks while deterministic workflows and established controls continue to manage critical process stages.
• Human-in-the-loop decisioning: Employees must be able to review, override, or approve decisions when the circumstances require it.

Consider an insurance claims process. An AI agent could classify an incoming claim, extract information from supporting documents, check policy details, and identify potential inconsistencies. A deterministic rules engine could verify coverage conditions and mandatory approval thresholds. If the claim involves conflicting evidence, a high-value settlement, or a potential fraud indicator, the process could automatically route the case to a claims professional.

In this model, automation does not eliminate accountability. It makes accountability more structured. The organization can define where autonomous action is appropriate, where fixed controls are required, and where human judgment must remain part of the process. 

An AI orchestration platform is therefore more than a collection of chatbots or disconnected assistants. It provides the backbone for coordinating agents, workflows, integrations, data, and employees across an end-to-end process. This is essential for insurers managing complex journeys that span policy administration, claims, underwriting, payments, customer service, and compliance.

The same principle applies to underwriting. AI agents may gather external and internal data, summarize risk information, and identify missing details. However, underwriting decisions still need to operate within approved guidelines, with clear escalation paths and records of how the recommendation was produced. 

What does Neutrinos’ inclusion signal?

Neutrinos’ inclusion in the Forrester APO landscape places the company within an emerging category focused on the next stage of enterprise automation. The report identifies the market as nascent and highlights a shift from task-level automation to process orchestration at enterprise scale.

For insurers evaluating an AI orchestration platform, this shift changes the evaluation criteria. The question is no longer simply whether a vendor can automate an individual task. Buyers must assess whether a platform can coordinate complex processes, work across existing systems, manage changing context, and provide the controls required for production use.

The Forrester landscape identifies Neutrinos as serving financial services, healthcare, and insurance, with a geographic focus spanning North America, Europe, the Middle East, and Asia-Pacific. It also lists Neutrinos among vendors associated with extended use cases including data orchestration, high-volume processing, and support for long-running processes.

For insurance operations, the practical opportunity is to connect capabilities that are often fragmented today. Coordinated enterprise AI agents can support claims and underwriting activities. Real-time data processing can reduce delays caused by manual handoffs. Adaptive workflows can respond to different case conditions. Unified visibility can help operations leaders understand how work is progressing across systems and teams.

However, insurers should approach this transition deliberately. Forrester recommends a low-risk hybrid path from static to adaptive automation, noting that deterministic process engines will remain essential for controlling end-to-end processes. This is particularly relevant in insurance, where legacy platforms, regulatory requirements, and complex product rules cannot be replaced overnight.

The strongest implementations will not pursue autonomy for its own sake. They will apply autonomy where it creates measurable value, retain deterministic controls where reliability matters most, and involve employees where expertise and accountability are essential.

The next question for insurers

The question for insurers is no longer whether to deploy AI agents. It is whether the platform coordinating those agents is designed for governance, auditability, hybrid execution, and human oversight at enterprise scale.

Adaptive process orchestration gives this shift a name. More importantly, it provides a way to think about how insurers can move from isolated automation experiments to connected, intelligent, and controlled operations.

Neutrinos’ inclusion in Forrester’s The Adaptive Process Orchestration Software Landscape, Q2 2026 reflects the growing importance of this category for organizations seeking to modernize complex processes without losing control. 

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