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What is Decisions?
Decisions is an enterprise platform for business process automation and AI orchestration. It is aimed at organizations that need to coordinate work across multiple existing systems rather than replace them, and it combines workflow automation with governance of AI agents.
Its pitch centers on orchestration: connecting processes, data and AI-driven steps into a single governed flow. The page emphasizes speed and cost efficiency, and highlights filling gaps around core systems — a signal that it is positioned as a layer on top of ERP, CRM and similar systems of record, not a rip-and-replace option.
Who it tends to fit
- Operations and IT teams in larger organizations with several disconnected core systems.
- Groups that want to add AI steps to a process but keep oversight and control over what those agents do.
- Teams with recurring, rules-heavy workflows that cross departmental boundaries.
Trade-offs to weigh
- Enterprise orchestration platforms usually require real implementation effort and integration work; they are not plug-in tools.
- Governance features matter most if you actually plan to deploy AI agents in production. If your need is simple task automation, a lighter tool may be enough.
- Because it sits around existing systems, its value depends on how well those systems expose data and events.
A useful next step is to list two or three end-to-end processes that currently break down between systems, then check whether the platform's orchestration and AI governance capabilities map to those specific handoffs. If the gaps are mostly inside a single application, start with that vendor's native automation instead.
How does Decisions automate business processes?
Decisions automates business processes by combining workflow automation with AI orchestration and governance in one platform. Instead of only routing tasks or only running AI models, it coordinates both: it moves work across your existing systems, applies rules and approvals, and keeps AI agents under the same process controls as human steps.
Decisions describes the core idea as orchestrating work across systems and governing AI agents. In practice, that means a process can call an AI model to classify a document, then pass the result into a human review step, then update a core system, all within one governed flow.
H3 Where it fits in a real process
A typical pattern looks like this:
- A trigger arrives from a business system, such as a new invoice or support request.
- Decisions applies business rules to decide the path.
- An AI agent or model handles a judgment step, like extracting fields or drafting a response.
- A person reviews or approves when the rules require it.
- The platform writes results back to the systems of record and logs the outcome.
The page evidence emphasizes "fill the gaps around core systems," which is the practical value: it connects and coordinates systems rather than replacing them.
H3 What makes it different from simple automation
| Approach | What it handles well | Where it struggles |
|---|---|---|
| Basic workflow tool | Fixed, rule-based task routing | Judgment steps and unstructured data |
| Standalone AI tool | One model or one task | End-to-end process control and audit |
| Decisions | Workflow plus AI orchestration and governance | Requires process design and integration work |
The trade-off is implementation effort. Orchestration platforms need clear process ownership, defined rules and integrations, so the initial setup is more involved than a single-purpose automation.
H3 A concrete reader scenario
An operations team at a mid-size insurer receives claims by email. They could use Decisions to extract claim details with AI, route high-value claims to a human adjuster, and push approved claims into the claims system, with an audit trail for each step. That reduces manual triage while keeping humans in the loop where risk is high.
A useful next step is to pick one process that already spans two or three systems and has a clear approval step, then map its steps before evaluating any platform. If the process is entirely rule-based with no judgment or unstructured input, a simpler automation tool may be enough. If AI decisions need oversight and auditability, orchestration is the stronger fit.
How does Decisions govern AI agents?
Decisions positions governance as part of the same platform that runs automation, rather than as a separate policy layer bolted on afterward. Based on the site's stated focus, it governs AI agents by orchestrating them within enterprise workflows: agents are treated as participants in a process that the platform controls, alongside human tasks and system integrations.
That framing matters because it changes where control lives. Instead of each agent acting autonomously and being audited later, the workflow defines what the agent may do, when it is invoked, what data it receives, and what happens to its output before anything downstream depends on it.
What this means in practice
- Central orchestration: Agents are called as steps inside a governed flow, so their inputs and outputs pass through a defined sequence rather than direct system access.
- Human checkpoints: Because workflows can include human tasks, an agent's recommendation or draft can require review before it triggers a downstream action.
- System boundaries: The platform's stated role of filling gaps around core systems suggests agents operate through controlled integrations, not unrestricted credentials.
- Oversight and traceability: Process automation platforms typically log each step, which gives operators a record of which agent ran, on what, and with what result.
A concrete scenario
An insurance operations team wants an agent to summarize claims documents. Rather than giving the agent direct write access to the claims system, the workflow has the agent produce a summary, routes it to a human adjuster for confirmation, and only then updates the record. If the agent's output is wrong, the checkpoint catches it before it reaches the system of record.
Trade-offs to weigh
Tighter orchestration buys control and auditability but adds latency and design work. Highly autonomous agents may handle open-ended tasks more flexibly, yet they are harder to constrain and explain. Decisions appears aimed at organizations that value governed, repeatable processes over maximum agent autonomy.
Next step: List the specific decisions you want an agent to influence, then mark which ones need a human approval step. That list becomes your workflow design and your governance boundary. For platform specifics, see Decisions.
How does Decisions integrate with existing enterprise systems?
Decisions is built to sit on top of the systems an enterprise already runs, rather than replace them. Its stated purpose is to "fill the gaps around core systems" and "orchestrate work across your systems," so integration is the central idea rather than an add-on. In practice that means the platform connects to existing applications and data sources, then coordinates tasks, decisions and AI agents across them through automated workflows.
Decisions
What that looks like in practice
- Workflow orchestration: you model a process once and let it call whatever systems each step needs, instead of stitching point-to-point scripts together.
- AI agent governance: the platform describes governing AI agents, which matters if you want automated decisions to follow the same rules and approvals as human ones.
- Bridging gaps: the "fill the gaps" framing points to the common enterprise problem of processes that span several systems with manual handoffs in between.
Who this suits, and the trade-off
This tends to fit organisations with a patchwork of core systems — ERP, CRM, ticketing, databases — where staff currently copy data or chase approvals by hand. A concrete scenario: an insurance team receives a claim in one system, checks policy data in another, and needs a rules-based decision plus an audit trail. A platform like this is meant to connect those steps without rebuilding the underlying applications.
The trade-off is that orchestration layers add a dependency: you gain central visibility and governance, but you also need people who can model and maintain those workflows, and integration quality depends on what each source system exposes. Decide based on how many systems a single process crosses and how much manual handoff you are trying to remove — if the answer is "one system, no handoffs," a dedicated automation tool inside that system may be simpler.
What are the pricing options and free trial for Decisions?
Decisions does not publish pricing tiers or a self-serve free trial on its main site, so you cannot compare plans or start a trial from the page alone. The site mentions "free trial" and "pricing" as topics, but no pricing links or payment details are shown, which is typical for enterprise automation platforms that quote per engagement.
That usually means pricing is scoped to your deployment: number of users, workflows, systems integrated, and whether you need AI agent governance or on-premises hosting. A practical next step is to contact the vendor with a shortlist of the processes you want automated and the systems they must connect to, then ask for a scoped quote and a time-boxed pilot rather than a standard plan comparison.
For context on the wider category when evaluating alternatives, you can look at UiPath and Appian, which also sell enterprise automation and generally quote custom pricing.
How does Decisions improve efficiency compared to traditional automation?
Decisions targets the gap that traditional automation leaves behind: work that spans multiple systems, needs human judgment, or requires AI agents to be governed rather than left unsupervised. Its pitch is orchestration—coordinating workflows, AI agents and people across existing applications—rather than automating one task inside one tool.
Where the difference shows up
- Cross-system work. Traditional RPA or single-app workflow tools usually operate inside one application or follow rigid screen-level rules. Decisions positions itself as the layer that "fills the gaps around core systems," connecting processes that start in one system and finish in another.
- AI with governance. Instead of adding an unmanaged AI step, Decisions describes governing AI agents within the workflow, so automated decisions stay inside defined process rules.
- Speed and cost. The page claims faster, more cost-effective orchestration and cites figures such as 70% and 40%, though the page evidence doesn't specify what those percentages measure—treat them as vendor claims to verify, not benchmarks.
- Human-in-the-loop. Because orchestration includes people as participants, exceptions and approvals can be routed without rebuilding the whole process.
A practical example
A new supplier onboarding process might need a form (one system), a credit check (another), an AI document review, and a finance approval. Traditional automation often handles only the form or only the check. An orchestration platform is meant to run the whole chain and hand off to a person when confidence is low.
How to decide
Choose traditional automation when the task is narrow, stable and system-specific. Consider orchestration when your bottleneck is handoffs, exceptions or AI steps that need oversight. Before committing, ask the vendor to demonstrate one of your real cross-system processes end to end, and ask what the 70% and 40% figures actually measure.
For a broader view of the category, see Decisions.
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