Watch this Ask the Expert session to learn how SAS Customer Intelligence 360 helps marketers uncover key insights about their customers and drive measurable business growth.
Watch the Webinar
You will learn how:
- To operationalize customer insights into real-time, personalized decisions that scale.
- To use arbitration, business rules and contextual data to drive the next best action.
- Other industries use decisioning excellence that translates into measurable business impact.
The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.
Q&A
Where are the decisions deployed? Does the software come with its own runtime environment, or do you need another environment?
Decisions can be deployed in the customer’s environment. The design environment runs as a service within our solution and creates a “decision container.” This container can be downloaded and deployed locally, which is ideal for hybrid architectures where sensitive data stays in your environment.
For production, the decision container is deployed in the runtime environment and accesses data there. Customers can manage deployment themselves or use our “fleet architecture” for one-click deployment from the design environment. This allows us to push the container into the customer’s cloud or cluster, reducing the burden on their team.
How do you see agentic tooling supporting decisioning?
A decision is a sequence of steps triggered by an event, covering eligibility checks, action assignment, arbitration, and optional external calls. Agentic AI enhances this flow by deeply understanding context, analyzing incoming real-time and historical data, and continuously learning from prior contacts and customer responses. Instead of static logic, AI can recommend how rules should adapt, which actions should be favored, and when suppression or escalation makes sense based on observed outcomes.
In CI 360, we see this evolving:
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Assistants that recommend thresholds, weights, and rule adjustments based on patterns and performance.
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Co-pilots that aggregate signals across channels and explain why a certain action was chosen or why performance shifts.
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Agents that allow users to describe intent in natural language and have the system create or refine decisions, journeys, and rules automatically.
Longer term, agents will continuously interpret context, evaluate decision outcomes, and proactively suggest optimizations, effectively turning decisioning from a configured system into a self-improving one.
Can you upload and use external adaptive/predictive models to run decisions?
Yes, this is in progress. Today, we can already consume predictive or adaptive scores from external systems and use them directly in eligibility, arbitration, and action assignment. The next step is enabling admins to execute Python code securely within the platform, allowing custom models, feature logic, or real-time calls to external model services. This makes it possible to operationalize advanced models in decisions while keeping governance, performance, and explainability under control.
You mentioned kitchen devices and cars—what else is coming in connected channels?
Expect an explosion of connected devices: smart home systems, vehicles, health devices, and emerging tech like smart glasses from Meta and Apple. These channels will continuously stream real-time context such as location, intent, environment, and even physical state. On top of that, wearables, in-store sensors, voice assistants, connected TVs, and industrial IoT will become decisioning entry points. The real shift is that channels stop being “communication endpoints” and become live signal sources, allowing decisions to adapt moment by moment rather than reacting after the fact.
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