Actionable Intelligence Transforms Data to Smarter Decisions
“That’s interesting, but now what?” is far too often the response to major data, analytics, and business intelligence initiatives. At DecisionCAMP 2025 last month, Carole-Ann Berlioz, Sparkling Logic’s Co-Founder and CPO, addressed the challenge of unactionable insights in her talk, “From AI to Actionable Intelligence Through Real-Life Use Cases.” Without the end goal of smarter decision-making, the next AI wave will continue to proliferate intelligence that isn’t actionable. Therefore, organizations must align AI projects with measurable business objectives.
AI is Multifaceted
AI these days seem to mean AI agents, autonomous systems designed to perceive and act without human intervention, and more specifically, GenAI-powered agents which are capable of generating new content. However, not all agents are nor should rely on GenAI. For example, a rules-based agent is sufficient for calculating price. AI is multifaceted and also includes business rules, predictive analytics (machine learning), large language models (LLMs), and more. As organizations seek to adopt more agents, two major considerations come into play:
- AI strategy: What kind(s) of AI should be used?
- Agentic architecture: How should the agents interact with each other?
One way to address these questions and facilitate agent design is to isolate function. According to Carole-Ann, core functions include the following:
- Gathering intelligence: Insight agents can analyze data, detect patterns, and generate predictions.
- Making decisions: Decisioning agents can apply business logic, evaluate trade-offs, and ensure compliance.
- Taking actions: Execution agents can automate workflows, trigger actions, and interact with systems.
Using this framework, organizations can not only draw a clear line from intelligence to action but also identify where agents can augment or replace manual tasks.
Business Analysts are Becoming Architects of Agentic Orchestration
In a previous post, we covered GenAI use cases for business analysts. While business analysts can leverage AI to improve work efficiency, Carole-Ann argued that AI is also fundamentally changing their role. Agentic architecture is not only a technical evolution but also a strategic one. Business analysts will become integral in identifying which AI-powered strategies to use and how to implement them within the organization. Applying existing skillsets, business analysts will design and optimize how agents interact with each other.
Business Analyst Skill | Business Analyst Role in Agentic Architecture |
|---|---|
| Structured Thinking | Designing agent workflows and decision tree |
| Problem Solving | Identifying bottlenecks and refining agent roles |
| Data Interpretation | Feeding agents with clean, contextual data for better outcomes |
| Rules Authoring | Engineering effective instructions for agents |
| Stakeholder Alignment | Translating business goals into agentic system objectives |
Think Big, Start Small
As a practical next step, Carole-Ann recommended that organizations start out with a pilot in one domain such as collections, work out the kinks, and then expand into other domains:
- Audit Your Ecosystem: Define your current process and systems and identify where agents can be leveraged
- Choose Your AI Strategy: Work with business analysts to define agents and design agentic workflows
- Align Your Strategy with Business Objectives: Ensure every agent’s output maps to measurable KPIs
- Scale Responsibility: Build appropriate levels of explainability, governance, and ethical safeguards.
View the recording of Carole-Ann’s presentation here.
Contact us today for a customized demo of SMARTS™ to learn how you can build and optimize decisioning agents!
More in our DecisionCAMP ’25 Blog Series: