Welcome to an insightful conversation with Chloe Maraina, a trailblazer in the realm of Business Intelligence and data science. With a passion for crafting compelling visual stories from big data, Chloe brings a unique perspective on how Decision Intelligence is reshaping the way organizations transform insights into actionable outcomes. In this interview, we dive into the essence of Decision Intelligence, exploring how it differs from traditional analytics and emerging technologies, its practical applications in high-stakes industries, the benefits and hurdles of adoption, and the critical steps to implement it effectively. Join us as we uncover how Chloe’s expertise is guiding businesses to make faster, smarter, and more compliant decisions in today’s fast-paced world.
How would you describe Decision Intelligence to someone who’s new to the concept?
Decision Intelligence is all about designing and managing the process of making decisions in a structured, repeatable way. Think of it as a system that combines data, predictive models, business rules, and human judgment to not just tell you what’s happening, but to guide you on what to do next and then learn from the outcome. Unlike traditional analytics that might just show you a snapshot of the past, Decision Intelligence focuses on creating a clear path from insight to action, making sure decisions are measurable and can be improved over time.
What sets Decision Intelligence apart from the Business Intelligence tools many companies already use, like dashboards?
The big difference is in the purpose and output. Business Intelligence tools, like dashboards, are great at summarizing what happened—think sales numbers or operational delays. They’re descriptive and often retrospective. Decision Intelligence, on the other hand, is about designing the moment of choice. It builds a workflow that takes data, applies rules and predictions, and delivers a specific recommendation or action, often embedding it directly into business processes. It’s not just about understanding the past; it’s about shaping the future with decisions you can track and refine.
Why do you believe Decision Intelligence is becoming so critical for businesses at this moment?
We’re at a point where the speed of operations, tighter budgets, and stricter compliance demands are putting immense pressure on organizations. Traditional reporting just can’t keep up with the pace of business today—decisions need to happen in real-time, right where the work is being done. Plus, with the rise of AI, many companies have powerful predictions but struggle to turn them into actions. Decision Intelligence bridges that gap, ensuring analytics investments pay off with measurable impact, especially in regulated sectors where showing how a decision was made is as important as the decision itself.
How does Decision Intelligence compare to technologies like Artificial Intelligence or Machine Learning in real-world applications?
AI and Machine Learning are fantastic at predicting outcomes or classifying data—like forecasting demand or spotting patterns. But they often stop at the “what’s likely to happen” stage. Decision Intelligence takes it further by embedding those predictions into a framework of rules, policies, and actions. It’s about using AI’s insights to answer, “What should we do right now?” and ensuring that decision leads to a tangible result. For instance, AI might predict equipment failure, but Decision Intelligence would prioritize the repair based on crew availability and safety policies, then trigger the work order.
What are some of the standout benefits of adopting Decision Intelligence, especially for organizations with limited resources?
For companies on tight budgets, Decision Intelligence is a game-changer because it focuses on delivering measurable value from existing data and analytics investments. It helps prioritize where to act, so you’re not wasting resources on low-impact decisions. It also boosts efficiency by automating routine choices while keeping humans in the loop for critical calls, which saves time and reduces errors. And in regulated industries, it provides a clear audit trail—showing exactly how and why a decision was made—which can save significant costs related to compliance issues down the line.
Can you share how Decision Intelligence supports compliance in heavily regulated sectors like government or life sciences?
Compliance is all about transparency and accountability, and Decision Intelligence excels here by making every decision traceable. It captures the data inputs, the rules applied, and even the model versions used, so you can explain every action to regulators. For example, in life sciences, when selecting trial sites, Decision Intelligence can score sites based on performance and risk, apply predefined thresholds for approvals, and document the rationale for each choice. In government, it ensures citizen services are triaged consistently with fairness metrics tracked across demographics, which helps meet equity standards while speeding up service delivery.
What’s a practical first step for a company looking to explore Decision Intelligence without overhauling everything?
Start small and focused. Pick one high-frequency, high-value decision that’s causing pain right now—like prioritizing work orders or triaging customer claims. Map out the current process, identify the trigger for that decision, and define a couple of success metrics, like how fast the decision is made or how often it’s overturned. Then build a minimal data set just for that decision, blend in existing rules with maybe a simple predictive model, and embed it into the workflow with a human backup for exceptions. This end-to-end pilot proves value quickly and sets a template for scaling up.
Could you walk us through a specific example of Decision Intelligence in action, such as predictive maintenance for utilities?
Absolutely. In utilities, predictive maintenance is a perfect use case. Imagine you’ve got hundreds of assets, like transformers or power lines, and limited crews to handle repairs during a heatwave. Decision Intelligence pulls together data on asset health, weather forecasts, and crew schedules to score and rank work orders by urgency. It might auto-approve low-risk repairs based on set policies, while flagging high-risk equipment—like something with safety concerns—for supervisor review. The decision gets pushed directly to field systems, with clear reasoning for each dispatch. The result? Higher uptime, safer operations, and a transparent process that can be audited or adjusted as conditions change.
What role does human oversight play in Decision Intelligence, particularly in high-stakes environments?
Human oversight is crucial, especially when the stakes are high. Decision Intelligence systems are designed to automate routine or data-driven choices, but they always include clear fallback paths for exceptions or high-risk scenarios. Humans step in to handle edge cases, approve critical actions, or override when context beyond the data matters. For instance, in a government triage system, complex citizen cases might be routed to specialized reviewers instead of automated workflows. This balance ensures speed and consistency without sacrificing judgment or accountability, and it builds trust in the system.
What’s your forecast for the future of Decision Intelligence in shaping business outcomes?
I see Decision Intelligence becoming the backbone of how organizations operate in the next five to ten years. As data grows and compliance demands tighten, businesses will need more than just insights—they’ll need engineered decision processes that are transparent, fast, and adaptable. I expect we’ll see wider adoption across industries, with systems becoming more integrated into everyday tools like CRM or ERP platforms. The focus will shift toward continuous learning, where every decision feeds back into improving the next one, creating a cycle of smarter, more impactful choices that drive real competitive advantage.
