Today’s organizations are navigating rapidly growing volume and complexity of decisions at every level—operational, tactical, and strategic. Too often, the difficulty of contemporary decision-making is exacerbated by siloed data, fragmented processes, and the limitations of legacy solutions for assessing risk and gaining insight. At the same time, leaders are under pressure to adopt scalable and explainable AI, all while ensuring that their decisions remain accountable and trustworthy.

For modern enterprises, a significant opportunity lies in enabling human expertise and AI to work together, transforming fragmented insights into a unified, contextual view that empowers confident, organization-wide decision-making.

Understanding different decision types and the opportunities to enhance

The types of decisions facing enterprises tend to fall into one of three categories: strategic, tactical, or operational.

1. Strategic: These tend to be long-term, high-impact, often cross-functional decisions. Examples include M&A, market entry, and digital strategy.

2. Tactical: These are medium-term, departmental, or cross-team decisions. Examples include budget allocation and hiring plans.

3. Operational: These decisions tend to occur day-to-day, are high frequency, and often automated. Examples include fraud detection, routing, and approvals.

The processes by which these decisions are made—in which humans and AI increasingly work in tandem within Decision Intelligence platforms (DIP)—also break down into three categories representing opportunities to enhance decision-making: support, augmentation, and automation.

1. Decision support refers to the use of data-driven insights to assist human decision-makers. DIPs provide humans with context, tools, and recommendations—in the form of information, analysis, and insights—that enable humans to make more informed decisions.
   
   Decision support may involve offering insights based on AI and prescriptive analytics, turning data into actionable steps. Typically, this is delivered through interactive dashboards, data visualizations, or analytical insights that empower users to rethink their information, explore alternatives, and make more informed choices. Crucially, decision support keeps humans fully in control of their decision-making.
   

2. Decision augmentation involves enhancing human decision-making capabilities by providing actionable intelligence that assists decision-makers—often by combining data processing, advanced analytics, and machine learning with human insight. DIPs use AI and analytics to augment the decision-making process, with technology surfacing insights and offering suggestions/next best actions, or by enhancing how efficiently a human can work by summarizing information, drafting reports or narratives, which can greatly improve the quality and efficiency of decisions.
   
   Decision augmentation offers more advanced recommendations and insights than support and is particularly useful for complex decisions where human judgment is still required but can be significantly improved with AI-driven insights and workflows. However, humans remain responsible for ultimate decisions.
   

3. Decision automation is the process of fully automating decision-making using DIPs. When the risks are acceptable and the decisions are not too complex, DIPs can execute decision-making processes such as pre-known decisions and situational decisions automatically, without human intervention. This allows organizations not only to dedicate knowledge workers’ time and attention to processes and decisions that do require their involvement, but also to respond more effectively to unexpected opportunities or disruptions.
   
   Leveraging predefined business rules, advanced analytics, AI, and real-time data, decision automation is designed to streamline processes by automatically executing decisions at speed and scale, while ensuring consistency and reducing the potential for human error.
   

Of course, different types of decisions necessitate different ratios of automation to human interaction. Operational decisions (such as transaction monitoring or data stewardship tasks) can often be largely automated; tactical decisions (such as campaign execution or supply chain analysis) benefit greatly from decision augmentation; strategic decisions (data democratization, CX enhancement) are best served through decision support, with humans occupying a greater role at the helm.

Applying decision intelligence in practice

Taking a real-world example, Quantexa works with one of the largest banks in the world—one that serves individuals, corporate, and institutional clients worldwide, and has footprints in wealth management, retail, and commercial banking— to enhance its decision-making across all levels of the enterprise:

• Operational decisions: This organization uses Quantexa’s Decision Intelligence Platform to automatically detect and flag potentially illicit transactions. This reduces manual workload and enables faster, more accurate responses. It also helps eliminate duplicative technologies, lowering costs and increasing productivity.

• Tactical decisions: Quantexa’s Platform equips the organization’s investigators and front-line staff with contextual views of customers and counterparties. This unified view enhances customer service, improves compliance checks, and enables real-time detection and prevention of financial crime.

• Strategic decisions: This organization’s deployment of Quantexa’s Decision Intelligence Platform is part of a broader initiative to build a strong data foundation that supports global operations and innovation. By deeply understanding customer relationships and supply chains, this organization can expand and improve its services and better support multinational clients.

This organization’s deployment of Quantexa’s Platform is truly enterprise-wide, with decision intelligence now embedded into all of its core processes—from financial crime detection to customer engagement. This deep level of integration is possible because of the platform’s flexibility and scalability across business units, all while also ensuring data, models, AI, and even decisions are governed effectively. Every decision is accurate, explainable, and aligned with regulatory and strategic organizational goals.

This organization’s experience with Quantexa’s Decision Intelligence Platform shows how a global bank can not only use data to inform decisions, but also to transform how decisions are made—aligning them with strategic objectives, improving consistency, and enabling growth.

Driving better decisions with Quantexa’s Decision Intelligence Platform

Quantexa’s Decision Intelligence Platform helps organizations overcome the limitations of siloed, fragmented data by unifying it into a connected, contextual fabric. This trusted foundation provides a single, enriched view of customers, counterparties, and transactions—surfacing hidden risks and opportunities while breaking down barriers across the enterprise.

At its core, the platform is built on a decision-centric architecture that models real decision workflows, seamlessly embedding AI, business rules, and simulation. By operationalizing AI in a scalable and explainable way—without compromising on accuracy, transparency, or regulatory compliance—Quantexa enables organizations to augment human judgment. Leveraging Quantexa’s Decision Intelligence Platform enables enterprises to increase confidence in critical decisions and drive better outcomes across strategic, tactical, and operational levels.

A core capability: Composite AI

Composite AI is a foundational differentiator of Quantexa’s Decision Intelligence Platform. Quantexa utilizes various methods and techniques across the entire platform, from data to insight to action. Whether that’s using NLP and Deep Learning techniques for parsing, cleansing, and transforming, and matching data, expert models, machine learning, and graph ML for uncovering insights and scoring, or generative AI and agentic workflows to augment and automate manual processes. The platform applies the right method to the right task and champions transparency and explainability where possible.

This hybrid approach is fused with human-in-the-loop workflows, ensuring analysts can augment or override AI-driven outcomes for greater accountability, compliance, and adaptability. The result is a transparent, outcome-driven AI that delivers accuracy, speed, and trust across diverse use cases—from financial crime and customer intelligence to supply chain optimization and master data management.

Start your decision intelligence journey

Enterprises today require a decision-centric architecture that leverages AI and human collaboration. By calibrating support, augmentation, and automation to the right tasks, organizations can arrive at confident decisions that help realize strategic objectives and promote growth and innovation.

If you’d like to learn more about Quantexa’s Decision Intelligence Platform and how it transforms decision-making across organizations, we invite you to get in touch.