10 Decision Intelligence Platforms to Consider in 2025

In 2025, decision intelligence is no longer a niche buzzword—it’s business-critical. The decision intelligence market is expected to expand from $15 billion in 2024 to $17.5 billion in 2025, growing at a 16.5% CAGR. Meanwhile, analysts project the market could reach up to $36-$50 billion by 2030, showing sustained momentum well into the next decade.

This surge reflects a growing urgency for businesses to make smarter, faster, and more reliable decisions. But that urgency is fueled by a critical underlying problem: poor data quality and misinformed decisions remain rampant.

A survey of 750 business leaders found that 58% of key decisions are based on inaccurate or inconsistent data most or all of the time. At the same time, 67% of organizations don't fully trust their data. That’s a recipe for missed opportunities and costly missteps.

Worse still, executives are facing analysis paralysis—overloaded with data but under-equipped to act decisively. Many leaders admit to delayed decisions, even when speed is essential. According to recent commentary, data overload is paralyzing CFOs and exec teams alike, leading to indecision at a time when agility is paramount.

Framing the challenge

The core challenge in 2025: Organizations are drowning in fragmented data, siloed analytics, and distrust, preventing timely and well-informed decisions. Traditional BI tools often fall short under these conditions.

Decision intelligence platforms provide the missing link, bringing together reliable data, real-time analytics, embedded AI/ML, and actionable insights with the goal of turning data into delivered outcomes.

In this article, you’ll discover what to look for in a decision intelligence platform and explore 10 platforms worth considering in 2025, each offering a unique blend of real-time analytics, AI integration, automation, and collaboration.

What is a decision intelligence platform?

A decision intelligence platform combines data integration, business analytics, machine learning, and automation to help organizations make more informed and timely decisions. Unlike traditional business intelligence (BI) tools, which focus primarily on reporting and dashboards, decision intelligence platforms enable real-time, context-aware decision-making by weaving together insights, predictions, and actions.

These platforms are often used to operationalize AI across business functions, deliver embedded insights into daily workflows, and close the gap between data analysis and decision execution.

Benefits of using a decision intelligence platform

If you're new to the world of decision intelligence, here's the good news: you're not alone—and you're not behind. The shift from traditional dashboards to action-oriented insights is still underway across most industries. Decision intelligence platforms are built to help everyone, not just data scientists, move from endless reports to smarter decisions that actually move the needle.

Here’s why these platforms are worth exploring:

1. From gut feel to data-backed confidence

Let’s be honest: most decisions still rely on gut instinct. And while experience matters, making decisions based on outdated spreadsheets or siloed reports leaves room for error. Decision intelligence platforms consolidate your data, clean it up, and surface insights in real time. That means your hunches can finally have backup, and your decisions can stand up to scrutiny.

2. Speed without the shortcuts

Traditionally, making a “data-driven” decision took time: analysts needed to pull reports, teams had to align, and by the time the insights were ready, the moment had passed. DI platforms shortcut that process by automating the grunt work like data prep, model running, and alerting, so you can focus on interpreting and acting. Think faster decisions with fewer emails and less waiting.

3. AI without the intimidation

You don’t need to be a machine learning engineer to use AI anymore. Modern DI platforms bake predictive analytics and AI features into everyday tools: forecasts that update in real time, anomaly detection that alerts you before problems escalate, and recommendations you can act on without writing a single line of code. It’s AI, minus the jargon.

4. Collaboration that moves decisions forward

Insight alone doesn’t lead to impact—alignment does. Most DI tools today include built-in collaboration features: think comments, shared dashboards, workflows, and automated alerts that loop in the right stakeholders. When the whole team has access to the same up-to-date insights, decisions don’t stall in meetings—they happen in motion.

5. Scaling decisions without scaling teams

For small teams or companies without a full data department, decision intelligence is a force multiplier. It turns messy, scattered data into clean, decision-ready insight. Instead of relying on a bottlenecked analytics team, you can empower your marketers, product leads, and ops managers to explore data and find answers themselves with governance and controls baked in.

Whether you're trying to get a clearer view of your sales funnel, predict next quarter’s churn, or automate responses to operational issues, decision intelligence platforms give you the toolkit to do it faster, smarter, and more collaboratively.

What to look for in a decision intelligence platform

Choosing a decision intelligence (DI) platform can feel like shopping for a high-tech coffee machine: lots of buttons, sleek dashboards, and promises of transformation. But what actually matters when it’s time to make a decision that affects your business?

Here’s a breakdown of the features that really count, especially if you're just starting with DI tools.

1. Real-time data (because last week’s insights are already outdated)

Your business doesn’t pause. Your data shouldn’t either. Look for a platform that supports real-time or near-real-time analytics so you can make decisions based on what's happening now, not a static report someone ran last Monday. Live dashboards, streaming data integrations, and instant alerts are huge here.

Bonus tip: If you’re in retail, logistics, or customer service, real-time isn’t a luxury; it’s essential.

2. Built-in AI and automation, minus the PhD requirement

A good DI platform should do more than show you what happened. It should help you predict what might happen next and recommend what to do about it. Tools with automated forecasting, anomaly detection, and no-code model deployment can put AI insights in your hands, even if you're not a data scientist.

Look for words like "embedded ML," "predictive analytics," or "auto-modeling".

3. Easy, intuitive interface (so your whole team can use it)

If only one person on your team knows how to use the platform, it’s not helping you move faster. Prioritize platforms that offer drag-and-drop interfaces, natural language search, or interactive dashboards designed for non-technical users. The goal: reduce the number of Slack messages asking, “Can you run this report for me?”

Pro tip: Try a demo. If you’re confused after five minutes, your team probably will be too.

4. Strong data integration capabilities

Great decisions come from connected data. Whether you're pulling from cloud warehouses, spreadsheets, SaaS tools, or even legacy systems, your DI platform should play well with them all. Look for a wide range of prebuilt connectors and APIs so you can bring in the full picture without custom builds.

Think of this like plumbing: the fewer leaks, the more reliable your insights.

5. Collaboration tools that keep the decision-making loop tight

Insights shouldn't live in isolation. Some platforms now include built-in messaging, comments, and workflow triggers so decisions can happen within the platform, not just during your Thursday team sync. This helps you close the loop from insight to action quickly and visibly.

Ask: “Can I tag someone here?” or “Can this trigger a Slack/Teams message?” If yes, you’re in good shape.

6. Governance and control (because not everyone should see everything)

Even the most open organizations need boundaries. Whether it’s financial data, HR metrics, or customer privacy, make sure the platform supports robust access controls, audit logs, and versioning. You want transparency where it helps and guardrails where it counts.

This is especially important if you're in a regulated industry, or just want to avoid “accidental insight leaks.”

7. Flexibility to grow with your team

You might start small with just one department or use case, but your needs will evolve. Look for platforms that scale across users, teams, and data sources without reengineering everything. Cloud-native options tend to offer the most agility, especially if you plan to expand into more advanced AI or automation later.

The best decision intelligence platform is the one your team will actually use.

It’s not about buzzwords—it’s about making better decisions, faster, with the tools and data you already have. Prioritize usability, automation, collaboration, and flexibility, and you’ll be set up to make smarter choices with less guesswork.

10 decision intelligence platforms to consider in 2025

Choosing the right decision intelligence (DI) platform comes down to finding the best fit for your team’s size, data maturity, and goals. Below are 10 standout platforms, each offering distinct strengths for different types of users, from business analysts and IT leads to startup founders and ops managers.

1. Domo

Best for: Teams without dedicated data science resources who want to move fast and act on data in real time.

Why it stands out: Domo helps teams bridge the gap between insight and action by combining real-time dashboards, automation, and low-code tools. It’s especially powerful for organizations that want to get decision-ready data into the hands of non-technical users quickly and securely.

What users gain: Faster time-to-insight, better cross-departmental visibility, and a more data-empowered workforce, even without a large analytics team.

2. BentoML

Best for: Data scientists and MLOps engineers who want full control over deploying machine learning models.

Why it stands out: BentoML isn’t a traditional BI platform—it’s an open-source framework for packaging and deploying ML models at scale. But for organizations building custom AI into their decision pipelines, it offers flexibility, scalability, and fine-grained control.

What users gain: A seamless path from model training to real-time inference in production apps, all while staying in control of the ML stack.

3. ThoughtSpot

Best for: Business users and teams who want quick, self-serve answers from complex data using natural language.

Why it stands out: ThoughtSpot makes querying your data feel as simple as a Google search. Its AI-powered search interface empowers marketing, sales, and operations teams to uncover insights without writing SQL or waiting on analysts.

What users gain: Fast, ad hoc answers without bottlenecks and a more confident data culture across the business.

4. Qlik Sense

Best for: Mid-sized to enterprise teams looking for deep analytics, visual exploration, and strong hybrid-cloud options.

Why it stands out: Qlik’s associative engine allows users to explore data contextually, uncovering relationships traditional SQL queries might miss. It’s built for analysts and data-savvy users who want rich visual discovery with robust governance.

What users gain: Greater insight depth, fewer blind spots, and more confidence in complex business scenarios.

5. IBM Cognos Analytics

Best for: Enterprise organizations with complex governance needs and a need for AI-augmented reporting.

Why it stands out: Backed by IBM’s deep AI and enterprise security credentials, Cognos offers powerful automated insights, narrative reporting, and regulatory-grade control. Ideal for financial services, healthcare, and other compliance-heavy industries.

What users gain: AI-powered reporting at scale with peace of mind around security, governance, and auditability.

6. SAS Decision Manager

Best for: Teams building rule-based decision systems in regulated or precision-heavy industries like finance or pharma.

Why it stands out: SAS Decision Manager helps operationalize statistical models and business rules into automated decision flows. It supports integration with external systems and regulatory workflows, making it a go-to for critical, repeatable decision processes.

What users gain: Consistent, traceable decisions backed by rigorous analytics, which is ideal when stakes (and scrutiny) are high.

7. Microsoft Power BI

Best for: Organizations already embedded in the Microsoft ecosystem looking for accessible, collaborative BI.

Why it stands out: Power BI is deeply integrated with Microsoft tools like Excel, Teams, and Azure. It offers strong data modeling, AI visuals, and tight security. With low-code tools and drag-and-drop dashboards, it’s accessible to beginners while still powerful for analysts.

What users gain: Fast adoption, strong collaboration, and a smooth learning curve, especially for users already fluent in Excel.

8. SAP Analytics Cloud

Best for: Enterprise organizations with heavy planning needs and close ties to SAP ERP systems.

Why it stands out: SAP Analytics Cloud merges planning, predictive analytics, and business intelligence into a single platform. It’s especially powerful for finance, supply chain, and operations teams looking to link data to strategic planning.

What users gain: Tighter alignment between forecasting and action, and deeper value from existing SAP investments.

9. TIBCO Spotfire

Best for: Analysts and technical teams in industries with streaming or scientific data (energy, manufacturing, healthcare).

Why it stands out: Spotfire supports real-time data streaming, predictive modeling, and advanced statistical analysis—all in a highly visual, interactive interface. It’s ideal for data-heavy environments that require constant monitoring and rapid response.

What users gain: Real-time operational oversight, advanced analytics, and flexibility to build custom workflows.

10. Sisense

Best for: Product teams, SaaS companies, and data engineers embedding analytics into customer-facing apps.

Why it stands out: Sisense takes an API-first approach to analytics. Its Fusion platform lets developers embed dashboards and decision logic directly into the tools customers or internal users already use—without requiring them to learn a new interface.

What users gain: Customizable, seamless analytics experiences inside the tools that matter, no context-switching required.

The future of decision-making starts with the tools you choose today

The pace of business isn't slowing down, and neither are the stakes of every decision. In 2025, the difference between reactive and proactive organizations often comes down to whether their teams have the right tools to turn data into action, fast.

Decision intelligence platforms aren’t just another analytics layer. They’re a bridge between insight and execution—a way to empower everyone, from frontline managers to C-suite leaders, to make confident, data-backed decisions in real time.

Whether you're a small team just beginning your data journey or a large enterprise streamlining decision flows at scale, the tools in this list offer a range of capabilities to fit your needs and maturity level. From embedded AI to low-code automation and real-time collaboration, DI platforms are reshaping how modern businesses operate.

If you're looking for a platform that brings together data, people, and AI in a single, intuitive experience—Domo can help you get there. With real-time dashboards, automated workflows, and AI-powered insights built for action, Domo supports teams of all sizes in turning decisions into impact.