Gurobi Decision Intelligence: How Does It Work?
Businesses make thousands of decisions every day—from allocating resources and planning operations to responding to shifts in demand and market conditions. Decision intelligence helps organizations understand, model, and improve how those decisions are made and evaluated. It can use data, business rules, and AI techniques such as machine learning and mathematical optimization to bridge the gap between insight and action and improve decision outcomes.
Watch the short video below to see how Gurobi helps organizations solve complex business challenges with decision intelligence.
What is Decision Intelligence Technology? Core Capabilities for Business Users and Decision Makers
Decision intelligence technology helps business users and decision makers understand, model, and improve how decisions are made. It brings together data, decision models, business rules, and AI techniques including mathematical optimization to compare alternatives, and recommend actions based on organizational objectives and constraints. Unlike traditional business intelligence, which primarily supports analysis and reporting, decision intelligence connects insights to actions and uses feedback from outcomes to improve future decisions.
With Gurobi's decision intelligence technology, you input the details about your complex, real-life business problem using relevant data sources, available data inputs, and business objectives. Then the technology gets to work, exploring the trillions of ways you can achieve your goals, given your business constraints.
Often in as little as seconds, you'll receive your detailed action plan. And as soon as your business situation changes—or you want to explore possible what-if scenarios—just adjust your inputs and run it again.
Input:
Your business objectives (what you want to achieve)
Your business constraints (the limitations you face)
Your decision variables (the questions you're asking)
Output
A detailed set of decisions that optimize your business objectives, given your constraints. It's how the world's leading organizations make optimized decisions, at the speed of change.
By combining optimization with connected data, organizations can improve operational efficiency, generate data-driven insights, and achieve stronger strategic outcomes across business functions.
Want to see how optimization can improve your organization's decision-making?
What Are Examples of Decision Intelligence?
Although there are countless ways to use decision intelligence technology, here are some real-world examples, with links to their stories:
Transportation providers such as Air France, Swissport, and Uber use decision intelligence to create optimal routing, staffing, and maintenance plans.
Professional sports leagues, including the National Football League and Beko BBL, plan their game schedules using decision intelligence.
Manufacturers use decision intelligence to plan and manage the procurement, production, and distribution of their products.
These examples highlight the benefits of decision intelligence, including improved supply chain planning, reduced operational risk, and more informed decisions as business conditions evolve.
Discover how organizations across industries use Gurobi to solve complex optimization challenges.
Why Is Decision-Making Difficult?
Decision-making is growing increasingly complex—with multiple factors to consider and multiple stakeholder inputs and opinions. And this complexity puts business decision-makers in an extremely challenging situation.
For example, you likely need to achieve multiple, often-conflicting objectives, such as:
Maximizing returns while minimizing risk
Maximizing production while minimizing environmental impact
Maximizing profits while minimizing employee churn
And you need to do so in the midst of:
Complex interdependencies
Regulatory constraints
Fluctuating energy costs
Global disruptions
Plus, you have many choices to make:
“Which discounts should we offer to which customers to maximize revenue?”
“What is the least costly way to reroute shipments?”
“What is the best way to allocate capital across all investment vehicles?”
To top it off, shareholders increasingly expect decision-makers to explain and defend their decisions transparently—and back it all up with data. For many organizations, information is spread across multiple systems, fragmented data, and external data sources, making it harder to analyze complex data and identify the best path forward.
It's no wonder decision-making is so difficult.
What's the Difference Between "Good" and "Optimal" Decision-Making?
You make decisions every day. And, of course, you work hard to make good decisions. But even "good" decisions can cost your organization millions in lost revenue and wasted time and resources.
That's because there's a huge difference between a good decision and an optimal decision.
When you make an optimal decision, you've considered every detail and every path and identified the best possible way forward—the one that will help your business achieve its goals, given its unique constraints. But that's not something our human brains can do on their own, so it requires a specialized technology: decision intelligence technology.
By applying decision logic, optimization, and business objectives, organizations can improve decision-making processes while keeping human intelligence at the center of strategic decisions. They can make fast, confident, unbiased decisions—even in the midst of rapid change and global disruption. What's more, they increase revenue, operational efficiency, customer satisfaction, and long-term business value.
With decision intelligence technology, you input your goals and limitations and the questions you need to answer. Then the technology evaluates trillions of possible solutions using available data points while considering your goals and constraints. Finally, it outputs your best-possible action plan, with detailed answers to your specific questions.
Why Is Decision Intelligence So Important?
Predictive analytics tools—like machine learning, statistical models, and simulations—seek to find patterns in data in order to predict what might happen in the future. And as long as your present situation continues to look like your past, these historical insights can help inform your decisions.
But these tools stop there. They can't tell you specifically what to do with those insights. As a result, most decision-makers are left to rely on “experience, ‘gut feeling,' or opinions” when making decisions.
That's why organizations are adding decision intelligence technology to their analytics toolkits.
What Are Examples of Combining Machine Learning and Decision Intelligence Technology?
By combining machine learning with decision intelligence technology, you can identify the optimal way to achieve your goals, given your predicted future.
For example, imagine you're planning a trip. A machine learning model can predict what you may encounter along your journey (weather, traffic, engine trouble). But with decision intelligence technology, you can take those predictions into consideration, as well as your goals (fastest, cheapest, safest route) and constraints (time, budget, speed limits, roadside services), and identify the single best route. In business settings, organizations can also orchestrate decision flow as conditions change.
Use machine learning to predict | Use decision intelligence to identify |
A customer’s likelihood to buy more with targeted offers | Which discounts to offer, to which customers, to maximize profits |
Supply chain issues, while there’s still time to remediate | The least costly way to reroute shipments |
Who will launch a cyberattack, before it happens | Which investigators to assign to potential threats, based on investigator skill and potential damage |
Experiments that are more likely to prove a hypothesis | Which experiments to pursue, based on talent, cost, and time |
Imminent machine failure | When to shut down the production line for maintenance, so as to minimize costs and production disruptions |
Price movements before the market does | How to allocate capital across all investment vehicles |
Frequently Asked Questions
What are decision intelligence platforms?
Decision intelligence platforms bring together data, analytics, business rules, and AI techniques to help organizations make better decisions.
Rather than simply reporting information, decision intelligence platforms provide recommendations that help organizations solve complex business problems and improve business outcomes.
What are the key capabilities and benefits of decision intelligence?
The key capabilities of decision intelligence include optimization, scenario analysis, predictive analytics, automation, and decision modeling. These core capabilities help organizations automate decision-making, improve operational efficiency, and support better decision-making across different business functions.
Potential benefits of decision intelligence (depending on your use case) are improved resource allocation, stronger customer satisfaction, a sustainable competitive advantage, and the ability to support decision makers with faster, more informed decisions.
What is the difference between decision intelligence and traditional business intelligence?
The difference between decision intelligence and traditional business intelligence is that business intelligence primarily analyzes historical performance, while decision intelligence recommends the best action to take. It combines predictive and prescriptive analytics, prescriptive analytics, optimization, and AI to turn insights into actions through structured decision workflows and decision flows. This practical discipline helps organizations explicitly model decisions instead of relying solely on intuition.
How do AI and data support intelligent decision making?
Decision intelligence uses operational, market, and other business data alongside AI techniques such as mathematical optimization. These techniques help organizations predict outcomes, evaluate alternatives, and turn complex information into better decisions.
How can organizations build trustworthy decision intelligence?
Successful decision intelligence depends on high data quality, transparent data, and strong data governance. Organizations should reduce fragmented and disparate data, connect information from multiple systems, and establish clear decision workflows that support consistent decisions. This approach also helps improve regulatory compliance, reduces operational risk, and allows organizations to orchestrate decision flow across teams while supporting business users.
How do you choose the right decision intelligence platform?
Choosing the right decision intelligence platform depends on your business objectives, available data, and operational needs. Organizations should evaluate whether a platform supports decision automation, integrates relevant data sources, and helps turn data into actionable insights.
As the decision intelligence market continues to grow, organizations should also consider features such as natural language processing, natural language search, real-time insights, decision-centric solutions, role based access control, and strong data governance to support regulatory compliance and risk assessment.
Resources like the Gartner Magic Quadrant can provide additional market insights, while collaboration between data scientists, data engineers, and business teams helps organizations maximize the value of their decision intelligence investments.
