Decision making is at the core of running any successful business. But in today‘s rapidly changing world, making the right choices at the right time is becoming extraordinarily complex. There are more variables to consider, more data to analyze, and more uncertainty around outcomes. This is where the emerging field of decision intelligence comes in – harnessing data, analytics and AI to design smarter business decision processes.
In this comprehensive guide, we‘ll explore what decision intelligence entails, how it works, key benefits for businesses, and leading solutions in the marketplace. Let‘s get started!
What Exactly is Decision Intelligence?
Decision intelligence refers to a range of methodologies and technologies for modeling, aligning, executing and improving data-driven business decision making. The key aim is to augment human decision makers with insights, recommendations and automation derived from advanced analytics and artificial intelligence.
At its core, decision intelligence is about understanding that decisions are based on causal models – our perception of how certain actions will lead to desired outcomes. As defined by Wikipedia:
"Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these causal chains."
So in essence, decision intelligence leverages predictive modeling, optimization algorithms, simulation and other techniques to map out the links between decisions, business rules, processes and results. This leads to higher quality decisions aligned to organizational goals.
According to Gartner, global spending on AI and ML solutions will reach $62 billion in 2024, up from $37.5 billion in 2021. As adoption of AI grows, interest in decision intelligence platforms is accelerating as well. A Deloitte survey of senior executives found that 47% have already implemented decision intelligence solutions, with 76% planning to adopt them within the next two years.
Key methodologies under the decision intelligence umbrella include:
- Decision Management – Structuring an organization‘s policies, rules, and actions for consistent decision automation
- Decision Modeling – Visually mapping decisions, logic and outcomes to understand dependencies
- Descriptive Analytics – Reporting on what has happened
- Diagnostic Analytics – Analyzing why something happened
- Predictive Analytics – Estimating what could happen in the future
- Prescriptive Analytics – Recommending the best course of action
How Do Intelligent Decision Models Work?
Let‘s break down the key steps involved in creating an intelligent decision model:
1. Observe
First, all available data related to the decision is collected – this can include historical data, transaction data, customer behavior data, market trends, competitive intelligence and more. The broader the data, the better.
2. Investigate
Next, analytics and data mining techniques are applied to analyze the data and uncover insights around the decision scenario. The aim is to deeply understand contexts and variables that can influence outcomes. Advanced AI algorithms can detect hidden correlations and patterns that humans may miss.
3. Model
With a firm grasp of the scenario, multiple decision models can be created to map out alternative actions, business rules, dependencies and projected outcomes. Simulation techniques assess the various models.
4. Contextualize
The business context, constraints and objectives are incorporated to refine the decision models and limit options to executable actions aligned to goals.
5. Execute
The AI system or human decision maker selects and implements the optimal decision path based on the contextualized models.
6. Learn
As outcomes from the executed decision become available, they are fed back into the system to enhance decision modeling and AI algorithms for the future.
This continuous loop of data-driven decision optimization lies at the core of decision intelligence.
Comparing Rules-Based vs. AI-Based Decisions
A key consideration in implementing decision intelligence is determining where to apply rules-based logic versus AI-based models.
Rules-based decisions rely on predefined business rules and logic flows to drive outcomes. They offer simplicity and transparency, but are limited in handling complex scenarios with many variables.
AI-based decisions can analyze a wide range of data inputs to continuously optimize outcomes. However, they lack explainability in some cases.
Here are some best practices on when to use each approach:
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Rules excel for high-volume operational decisions with clear cut decision criteria. Eg: approving routine invoices.
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AI delivers better results for decisions with many influencing factors that evolve over time. Eg: dynamic pricing, predictive maintenance.
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Blending rules and AI provides transparency for stakeholders while optimizing decisions. Eg: credit risk modeling.
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Start with rules-based models first to establish a baseline, then enhance with AI to improve performance.
Getting the right balance is key to maximizing the strengths of both methodologies.
Types of Decision Intelligence Models
There are three primary archetypes of decision models, differing in the balance of human vs AI involvement:
Human-Based Decisions
In these models, the AI system serves an advisory role – providing analytics, projections and insights to human decision makers who drive the final calls and actions. This allows leveraging AI while keeping humans in charge.
Machine-Based Decisions
Here the AI system is configured to make fully autonomous decisions and take actions based on the decision model without human oversight. While less common, certain high frequency operational decisions can be handled this way.
Hybrid Decisions
A collaborative approach where both humans and AI drive the decision process together based on the decision model. The balance of automation versus human judgment can be adjusted as needed. This provides the best of both worlds.
Hybrid models are emerging as a preferred approach for many enterprises, achieving optimal tradeoffs between AI-driven insights and human expertise.
Principles for Sustainable Decision Models
According to Gartner, sustainable and effective decision intelligence models should follow these core principles:
Relevance
Each decision should directly or indirectly contribute towards business goals and desired outcomes. For example, in customer service, relevance means taking actions that acquire, retain or grow customer lifetime value.
To ensure relevance, decision modeling should start with deep analysis of business objectives, KPIs and long-term strategies. Models optimized solely for accuracy may not align to desired business outcomes.
Transparency
With growing use of AI, there is often a tradeoff between accuracy and explainability. Black box models can make very accurate decisions but offer little visibility into the why. Maintaining transparency is vital for trust, governance and oversight.
Strategies like showing the top factors driving AI-based decisions or auditing models for biases can promote transparency. Explainable AI techniques are progressing rapidly as well.
Resilience
Decision models must be robust in the face of uncertain conditions and ever-evolving contexts. This includes detecting biases, security risks and graceful failure modes when encountering unprecedented scenarios.
Continuously monitoring model performance, recalibrating based on new data, and stress testing with edge cases improves resilience. As contexts shift, models must adapt.
Key Benefits of Decision Intelligence
Implementing decision intelligence can significantly enhance business performance and competitiveness through:
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Better Outcomes – By basing decisions on data and models versus gut instinct, results improve across metrics like revenues, costs and customer satisfaction. Quantellia found that 96% of firms report decision intelligence solutions significantly improved business results.
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Greater Speed – Automated decision making based on analytics far exceeds human speed and scalability. One logistics firm was able to optimize routes 46% faster using AI-based models.
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Reduced Biases – Data-driven decisions minimize human cognitive biases that skew judgment. Models also need to be monitored for algorithmic biases.
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Augmented Intelligence – Combined power of AI algorithms and human experience creates optimal decisions.
According to a McKinsey survey, decision intelligence adoption led to:
- 29% faster decision velocity
- 23% improvement in decision outcomes
- 32% increase in new revenue opportunities
The returns can be substantial, but realization relies on change management and building trust in AI recommendations.
The Future of Decision Intelligence
Looking ahead, decision intelligence will become an increasingly vital capability as the volume and complexity of business data continues exponential growth.
IDC predicts that global data volume will balloon from 33 zettabytes in 2018 to 175 zettabytes by 2025, driven largely by unstructured data like images, video and sensor streams. This enormous influx of "big data" makes AI-enabled decision intelligence critical.
In the years ahead, we can expect:
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Hybrid decision models combining AI and human intelligence will become the norm rather than the exception. Rote operational decisions will be fully automated while strategic choices involve human judgment.
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A shift from reactive to predictive modeling. With enough quality data, future scenarios can be simulated to guide proactive decisions aligned to goals.
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Specialized decision intelligence solutions tailored for industries like healthcare, manufacturing, retail and more verticals.
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Closer integration of decision modeling and automation capabilities into business processes and workflows.
The businesses that embrace decision intelligence now will gain a distinct competitive advantage in their industries going forward.
Decision Intelligence in Action: Real-World Case Studies
To make decision intelligence more concrete, let‘s look at some examples of enterprises deploying these solutions:
Dynamic Pricing Optimization
A leading regional airline was losing revenue due to suboptimal pricing of seats. By implementing AI-driven dynamic pricing, they increased revenue by 12% and reduced empty seats by 30% within a year. The system analyzes demand signals, competitions, and other factors daily to optimize fares.
Predictive Maintenance
An industrial equipment manufacturer was facing low uptime and high maintenance costs. By applying IoT sensor data, weather data, usage patterns and other inputs to their decision intelligence platform, they can now predict failures before they occur and minimize downtime.
Next Best Action Recommendations
A retail bank used decision intelligence to determine optimal real-time offers and interactions for customers. This flows through their web site, mobile app, call center and branches. Conversion rates on targeted offers have increased by over 50%. Data like purchase history, behaviors, demographics, etc. feed the model.
Claims Fraud Detection
An insurance company implemented AI and advanced analytics to spot fraudulent claims. The automated system has identified 12% more fraud cases compared to manual methods, saving millions in payouts. Anomaly detection algorithms find subtle patterns humans would miss.
These examples showcase the far reaching applications of decision intelligence across diverse industries.
Leading Decision Intelligence Platforms
Many enterprise software vendors offer decision intelligence capabilities, either stand-alone or embedded within larger platforms:
IBM – The IBM Decision Optimization suite includes powerful modeling and AI tools for optimization, prediction and simulation. IBM SPSS Modeler provides a robust analytics foundation.
Google Cloud – Google Cloud‘s decision intelligence tools leverage its advanced machine learning and analytics capabilities via services like BigQuery and Looker.
Oracle – Oracle offers a complete decision intelligence platform across its cloud applications like CX, ERP and SCM. The system can ingest data from any source.
SAS – With advanced AI, optimization algorithms and model management, SAS Decision Manager provides end-to-end decision support.
Busigence – Specializes in tailored decision intelligence software for manufacturing focused on production optimization and asset management.
Urbint – Urbint‘s decision intelligence platform focuses on risk modeling and mitigation for the insurance industry.
Quantellia – Quantellia‘s solutions are designed for banking, credit risk modeling and finance. Includes MLOps for model monitoring.
Xyleme – Optimizes media & entertainment decision making via AI-enabled analytics. Enables data-driven content creation.
The right platform depends on your industry, use cases, integration needs and other factors. We advise carefully evaluating options to determine the best fit. On average, companies use 3-5 different decision intelligence tools from multiple vendors.
Conclusion
Decision intelligence is transforming organizations worldwide through next-gen approaches to observations, predictions, modeling and recommendations. By infusing business decision processes with data, analytics and AI, companies can unlock superior speed, accuracy, and alignment to strategic goals versus relying solely on human judgment.
Now is the time to start exploring decision intelligence if you haven‘t already. As the volume and complexity of business data continues its relentless growth in the digital age, these AI-powered capabilities will likely become mandatory just to keep pace. Partner with leaders in this space to accelerate your decision intelligence journey.
