AI has opened up new avenues of data exploration and insight seeking for organizations. But making business decisions based on experience, intuition, and analytics can be complex and time taking. If decisions taken are not based on the right data or taken in the right context, outcomes can be catastrophic. In fact, McKinsey reports that the average S&P 500 company wastes $250 million per year due to poor decision-making. This is why organizations are adopting decision intelligence, an operational AI framework and data foundation, to make informed and confident business decisions based on trusted data.
Why Every Business Leaders Should Get to Know About Decision Intelligence
Advancement of technology has brought faster industry change cycles, heightened customer expectations, innovative competitors, more operational complexity, and high market volatility. Joining the dots of basic analytics relying on experience leaves a lot of room for guess works for business leaders. Decision intelligence leverages AI to focus on the business needs of nuanced decision-making. It is a framework that fulfills the operational needs of business decision makers instead of just solving mathematical challenges and churning numbers. A key differentiator of Decision Intelligence is its focus on outcomes: It is a meeting point for traditional BI and analytics with the decision-making business leader.
The Components of a Decision Intelligence Platform
The core of a Decision Intelligence platform rests on the following components:
Trusted data foundation
A trusted data foundation is essential to improve data accessibility and ensures the quality of your data. A unified view of your data helps to break down data silos and take a comprehensive look at your data. Establishing a reliable data foundation involves ensuring data quality, minimizing data inconsistencies and biases, so that decision intelligence platforms provide more accurate and objective insights. A trusted data foundation supports a data-driven culture by making insights accessible to all stakeholders, fostering collaboration and strategic alignment.
AI-Powered contextual analytics
Contextual analytics is the practice of analyzing data within its relevant context, such as time, location, user behavior, and environmental factors, to gain deeper insights and make more informed decisions. Contextual analytics provides insights that are timely, personalized, and actionable, enabling decision-makers to understand the “why” behind the data. This approach streamlines decision-making processes by providing context-specific data directly within the applications users work with, reducing the time and effort required to make informed decisions. By incorporating contextual information, AI models can offer more accurate predictions and recommendations, enhancing the overall effectiveness of AI-powered decision intelligence systems. Decision Intelligence platforms use AI and machine learning to connect billions of data points across internal and external sources, providing a trusted and reusable data foundation. This integration enables the identification of complex relationships and patterns that might be missed by traditional analysis
Augmented and automated decisions
A Decision Intelligence platform leverages augmented and automated analytics to enhance the decision-making process. Augmented analytics empowers users by providing data-driven recommendations, uncovering hidden patterns, and offering predictive insights. Automation within DI platforms streamlines repetitive tasks, accelerates data processing, and ensures consistency in decision-making. By automating routine decisions and processes, Decision Intelligence platforms enable faster decision-making cycles.
Industry Specific Use Cases of AI-Driven Decision Intelligence
AI-driven decision intelligence is transforming industries by providing actionable insights and automating decision-making processes.
Finance: Fraud detection and risk management
AI-driven decision intelligence plays a critical role in fraud detection and risk management. Advanced machine learning algorithms analyze vast amounts of transactional data to identify patterns that may indicate fraudulent activities. This real-time monitoring enables financial institutions to prevent losses and protect customer assets. Additionally, AI helps assess creditworthiness and predict potential risks, allowing for more informed lending decisions and portfolio management.
Healthcare: Personalized treatment and predictive diagnostics
In healthcare, AI-driven decision intelligence is revolutionizing patient care through personalized treatment plans and predictive diagnostics. By analyzing medical histories, genetic profiles, and real-time health data, AI systems can predict patient outcomes and suggest tailored treatment strategies. This approach not only improves patient care but also helps healthcare providers manage resources more effectively.
Supply Chain: Optimization and demand forecasting
AI-driven decision intelligence is transforming supply chain management by optimizing logistics and predicting demand. Advanced analytics help companies analyze historical sales data, seasonal trends, and external factors like weather and economic conditions to forecast demand accurately. This enables businesses to manage inventory levels effectively, reduce waste, and ensure timely delivery of products. Decision Intelligence platforms can automatically reroute a plan from a disrupted route to ensure timely and hassle-free delivery. Automated systems raise PO for SKUs going out of stock.
Marketing: Customer Segmentation and personalized campaigns
In marketing, AI-driven decision intelligence is used to segment customers based on their behavior, preferences, and demographic data. This segmentation enables marketers to create personalized campaigns that resonate with specific audience groups, increasing engagement and conversion rates. AI also helps analyze customer feedback and sentiment, allowing marketers to refine their strategies and improve brand reputation.
Real Life Examples of Decision Intelligence
Speedy
Speedy aimed to minimize inventory levels while maintaining high service levels across its extensive network of over 200 depots in the UK and Ireland. The complexity of managing 300,000 assets made traditional forecasting methods inefficient. Speedy implemented a multi-layered forecasting system using AI and machine learning. This approach combined various data sources, including inventory, demand, supply, customer, and EPOS data, to predict demand accurately. An optimization algorithm determined the optimal stock levels for each product, considering operational constraints. Speedy achieved an 18% reduction in inventory levels without compromising service quality. The integration of AI outputs into Speedy’s ERP system enabled informed decisions on stock management, ensuring the right quantities were at the right locations at the right time. The system helped Speedy decide whether to buy, repair, or relocate stock, optimizing asset utilization and improving Return on Capital Employed (ROCE).
Eurocell
Eurocell, a leading UK manufacturer and distributor of UPVC products, has leveraged Decision Intelligence to transform its customer experience and business operations. By implementing AI-driven solutions, Eurocell achieved a 73% increase in website recommender-driven average order values. This was accomplished through hyper-personalization, where AI models analyze customer data to provide tailored product recommendations based on purchase behavior. These recommendations are integrated into Eurocell’s email service provider to send highly relevant emails to customers. Additionally, Eurocell has also transformed its inventory management using AI, optimizing stock levels across its branch network and improving product availability by 6.7%. This has resulted in significant operational benefits, including the release of £1.86 million in inventory and an increase in average transaction values
Akisgorta
Aksigorta, a leading Turkish insurance company, faced challenges in competing on price using traditional auto policy pricing models. To address this, Aksigorta developed an AI-based pricing model that enhanced its understanding of the risk associated with each customer. Aksigorta experienced a significant increase in its market share, with a 55% rise in the Turkish passenger car market and a 237% increase in the light commercial vehicle market. The company’s overall margin improved from 15% to 23%, reflecting the success of the AI-driven pricing strategy in enhancing profitability.
Carhartt
Carhartt, a leading workwear retailer, faced challenges in selecting optimal locations for new stores. To address this, the company developed a decision intelligence tool that integrated various data points, including consumer behavior, historical sales, employment levels, weather, and socioeconomic factors. This tool enabled Carhartt to predict demand at a postal code level, providing insights into revenue opportunities across different locations in the U.S. The decision intelligence tool significantly enhanced Carhartt’s ability to select profitable store locations by analyzing complex data sets. By leveraging data-driven insights, Carhartt was able to make informed decisions about where to expand its retail footprint, ensuring strategic growth and maximizing revenue potential.
How to Get Started with Decision Intelligence
Great decisions make great business. Decisions are driven by data which has no dearth in today’s businesses. In fact, businesses are drowning under a deluge of big data, often failing to fish for the important bits of information or screen the right signals. Here’s a step-by-step guide to implementing Decision Intelligence (DI) in your organization:
1. Define Business Objectives and Challenges
Identify Key Decision Areas: Pinpoint areas where DI can have the most significant impact, focusing on high-value decisions with clear metrics for success.
Set Clear Goals: Define specific, measurable, achievable, relevant, and time-bound (SMART) objectives for your DI initiative.
2. Address Data Silos
Data Unification: Use tools that enable your team to leverage operational data from various sources across the business.
Data Quality and Governance: Ensure data is clean, accessible, and governed properly to avoid AI project failures.
3. Focus on Outcomes
Business Case Alignment: Ensure DI initiatives are driven by desired business outcomes and align with existing processes.
Integration with Existing Systems: Use platforms well-integrated with your business’s key operational solutions to simplify deployment.
4. Develop Trusted Decision Intelligence
Transparency and Ethics: Develop AI systems with transparency and ethics in mind to build trust among stakeholders.
Explainable AI: Use platforms with explainable AI tools to make decisions understandable and build confidence.
5. Ensure Scalability
Accessible Technology: Implement a platform that lowers the barrier to entry for DI, enabling various business users to leverage it.
Consistent Best Practices: Ensure solutions are developed consistently across the organization using a DI platform.
6. Engage Stakeholders and Allocate Resources
Collaborative Approach: Engage stakeholders across departments to foster buy-in and ensure comprehensive strategy alignment.
Resource Allocation: Allocate appropriate financial and human resources to manage the integration effectively.
7. Implement and Monitor
Pilot Project: Start with a low-risk pilot project to demonstrate DI’s value before scaling.
Continuous Evaluation: Track key performance indicators (KPIs) and refine the DI approach based on feedback and results.
Conclusion
A Gartner study found that by 2026, 75% of Global 500 companies will apply decision intelligence practices, including the logging of decisions for subsequent analysis. Trusted and contextual data is essential to trigger timely decisions for enterprises. A Decision Intelligence system will validate and streamline data and provide recommendations accelerating the decision-making process of business leaders.
