Decision Intelligence has been a buzzword in the field of technology for several years. However, the term became prominent when Gartner named it as a top technology trend in 2022. Today, decision intelligence is an important business strategy.
What is Decision Intelligence?
Decision-making is a critical business process. Thus, companies should have systems of record to model, monitor, learn and improve specific business decisions. This approach begins with focusing on critical decisions driving business performance, followed by people, process, and insights to consistently make high-quality decisions. Decision intelligence refers to the using available information to make informed decisions that serve the business objectives.
Shift in Decision-Making Dynamics
The pandemic has served as a catalyst for the adoption of decision intelligence. Decades of ingrained decision-making behaviors shifted within months, eradicating old habits and fostering new ones.
- Remote work brought a massive shift in work dynamics and team collaboration, thereby impacting decision-making. Leaders had to adapt, making agile decisions with team members present at different locations.
- Business strategy and operational changes accelerated rapid decision-making for modern businesses. Decentralization in decision-making empowered companies to drive sustainable improvements in operational processes.
By leveraging advanced AI/ML algorithms and data analytics, retailers can increase efficiency and improve customer satisfaction
Applying AI to decision-making makes a business outcome-oriented. It optimizes functions of every department and improves overall business performance.
The approach towards Decision Intelligence is intuitive, flexible and human as it analyzes data from both perspectives – internally from a business PoV and externally from a global perspective.
Insights helps businesses make strategic choices that align with revenue goals, marketing objectives, and growth.
Inventory Management
Predictive analytics and forecasting techniques based on historical data helps in efficient inventory management. Inventory placement decisions are accurate and sales plan for each store is efficient. It helps avoid wasteful promotions, fast shipping, and drives profit margins.
Reduce Cart Abandonment
Data from multiple sources – sales, customer touchpoints, and clickstream- helps retailers understand customer expectations. DI helps identify anomalies leading to loss of revenue due to cart abandonment. Using advanced analytics, retailers can discover best opportunities to upsell or cross-sell.
Sustainable Supply Chain
Are you facing the challenge of stock vacation? Every time a stock is moved from a warehouse to a distribution center, the retailer incurs additional costs due to extra logistics. However, with real time insights such as production output, actual demand, forecasted demand, processing costs, and transportation costs, retailers can optimize inventory levels minimize stock movement, and reduce operational costs.
Efficient Delivery Timeline
Retail businesses need to increase product pickups from a warehouse in a specified timeline. Decision Intelligence collects the relevant information to create an ML model. The model automates scheduling of order pickup, improving logistics and thus impacting the entire value chain. This enhances customer satisfaction levels.
Use Case: Supply Chain Transparency
We helped a client extract relevant data from tracking documents (invoices, bills of lading, customs slips) to tag them automatically to the correct PO. Our procurement AI solution helped automate the complete order tracking process.
Impact: 10x time saved in data entry, and less than 3 weeks to build and integrate the solution.
