Now, picture this instead: the data pulls itself together, relevant risks are flagged in real time, and the report drafts itself. You simply review and hit send.

This kind of seamless orchestration and automation has become a reality with the right tools and platforms. By connecting disparate data, automating workflows, and embedding intelligence into decision-making, organizations can reduce complexity and improve productivity.

At Tiger Analytics, we focus on three foundational pillars when building an analytics solution that is optimized and streamlined for efficiency and effectiveness:

• Reliable data
• Creating extensive data lakes with new governance processes
• Setting up connectors across data applications
• Strong analytics engine
• Managing and maintaining the diverse dependencies as the solution evolves
• Improvements to the framework (Agentic or otherwise) or dependencies
• Efficient implementation
• Identifying the right set of tools/framework dependencies to onboard
• Identifying internal/external data dependencies
• Building orchestration and governance on those dependencies

Over the years, we used this foundation to build accelerators and SOPs, which connect business needs with domain context and technical knowledge, for faster and smoother implementation.

Agentic AI platforms like Google Agentspace follow the same principles to help users across the enterprise quickly access information from various sources, summarize and understand the data, and take action with the help of AI agents. They provide the flexibility to integrate custom accelerators and SOPs, along with the structure needed to set up custom governance frameworks that ensure integrity throughout the decision-making process.

In this blog, we explore regulatory compliance in retail banking and demand forecasting in retail, and the role AI agents can play in reducing risk and improving outcomes.

Use case 1: Automated regulatory reporting and compliance monitoring in retail banking

Retail banks navigate a maze of compliance and regulatory requirements such as Basel III, GDPR, and more. As of 2023, 88% of global companies said GDPR compliance alone costs their organization more than $1 million annually, while 40% spend more than $10 million. These regulations demand extreme diligence with a focus on transparency, accuracy, and timeliness.

Traditionally, banks rely on multiple systems to collect and report data:

• ERP systems for financials
• CRM tools for customer data
• Core banking systems for transaction histories
• Email communication for policy updates and legal notices

Manually piecing together fragmented data is a time-consuming and error-prone process that may expose the organization to compliance risks. Here’s where we believe Agentic AI can add value:

• Seamless data integration: Platforms such as Agentspace integrate data from various sources, including email communications, using prebuilt 2P and 3P connectors for easy aggregation and standardization of data as required for regulatory reports. This eliminates the need for manual data entry.
• Real-time compliance monitoring: Custom AI agents can be built and orchestrated to continuously monitor and analyze compliance-related data against industry standards, such as Basel III and GDPR for up-to-date reports. For example, Jira/Salesforce can be seamlessly connected with Agentspace applications through prebuilt connectors, and processes can be tracked to ensure proper compliance with policy
• Timely report generation and submission: AI agents can help automate the process of preparing and submitting Basel III liquidity reports, capital adequacy reports, and GDPR compliance documents, saving time and improving efficiency.
• Audit-ready reporting: Integrating prebuilt 2P and 3P connectors with enterprise solutions ensures every data point, action, and process is tracked, so organizations are always prepared for external audits. These comprehensive audit trails also provide much-needed transparency, thereby reducing the risk of penalties due to non-compliance.

Automating regulatory reporting helps retail banks reduce manual effort, cut down on compliance costs, and meet reporting deadlines more efficiently. Real-time monitoring and built-in validation minimize risk exposure while keeping pace with evolving regulations. In addition, the process becomes fully traceable and audit-ready by design.

Use case 2: Integrated demand forecasting & inventory management in retail

With fluctuating market conditions, changing consumer demands, and growing competition, retailers are finding new opportunities to improve operations by leveraging AI. According to a 2024 Deloitte report, 6 in 10 retail buyers in the US said that AI-enabled tools enhanced demand forecasting and inventory management. As expectations for hyperpersonalized experiences and seamless omnichannel shopping grow, AI can help retailers remain agile and respond effectively.

Traditional demand forecasting in retail is often based solely on historical sales data and fails to account for external factors such as weather, economic conditions, or cultural trends that could impact consumer behavior. As a result, retailers can face challenges managing inventory across both online and brick-and-mortar stores, leading to overstocking, stockouts, or lost sales. Here’s how AI agents can help:

• External factor integration: In addition to integrating data sources spread across the business and every touchpoint, AI agents also enable integration of external data sources such as weather forecasts, local events, trends on social media platforms, etc. This provides retailers with a holistic view of inventory and demand, and helps proactively adjust inventory levels based on real-time external conditions.
• AI-driven demand forecasting: Machine learning algorithms analyze historical sales data, customer preferences, weather patterns, economic conditions, and social media trends to predict demand with high accuracy across multiple product categories and geographic regions.
• Real-time inventory optimization: AI agents help retailers track inventory across both online and offline channels and automatically adjust stock levels based on demand forecasts. For example, if a popular product is selling faster than expected in an online store, agents can trigger automatic inventory replenishment from physical stores or external suppliers. They also enable cross-channel inventory synchronization so products are available where customers want to buy them.

With AI-driven demand forecasting enhancing forecast accuracy, retailers can reduce instances of stockouts and overstocking. This optimization lowers storage and stockholding costs and improves customer satisfaction, ensuring the right products are available at the right time. Real-time data collection and analysis help retailers make faster, more informed decisions, boosting agility and driving better business performance.

In summary

Any analytical solution is only as strong as the underlying data. That’s why every large-scale analytics transformation must begin with a robust data infrastructure and quality control. As businesses adopt large language models and Agentic frameworks, the focus shifts to ease of adoption and driving measurable outcomes at scale. To remain competitive, companies must automate complex processes and connect fragmented data sources. Platforms like Agentspace, with its multi-agent architecture, help facilitate efficient data flow, adaptive learning, and improved decision-making.

Governance is crucial throughout the deployment, operation, and scaling of AI agents. A structured approach that combines ‘human-in-the-loop’ oversight and clear operational guardrails ensures integrity and compliance of agents and agentic frameworks, aligning them with organizational objectives while maintaining ethical standards.

References

https://www.pwc.com/us/en/services/consulting/cybersecurity-risk-regulatory/library/privacy-reset.html
https://www2.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html

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Importance of commercial waste management

The commercial waste management lifecycle comprises collection, transportation, and disposal. Ensuring that all the waste materials are properly handled throughout the process is a matter of compliance. After all, multiple environmental regulations dictate how waste management protocols should be implemented and monitored. Instituting the right waste management guidelines also helps companies fulfill their ethical and legal responsibility of maintaining proper health and safety standards at the workplace.

For instance, all the waste materials generated in commercial buildings are stored in bins placed at strategic locations. If companies do not utilize them effectively, it will lead to bin overflows causing severe financial, reputational, and legal repercussions.

Impact of data analytics on commercial waste management

Data analytics eliminates compliance issues that stem from overflowing bins by bridging any operational gaps. In addition, it provides the precise know-how for creating intelligent waste management workflows. With high-quality video cameras integrated into the chassis of their waste collection trucks, image-based analytics can be captured and shared through a cloud-hosted platform for real-time visual detection. From these, data insights can be extracted to evaluate when the trash bins are getting filled and schedule the collection to minimize transportation, fuel, and labor expenses. They can also determine the right collection frequency, streamline collection routes, and optimize vehicle loads.

By monitoring real-time data from the video cameras, the flow of waste in each bin can be managed promptly to avoid compliance-related repercussions. The trucks also receive real-time data on the location of empty bins, which helps them chart optimal routes and be more fuel-conscious.

Ultimately, leveraging sophisticated data analytics helps build a leaner and greener waste management system. In addition, it can improve operational efficiency while taking an uncompromising stance on environmental and health sustainability.

Tiger Analytics’ waste management modeling use case for a large manufacturer

Overflowing bins are a severe impediment in the waste management process as they increase the time required to process the waste. Waste collection trucks will have to spend more time than budgeted for ensuring that they handle overflowing bins effectively – without any spillage in and around the premises. It is also difficult for them to complete their trips on time. When dealing with commercial bins, the situation is even more complicated. The size and contents of the commercial bins vary based on the unique waste disposal requirements of businesses.

Recently, Tiger Analytics worked with a leading waste management company to harness advanced data analytics to improve compliance concerning commercial waste management.

Previously, the client had to record videos of the waste pick up process and send them for manual review. The videos were used to identify the commercial establishments that did not follow the prescribed norms on how much waste could be stored in a bin. However, their video review process was inefficient and tedious.

When the pick up takes place, the manual reviewer is expected to watch hours of video clips and images captured by each truck to determine violators. Thus, there was an uncompromising need for accuracy since overflowing bins led to compliance violations and potential penalties.

Tiger Analytics developed a solution that leveraged video analytics to help determine whether a particular bin in an image was overflowing or not. Using cutting-edge deep learning algorithms, the solution enabled a high level of accuracy and eliminated all activities related to the manual video review and the associated costs.

Tiger Analytics’ solution was based on a new data classification algorithm that increased the efficiency of the waste collection trucks. Based on the sensor data collected from the chassis, we empowered the client to predict the collection time when the truck was five seconds away from being in the vicinity of a bin. Furthermore, with advanced monitoring analytics, we reduced the duration of the review process from 10 hours to 1.5 hours, which boosted workforce efficiency too.

As a result, the client could effortlessly de-risk their waste management approach and prevent overflow in commercial bins. Some of the business benefits of our solution were:

• More operational efficiency by streamlining how pickups are scheduled
• Smarter asset management through increased fuel efficiency and reduced vehicle running costs
• Improved workforce productivity – with accelerated critical processes like reviewing videos to confirm the pickup
• Quick risk mitigation of any overflow negligence that leads to compliance violations

Conclusion

New avenues of leveraging advanced analytics continue to pave the way for eco-conscious and sustainable business practices. Especially in a highly regulated sector like commercial waste management, it provides the much-needed accuracy, convenience, and speed to strengthen day-to-day operations and prevent compliance issues.

Day by day, commercial waste management is growing into a more significant catalyst for societal progress. As mentioned earlier, more companies are becoming mindful of their impact on the environment. In addition, the extent of infrastructure development has taken its toll – thereby exponentially increasing the need to optimize waste disposal and collection methods. Either way, data provides a valuable understanding of how it should be done. 

This article was first published in Analytics India Magazine.

The post Waste No More: Making a Difference with Tiger Analytics’ Data-Driven Solution for a Greener Tomorrow appeared first on Tiger Analytics.