AI Use cases

Streamlined grant application

Opportunity: State governments often face the challenge of accurately identifying suitable federal health grant opportunities and creating compelling funding proposals. The traditional approach to grant search and application drafting is resource-intensive and time-consuming, with the potential for inaccuracies and missed opportunities.

Solution: Leveraging large language models (LLMs) simplifies the grant application process, offering a two-fold solution. Firstly, by extracting key concepts from grant opportunities, matching them with relevant health factors based on generated relevance scores. Secondly, by automatically generating draft grant applications using function-specific models and prompts, increasing output quality and reducing error.

Outcome: This solution can significantly streamline the grant application process, saving both time and resources. It ensures the accuracy and consistency of applications, provides customized proposal content, and garners insights from past applications to inform future strategies. Uniquely, this model also enhances the state government's capacity to secure funding and address community needs, fostering improved community services and welfare.

Harmonized road rules

Opportunity: Automated Driving Systems (ADS) must comply with fragmented and often disparate traffic rules and regulations ("rules of the road") across different jurisdictions. Extracting, understanding and formatting these different, non-machine-readable rules for a specific geographic area and a specific driving scenario can be a labor-intensive and error-prone task.

Solution: An LLM can be employed to scrape traffic rules and regulations from various sources across different jurisdictions. This GenAI system can then answer natural language queries about the road rules, identify the relevant rules, and format them for easier interpretation and application for the given driving scenario.

Outcome: This AI-powered system can significantly streamline the process of extracting road rules, saving both time and resources. It ensures the accuracy and consistency of rules understanding, significantly reducing the chances for rule misinterpretations. As a result, it allows a faster and higher volume coverage of rule scouting across more jurisdictions for different driving scenarios, enhancing road safety across varying geographic locations and driving conditions.

Efficient contract management

Opportunity: Companies often rely on manual processes for contract creation, review and approval. This method raises the risk of overlooking important contract details like compliance mandates, commitments, potential discounts or hidden risks.

Solution: An advanced supplier and legal contract analysis tool powered by GenAI can aid every stage of the contract management lifecycle. This tool allows for quick validation and extraction of key contract data and entities, asks questions to the contract document for a generative summarization of responses, validates a contract against a commercial and legal checklist, and compares contract clauses for risk identification.

Outcome: This contract management solution should significantly reduce turnaround times in the contract management lifecycle. It supports superior decision making with valuable insights and suggested negotiation strategies. It provides improved visibility with clear and actionable risk data, identifies new opportunities through key contract information extraction, and offers upfront risk mitigation plans and negotiation tips.

Intelligent financial commentary

Opportunity: Companies often find the process of identifying outliers and drivers from financial reports to be time-consuming. Manual queries and case-by-case investigations by experts make it difficult to scale this process.

Solution: Introducing an AI-enabled solution can streamline this process by automating the creation of commentary on standard and non-standard financial reports. Firstly, it could generate pre-commentary on standard reports for controllers, identifying outliers, drivers and other notable trends. Then it could tackle non-standard reports, retrieving and summarizing data as required. Lastly, it could perform deep root cause analysis, identifying products likely to decline over the next six months, and providing reasoning for this prediction.

Outcome: An AI-enabled solution transforms the financial commentary process by making it more efficient, scalable and accurate. It allows for deep, data-driven insights into company performance, supports strategic decision-making and reduces the time spent on manual data analysis. This could lead to more proactive business strategies and improved financial health.

Improved receivables management

Opportunity: Many organizations struggle to efficiently manage their accounts receivable collections. This affects essential metrics such as collections rate, Days Sales Outstanding (DSO), and the receivable period. Lack of customer profiling, analytics regarding customer payment history and transparency about effective time utilization make this process even more challenging.

Solution: Implementing an Accounts Receivables (AR) Collection Assistant powered by AI can streamline this process. The AI assistant uses machine learning to prioritize accounts, identify "at risk” customers, and recommend the "next best action.” Integrated with ERP applications, it provides a unified platform for agents, enhancing efficiency. Voice-enabled features facilitate the follow-up process, making it more effective and user-friendly.

Outcome: The AR Collection Assistant can significantly improve collections, resulting in a 30% improvement in DSO, a 40% improvement in productivity and a 22% decrease in the receivable period. It provides a more straightforward and intuitive view of the customer’s payment patterns and past interactions. This ultimately should lead to more proactive collections management, better cash flows and higher customer satisfaction.

Efficient deal sourcing

Opportunity: Private equity firms often find the deal sourcing and diligence process repetitive, with significant amounts of manual tasks involved. This ranges from deal origination, analysis conduction and target valuation to risk assessment — all requiring labor-intensive work that impacts the overall efficiency of the firm.

Solution: Integrating GenAI into the process can lead to full or partial automation of various steps, thereby reducing labor-intensive tasks. This automates properties such as data sourcing for deal origination, preliminary due diligence processing of public information, drafting of reports, performing pre-defined and risk analyses, modeling based on a business plan, and drafting legal documents with limited fine-tuning.

Outcome: The implementation leads to major diligence efficiencies such as increased target insights, faster time from sourcing to deal closure, the potential for lower deal-related expenses, and the scaling of capabilities. Overall, it greatly enhances the efficiency and speed of deal sourcing and closing, giving firms a significant competitive edge.

Optimized transport plans

Opportunity: Railway companies often face complex challenges in planning their transport schedules effectively. The traditional model struggles to incorporate real-time variables such as passenger flow, weather conditions, maintenance schedules, crew availability, rolling stock use, and potential service delays, which could impact operational efficiency.

Solution: By using a GenAI tool, it is possible to create a dynamic transport plan that ingests real-time variables, such as sudden fluctuation in passenger numbers, unscheduled maintenance, or adverse weather effects. The AI tool processes this current data and generates an optimal plan that most efficiently uses resources while reducing service disruptions.

Outcome: Implementing AI in transport planning not only streamlines the operation but makes it more adaptable to the fast-changing realities of a railway service. It could lead to better resource allocation, minimizing idle time, and synchronizing various components that make up the service. This proactive approach can also increase the company's ability to predict and manage potential disruptions, leading to enhanced reliability, improved customer satisfaction and significant market differentiation.