7
Use cases

Discover through successful business examples, how AI can ignite innovation, unlock efficiencies and transform businesses.

From challenges to opportunities: How EY and AI work hand in hand

7.

Use cases

When it comes to successfully implementing AI applications into the everyday work of our clients and helping to build a better working world, we focus on three pillars: building confidence, creating value and augmenting people ́s potential. The following section delves into these example use cases to discover how this technology can ignite innovation, unlock efficiencies and transform businesses.

Augment people

Augmented customer service

Opportunity: Ensuring the satisfaction of their many customers is crucial for telecommunications companies, especially in an area where customers change providers very quickly. Qualified and, above all, fast support is of the utmost importance from the companies’ point of view.

Solution: Setting up and deploying AI programs for customer support can improve current solutions. They will augment the work of human experts and help them help their customers become more efficient – and thereby bring their experience and satisfaction with the provided service to the next level, keeping the customer with the company.

Outcome: AI applications represent a completely new and still little-used link in a chain of solutions that ultimately can improve customer satisfaction through speed and expertise. Walking this path together with the customer leads to learning effects on both sides - and for the fact that we stay one step ahead with the customer through the newly gained knowledge.

Augmented knowledge

Opportunity: Banking employees face challenges in keeping up with frequently changing regulations, understanding complex financial products, ensuring transaction security and efficiently serving customers amidst increasing cyber threats and diverse financial needs.

Solution: An AI-enabled knowledge base trained on tailored banking data can support employees to surface accurate information quickly. The AI-enabled knowledge base can offer real-time details on current regulations, intricate product specifications and up-to-date security protocols.

Outcome: Enhance regulatory compliance, enhance consistency in product information dissemination, expedite customer service and fortify security measures, ultimately leading to increased customer satisfaction, reduced risks and operational efficiencies.

AI-enabled knowledge base trained on tailored banking data can support employees to surface accurate information quickly.

Streamlined health care operations

Opportunity: Health care organizations grapple with time-consuming, repeatable tasks. They need collaborative workspaces for data scientists and a streamlined model deployment process.

Solution: Setting up a center for automation with GenAI and machine learning can help. This includes creating a collaboration-friendly workspace, an easily accessible feature store, a continuous integration and continuous delivery-style MLOps pipeline and integrating a chatbot.

Outcome: This automation center can enhance efficiency, improve user experience by leveraging data insights, increase process transparency and deliver improved health outcomes.

Enhancements in learning

Opportunity: Financial institutions often grapple with creating engaging, timely and effective learning content for advisors due to the rapid evolution of the sector.

Solution: Build AI capabilities, such as a conversational assistant, to craft specialized learning content. This will work in sync with existing chatbot systems using platforms.

Outcome: The GenAI solution can aid in creating a robust learning framework and increase time efficiency. It can also set the stage for future innovations like powering an internal search engine and knowledge management platforms, meeting regulatory documentation requirements and improving productivity.

Build confidence

Compliance with AI regulations

Opportunity: Automotive companies developing AI systems often face the challenge of navigating complex and ever-evolving regulations, such as the EU AI Act. Managing the reputational implications of underperforming AI models, particularly in safety-critical applications, is crucial. Assessing their readiness for these regulations and shaping their AI risk management program to comply is a key priority.

Solution: Companies can conduct an AI readiness assessment to ascertain their preparedness for these regulations. The assessment can include a thorough evaluation of their policies and procedures, accountability structures, training and awareness programs, ethical AI foundations, and management of third-party risk.

Outcome: The AI readiness assessment alerts the company to their progress in identifying and managing AI risk. This evaluation forms a guidepost for future AI governance planning and identifies areas requiring added investment or resource allocation. Regular assessments not only help track progress but foster proactive compliance with regulations, minimizing regulatory and reputational risks.

Regulatory compliance readiness

Opportunity: Oil & Gas sector entities deploying AI systems face new legal obligations due to regulations such as the EU AI Act. Given the regulatory pressure and reputational risks, these organizations need a clear understanding of their preparedness for these regulations.

Solution: Undertaking a Responsible AI readiness assessment provides a measure of an organization’s readiness for these regulations. It evaluates key areas like policies and procedures, governance and accountability, training and awareness, ethical AI foundations, and third-party risk, providing tailored next steps for future actions.

Outcome: The readiness assessment helps organizations quickly gauge their progress in identifying and managing AI risk. It guides AI governance roadmaps, highlights areas requiring additional resources, fosters collaboration and ensures regulatory compliance readiness.

Create value

Agile product design

Opportunity: Retail organizations have a desire to offer a product catalogue that stays at the forefront of consumer preferences and market trends. The traditional process for product development is time-intensive, involving the evaluation of numerous options before commercializing final products.

Solution: Accelerate the design process using a GenAI tool trained on sales and competitive data, customer feedback, and thousands of labeled images of features and designs. Using a series of prompts, AI can augment the designer’s process with suggested hyper-realistic, uniquely styled designs that are inspired by real-time concepts.

Outcome: A GenAI tool has the potential to reduce design cycle time, increase customer satisfaction, and generate cost savings through AI-driven design automation. Additionally, this approach can help achieve market differentiation and speed to market by responding to real-time concepts and generating unique and realistic designs aligned with consumer preferences.

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. Such a tool will allow for quick validation and extraction of key contract data and entities, ask questions to the contract document for a generative summarization of responses, validate a contract against a commercial and legal checklist, and compare contract clauses for risk identification.

Outcome: A 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.

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 process.

Solution: Integrating GenAI into the process can lead to full or partial automation of various steps, thereby reducing labor-intensive tasks. This can automate 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 integration of GenAI can lead 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 can greatly enhance the efficiency and speed of deal sourcing and closing, giving firms a significant competitive edge.

Optimized transport plans

Opportunity: Companies from the transportations and logistics sector 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 can process this current data and generate 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 transportations and logistics. 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.