The GenAI Building Blocks

Use Case Management: How to steer the Generative AI use case portfolio to maximize value

GenAI Building Block: Header
  • Article
  • 8 minute read
  • 12 Dec 2023

Written by Frauke Schleer-van Gellecom, Andreas Odenkirchen, Phil Schäfer, Victoria Reifschneider and Nele Steigerwald. In the rapidly evolving realm of artificial intelligence, the advent of Generative AI has sparked a transformative wave across diverse fields of application. As technology continues its rise, businesses are confronted with a myriad of possibilities and challenges in harnessing the full potential of Generative AI for their organization.

From natural language generation and audio synthesis to image creation and code generation, this article examines the multifaceted impact of Generative AI, shedding light on how businesses can effectively navigate through the landscape of Generative AI applications and manage their Generative AI use case portfolio.

Diversity of Generative AI applications

The benefits and implications of Generative AI for organizations are driven mainly by three levers, elevating tasks and processes significantly.

  • Automation: Generative AI serves as a catalyst for business process automation, streamlining repetitive and low-value tasks, and enhancing operational efficiency. The technology enables organizations to automate workflows, reduce human intervention and minimize errors. Thus, it increases efficiency by freeing up resources for value-adding or strategic tasks and decision-making.
  • Insights: Extracting valuable information and insights is profoundly transformed by Generative AI. Not only is the extraction of valuable insights automated from datasets but also enriched, through deeper root-cause analysis providing valuable insights beyond the possible of human thinking. Furthermore, AI-driven analytics is an indispensable tool for unlocking the hidden potential within complex datasets and accelerating human-led data-driven decision-making and innovation further. As enabler for human analyst Generative AI is boosting insight generation in terms of both speed and quality.
  • User Experience: The success of AI applications hinges on their ability to seamlessly integrate into the user's daily life, minimizing friction and enhancing accessibility, like Chatbots that allow for user-friendly Q&A or integrated natural language querying. The improved usability of services through Generative AI results in higher adoption. User-friendly interfaces and streamlined workflows encourage greater use of systems, ensuring better compliance with policies.

Potential of Generative AI along the value chain

However Generative AI is not merely a technological advancement, it is a catalyst reshaping the very foundation of how businesses operate. Given its vast possibilities, Generative AI is not limited to certain business functions but impacts the entire value chain of an organization.

Finance

  • Generative AI Enabled Reporting: Automate the process of generating reports and comments for financial reports in natural language using Generative AI. It automates data analysis, generates visually appealing reports, and provides real-time insights. Additionally, it can be used to “Chat with your data” to quickly answer questions of the report user in natural language.
  • Generative AI supported Planning: Leverage (Generative) AI and external data like market- or competitive analysis to dynamically provide contextual information, explanations and charts when users select planning objects. It automates the process of offering additional insights, enhancing the planning experience.
  • Processing Automation: Generative AI can automate the matching process by reading invoices, for example, and comparing them to purchase orders and receipts. Additionally, this can be easily integrated into existing systems and workflows.

HR

  • AI enhanced Recruiting: Automate tasks involved in the recruitment process and streamline the hiring process such as job postings, resume screening, candidate sourcing, and scheduling interviews. Further Generative AI can help by formulating job descriptions. 
  • Employee Engagement: Generative AI based tools can analyze employee feedback, engagement data, or social media posts providing insights to enhance workplace satisfaction and productivity and monitor employee´s engagement.
  • Training and Development: Generative AI can create personalized training programs based on individual employee needs, improving skill development and performance.
  • Talent Pooling: Use Generative AI to aggregate talent information and streamline talent matching, enabling organizations to match project requirements with available skills quickly and effectively.

IT

  • Predictive System Monitoring: In IT infrastructure, AI can support in monitoring and optimizing resource allocation and predict equipment failures or performance issues allowing for proactive maintenance and minimizing downtime.
  • Code and Test Case Generation: Generative AI can automatically generate code snippets or entire functions based on high-level descriptions provided by developers or translate code to various programming languages. Utilize Generative AI to derive and write test scenarios based on business descriptions that are given in user stories. 

Procurement

  • Supplier Proposal Analysis: Generative AI technology can help with supplier proposal analysis by automatically generating summaries and analysis of proposals. This can save time and reduce errors, as well as provide an unbiased and objective analysis. 
  • Automated Order Confirmation Process: Use Generative AI to extract and validate relevant information from unstructured or semi-structured data sources such as e-mails and process the data automatically in the ERP system end-to-end to the customer. 
  • RFX Creation: Generative AI can transform the RFX creation process by automating document generation, optimizing content, personalizing documents, and ensuring compliance. This not only improves efficiency but also enhances the overall quality and effectiveness of the procurement process.

R&D

  • Product Innovation: Generative AI can analyze market trends, customer preferences, and historical data to suggest innovative product ideas and features, aiding R&D (Research and Development) teams in the ideation phase.
  • Simulation and Modeling: AI can assist in simulating and modeling complex scenarios, helping researchers test hypotheses, optimize designs, and accelerate the development process.
  • Automated Experimentation: Generative AI can design and conduct experiments autonomously, accelerating the R&D cycle and improving the efficiency of the research process.

Supply Chain

  • Supplier Performance & Risk Evaluation: Assess the supplier´s performance and identify potential risks and their impact with the help of Generative AI through analyzing and summarizing large amounts of data efficiently. This also helps in monitoring the supplier's compliance with regulations and environmental standards. 
  • Warehouse Operations Optimization: Optimize warehouse layout, inventory placement and workforce scheduling for maximum efficiency through balancing inventory levels and improving the order fulfillment process and adapting dynamically to changing conditions
  • Deviation Reporting: Generative AI can be used to streamline the process of deviation reporting within its warehouses. With Generative AI the system continuously scans and analyzes images and data from warehouse cameras and inventory management systems. It cross-references this data with expected inventory levels and generates real-time deviation reports.

Manufacturing

  • Maintenance Assistant: Generative AI can scan manuals efficiently so that the maintenance worker can ask what he should know related to a certain problem and the solution provides him a summary of the tasks that should help based on what it found in the manuals.
  • Report Creation: Generative AI can be used to create reports such as shift reports to ensure smooth shift hand over or quality incident reports for documentation of quality reason, in a structured and efficient way.
  • Workforce Scheduling Optimization: Generative AI can help in creating the ideal production or maintenance schedule considering different parameters such as workforce availability and skills, material and equipment availability, bottlenecks leading to improved production output and reduced costs.

Marketing & Sales

  • Automated Marketing Content Generation: Generative AI can assist in generating creative content for marketing campaigns, including images, videos, newsletters and social media posts and optimizing content for specific target audiences. 
  • Customer Segmentation: Employ data-driven segmentation and analysis to analyze customer data, encompassing purchase history, browsing behavior, and demographics. Use these insights to deliver personalized marketing campaigns and product recommendations.
  • Customer Retention: Analyze customer behavior, transaction data and engagement metrics using Generative AI, enabling organizations to provide early warnings for potential issues and leverage customer preferences to develop proactive customer retention strategies.

Customer Service

  • Virtual Agents: Generative AI powers chatbots and virtual assistants that can handle routine customer inquiries, providing immediate responses and freeing up human agents for more complex issues.
  • Customer Pattern Analysis: Leverage Generative AI to recognize patterns in customer interactions and inquiries, enabling a proactive approach in addressing customer needs and identifying areas for improvement in customer satisfaction. 

Considering the multitude and broadness of potential fields of application, it is not surprising that organizations are struggling to navigate themselves through the possibilities Generative AI has to offer. In addition, the majority of organizations are seeing the impact AI has and have understood the need for a comprehensive AI strategy and transformation roadmap. However, given common challenges, such as limited IT and innovation budgets, they rather choose to start small with selected use cases to prove the value Generative AI has before deciding about larger investments. Therefore, actively steering the portfolio to maximize ROI is critical to the success of Generative AI in organizations. Some of the key questions and challenges we see currently in the market are the following:

  • Which value-add does Generative AI offer for my organization?
  • Which Generative AI use cases are the right ones for my organization to quickly start, learn and scale?
  • How can we create effective lighthouse applications of Generative AI in the organization to kickstart the broader transformation journey?

Use Case Management for Generative AI

In this context use case management plays a pivotal role in guiding organizations through the process of applying modern technologies, like Generative AI and transforming innovative ideas into tangible solutions securing companies’ competitive advantage and creating tangible business value.

Typically use case management can be described with a funnel following a 5-step approach

Ideation

This marks the initial phase where use case ideas are collected, and potential use cases are identified. This stage involves brainstorming sessions and collaborative efforts to capture a diverse range of ideas.

As one of the common asks of businesses is to get inspired by the art of possible and domain- or industry-specific fields of application, we have developed the Applied AI Use Case Compass. The use case compass is an intuitive and interactive web application providing an overview of different use cases clustered by business domain, function, industry, and technology. It contains more than 200 use cases across domains and industries and thus is a great source of inspiration to discover new opportunities for using AI for organizations and is continuously growing.

Applied AI Use Case Compass

Evaluation

In the next step the individual use cases must be evaluated. First further concretion of the use cases is done by specifying business problems the use cases aim to solve, their objectives and requirements. Furthermore, primary users and stakeholders are identified to understand the audience and their needs. Once the use case is specified, the potential benefits and effort is assessed. Therefore, the use cases should be evaluated based on two dimensions.

  1. Estimated Business Impact
  2. Estimated Implementation Complexity

Creating a matrix based on those two dimensions and adding the use cases helps in selecting and prioritizing use cases.

Prioritization

Use case prioritization involves assessing and ranking use cases based on their impact and feasibility. Stakeholder input is crucial in determining priorities, ensuring alignment with organizational goals. The prioritization criteria help allocate resources efficiently and guide development efforts. The goal is to narrow down on a strategic Generative AI use case portfolio, including a handful of use cases to be implemented within the next 6 months.

Prototype

This step consists of an iterative process where a simplified, preliminary version of a Generative AI use case is created to visualize and test its design and functionality. Those Prototypes can range from a drawn mock-up to a simple click-dummy or small proof-of-concept implementation in which also Generative AI can be applied to create the same. Prototypes allow stakeholders to provide feedback early in the development cycle, helping to identify and address potential issues, as well as testing the feasibility and effectiveness of Generative AI solutions practically. This iterative approach helps refine requirements, improve user experience and reduce the risk of costly errors later in the development process.

Development & Operations

In this phase the actual AI product that addresses the use case is implemented. Depending on the nature of the use case this may include the development or fine-tuning of a foundational or domain-specific AI model. This involves coding algorithms, preprocessing data and optimizing model parameters. Continuous monitoring and testing are crucial to address issues like bias and robustness. Generative AI development may involve using machine learning frameworks, neural network architectures and other AI technologies. With the capabilities of Generative AI tasks such as coding, testing or documenting might also be accelerated with it. Collaboration between data scientists, domain experts and software engineers is essential to ensure that the Generative AI solution meets performance and reliability standards. Once the solution is ready to deploy, it needs to be integrated into existing systems and a monitoring mechanism to track model performance over time is required. DevOps practices specific to AI, such as MLOps (Machine Learning Operations), play a critical role in automating the deployment and maintenance of AI models and ensure continuous improvement.

Best practices for Use Case Management in Generative AI

As organizations navigate through the dynamic landscape of use case implementation, it becomes imperative to anchor their efforts in a framework that not only streamlines processes but also ensures a holistic and purposeful approach. The following success factors will help businesses in implementing the right Generative AI applications according to their individual circumstances and roadmap.

  • Alignment with overarching strategy and business objectives: Businesses must meticulously align their initiatives with broader business objectives. This involves a careful examination of how each use case contributes to the overarching goals and vision of the organization. By establishing this alignment, companies can ensure that their resources and efforts are directed towards initiatives that have a meaningful impact on the bottom line.
  • The Significance of Domain / Industry Specificity: While ChatGPT and similar generalized Generative AI tools are valuable, especially to leverage general work productivity, it is crucial to recognize that the highest impact Generative AI use cases often emerge within specific domains. Domain specificity tailors AI solutions to the unique challenges and opportunities of a particular industry, sector or domain. Using the right technology and models to fit seamlessly within the unique requirements of the business domain enhances the likelihood of success and sustainability.
  • Continuously improve and adapt along the way: Harnessing the full power of Generative AI requires a commitment to continuous learning and adaptation. The technology itself undergoes rapid iterations, and its application landscapes are subject to dynamic shifts. Therefore, a constant pursuit of learning to stay abreast of advancements and refinements is necessary and adapting to emerging trends, refining strategies, and incorporating feedback from real-world implementations are essential elements of a successful Generative AI deployment. Moreover, the ability to pivot in response to unforeseen challenges and ethical considerations is crucial for maintaining the relevance and responsible use of Generative AI.
  • Emphasize about people enablement and upskilling: The intersection of human expertise and Generative AI capabilities opens avenues for groundbreaking solutions across diverse industries. Investing in people enablement ensures that professionals across disciplines, from content creators to developers, possess the foundational knowledge to collaborate effectively with Generative AI models. Upskilling initiatives further enable individuals to navigate the evolving technical landscape, enhancing their ability to integrate AI technologies seamlessly into their workflows.

Key take aways and how PwC can support your journey

Generative AI's impact on diverse business functions is profound, ushering in automation, providing valuable insights, and enhancing user experiences. Generative AI is not confined to specific business functions; rather, it permeates the entire value chain of organizations. This broad applicability is evident in exemplary use cases across finance, HR (Human Resources), IT, procurement, R&D (Research and Development), supply chain, manufacturing, marketing, sales, and customer service. From automating financial report comments to optimizing warehouse operations, Generative AI emerges as a catalyst for innovation and efficiency. However, navigating the vast landscape of Generative AI use cases and prioritizing them for your strategic investments remains a challenge for businesses and their AI leaders.

With our deep industry and functional expertise, we at PwC are committed to providing strategic but actionable guidance to our clients. We invite organizations to engage with us through interactive workshops, where we can collaboratively identify the next best Generative AI use cases specific to their unique goals and challenges, and jointly shape the transformation journey from strategy through execution.

Follow us

Contact us

Franz Steuer

Franz Steuer

Partner, PwC Germany

Andreas Hufenstuhl

Andreas Hufenstuhl

Partner, Data & AI Use Cases, PwC Germany

Tel: +49 1516 4324486

Christine Flath

Christine Flath

Partnerin, PwC Germany

Andreas  Odenkirchen

Andreas Odenkirchen

Director, Data & Analytics, Technology Consulting, PwC Germany

Tel: +49 151 15535019

Phil Schäfer

Senior Manager, PwC Germany

Victoria Reifschneider

Victoria Reifschneider

Manager, PwC Germany

Tel: +49 160 2028899

Nele Steigerwald

Nele Steigerwald

Senior Associate, PwC Germany

Tel: +49 1511 1696270

Hide