GenAI Opportunities (across the Value Chain) (PowerPoint PPTX Slide Deck)

BENEFITS OF THIS POWERPOINT DOCUMENT

1. Provides over 100+ GenAI opportunities across the Value Chain, which can drive significant revenue enhancements, productivity gains, and cost savings.
2. Allows you to prioritize your GenAI initiatives by corporate function or formulate a cross-functional, holistic AI Strategy.
3. Lay the foundation to your Digital Transformation Program or AI Strategy with targeted GenAI initiatives.

ARTIFICIAL INTELLIGENCE PPT DESCRIPTION

This product (GenAI Opportunities [across the Value Chain]) is a 95-slide PPT PowerPoint presentation slide deck (PPTX), which you can download immediately upon purchase.

Generative Artificial Intelligence (GenAI) refers to foundation models that create new content, code, analyses, and decisions based on large-scale training data. Unlike traditional automation and analytics, GenAI operates as a reasoning and production layer embedded directly into daily workflows, knowledge processes, and decision systems.

GenAI has moved from experimentation to everyday operations across industries—customer, supply chain, finance, and technology. Organizations are no longer testing isolated tools. They are redesigning how work gets done, how insights are generated, and how value is delivered at scale.

Organizations that adopt GenAI systematically are widening performance gaps in speed, cost, and experience. In McKinsey's 2025 State of AI, 71% of respondents say their organizations regularly use GenAI in at least one function, up sharply from early 2024. This rapid diffusion shifts the focus from adoption to disciplined execution and measurable returns.

This pervasiveness makes structured, reusable GenAI frameworks essential for value capture at scale. Without clear prioritization, governance, and integration into core processes, most deployments remain productivity tools rather than enterprise performance drivers.

Organizations that embed GenAI into core workflows outperform peers. McKinsey finds that top economic performers from AI generate more than 3x the value of average adopters. The opportunity is no longer experimentation. It is systematic execution at scale.

This PPT presentation provides a catalog of 100+ GenAI opportunities. These GenAI opportunities are categorized by the 9 functional activities of the Michael Porter Value Chain:

1. Inbound Logistics
2. Operations
3. Outbound Logistics
4. Marketing & Sales
5. Service
6. Firm Infrastructure
7. Human Resource Management
8. Technology
9. Procurement

Mapping opportunities to the classic Value Chain ensures every function sees relevant, actionable use cases and enables cross-functional prioritization tied directly to business outcomes.

This PowerPoint is organized around these 9 functions. For each function, we delineate a series of GenAI opportunities, including clear descriptions, examples, enabling technologies, business owners, potential financial and operational impact, and realistic deployment horizons.

ARTIFICIAL INTELLIGENCE PPT SLIDES

GenAI Deployment Opportunities Mapped to Porter Value Chain Functions

This PPT slide presents a structured overview of over 100 GenAI deployment opportunities within an organization, aligned to the nine functions of the Porter Value Chain. It emphasizes how GenAI can be integrated into core support and primary functions, highlighting specific use cases and potential impacts. The support functions include firm infrastructure, human resource management, technology, and procurement, each with tailored GenAI applications like drafting narratives, creating runbooks, or developing RFx packs. The primary functions—such as inbound logistics, operations, outbound logistics, marketing, sales, and service—are mapped with examples of how GenAI can streamline processes, improve communication, or generate content.

The visual layout uses color coding to differentiate between support functions (gray) and primary functions (blue), making it clear where GenAI can add value. Each function has associated descriptions that specify what GenAI can do, such as writing job descriptions, helping with supplier outreach, or powering self-service portals. These descriptions suggest a focus on automation, efficiency, and better decision-making. The slide also hints at the importance of quantifying impact, specific AI technologies, and timelines for implementation, although these details are not fully visible.

Overall, the slide functions as a comprehensive map for organizations to identify where GenAI can be deployed across their operations. It underscores the broad applicability of AI, from strategic planning to day-to-day activities, and encourages a systematic approach to capturing value. For decision-makers, it offers a clear framework to prioritize initiatives, allocate resources, and accelerate AI adoption in critical business functions.

AI Opportunities in Inbound Logistics: Communication and Discrepancy Management

This PPT slide outlines 16 identified opportunities for deploying AI within inbound logistics activities, focusing on areas like predictive ETAs, dock optimization, and vision-enabled receiving. The content is structured around 2 specific use cases: Supplier Communications Co-Pilot and ASN Discrepancy Explainer. Each case includes a clear description of the business objective, the typical users involved, an example of how the AI solution functions, its potential impact, and the expected timeframe for implementation.

The Supplier Communications Co-Pilot automates the review of inbound supplier emails, portals, and attachments, generating clear replies, RFIs, and follow-ups based on approved playbooks. It aims to reduce cycle times and errors in supplier communication, with a projected email turnaround improvement of 30-50% and fewer miscommunications. The technology stack includes response drafting, retrieval-augmented generation, NLP, and human-in-the-loop processes, with a deployment timeline of 4 to 6 weeks.

The ASN Discrepancy Explainer automates the detection of mismatches between ASN data and historical receipts. It provides root cause analysis and suggests resolution steps, improving first-time receipt accuracy. The tool targets inventory control, receiving, and supplier management, with an expected reduction of 25-40% in recurring discrepancies. Its development timeline is 6 to eight weeks, emphasizing rapid deployment to address common issues in inbound logistics.

Overall, the slide emphasizes how AI can streamline communication and discrepancy resolution, leading to faster, more accurate inbound logistics processes. The focus on specific use cases, clear examples, and realistic timeframes makes it a practical guide for companies seeking to leverage AI in their supply chain operations.

Identification of 19 AI Deployment Opportunities in Firm Infrastructure

This PPT slide outlines 19 identified opportunities for deploying AI within firm infrastructure activities, specifically targeting areas like finance, legal, risk, real estate, tax, and ESG. It categorizes these opportunities into 2 main initiatives: a continuous close narrative and variance explainer, and a board pack and CEO brief co-pilot. Each initiative is described with its business objective, typical users, and an example of its application.

The first initiative aims to automate and streamline the close process, reducing manual effort and speeding up close cycles by 25-40%. It leverages AI technologies such as generative narrative drafting, retrieval-augmented generation, and document AI to produce daily or weekly close narratives. The timeline for implementation is roughly 4 to 6 weeks. The second initiative focuses on improving the speed and consistency of board and executive reporting, generating monthly memos with reconciled metrics and blurb summaries. This effort also takes about 4 to 6 weeks and uses AI to produce fewer restatements and faster prep times.

Overall, the slide emphasizes how AI can significantly cut down manual effort and improve reporting accuracy in core infrastructure functions. It highlights specific use cases, technological enablers, and realistic timeframes, making it clear that these opportunities are actionable and measurable. The framework is based on a structured approach, mapping AI tools to business needs, which can serve as a blueprint for other similar initiatives. This presentation offers a practical view of how AI deployment can transform routine, but critical activities, delivering tangible efficiency gains.

Operational AI Deployment Opportunities and Expected Impact

This PPT slide presents a structured overview of 20 identified opportunities for AI deployment within operations, focusing on areas like yield optimization, scheduling, quality control, energy management, and safety. It categorizes these opportunities into specific use cases, each with defined business objectives, typical users, and illustrative examples. For instance, the "Frontline SOP & Work Instruction Assistant" aims to reduce errors and ramp time by providing step-by-step guidance, mainly used by frontline associates, supervisors, and L&D teams. An example highlights how an associate calibrates a labeler, leading to faster training and fewer SOP-related defects.

The slide also details the potential impact of these opportunities, with estimates on improvements such as 15-30% fewer defects and 20-40% faster training or repair times. The timeframe for realizing these benefits ranges from 4 to 6 weeks, indicating a relatively quick deployment cycle for these AI solutions. Technologies like GenAI, Retrieval-augmented generation, NLP, and human-in-the-loop systems are identified as enablers for these use cases, emphasizing the importance of advanced AI tools in operational efficiency.

Another key opportunity, "Troubleshooting Co-Pilot for Lines & Stations," aims to interpret error codes and sensor data to generate troubleshooting steps. This use case is targeted at operators, maintenance techs, and line leads, with the goal of reducing mean time to repair and minimizing downtime. The example describes how the co-pilot suggests safe resets and restores flow without requiring a tech roll, again with a 4 to six-week implementation window. Overall, the slide offers a clear view of how AI can be systematically applied to improve operational performance, with specific use cases, expected benefits, and technology enablers laid out for quick wins and strategic deployment.

AI Deployment Opportunities in Human Resource Management Processes

This PPT slide presents a structured overview of 15 identified AI deployment opportunities within the Human Resource Management function. It categorizes these opportunities into specific initiatives, each with a clear description, target business objectives, intended users, and example references. The focus appears to be on practical applications that can improve efficiency, response times, and fairness in HR activities.

Two detailed examples are provided: Talent Sourcing Outreach Co-Pilot and JD Rewriter & Competency Mapper. The Talent Sourcing Co-Pilot aims to personalize outreach with AI-driven messaging, targeting recruiters, sourcing teams, and hiring managers. It emphasizes faster candidate response times—3 to 5 weeks—and higher reply rates, with a technology stack including GenAI, RAG, and bias filters. The JD Rewriter focuses on improving applicant fairness and resume quality, with similar timelines and a structured rubric approach, utilizing HRBPs, TA, and hiring managers as users.

The slide also indicates that these initiatives are part of a broader framework called GenAI Opportunities across the Value Chain, created by leading consulting firms. It suggests that these opportunities are actionable and measurable, with specific timeframes for implementation. The overall message is that AI can significantly impact HR processes by automating, personalizing, and improving decision-making, ultimately driving better outcomes in sourcing, hiring, and retention. The detailed examples serve as templates for how AI can be integrated into existing workflows to produce tangible results within a short period.

This slide provides a comprehensive view of targeted AI applications in HR, emphasizing practical deployment and measurable impact. It signals to executives that AI-driven HR initiatives are not theoretical, but deliver quick wins and scalable improvements across core activities. The emphasis on timelines and specific technologies underlines the urgency and feasibility of adopting these solutions.

AI Deployment Opportunities in Outbound Logistics Activities

This PPT slide outlines 16 identified opportunities for deploying AI within outbound logistics activities, focusing on routing, reliability, packing, and last-mile capacity. It categorizes these opportunities into specific use cases, starting with customer delivery communications and promise-date explanations. Each use case is linked to a clear business objective, such as reducing failed attempts or improving transparency, with examples illustrating potential triggers and outcomes. The potential impact is quantified, showing a 20-40% reduction in "where is my order" contacts for delivery communications, and a 3-6% uplift in checkout conversion for promise-date tools. The estimated timeframes suggest these initiatives can be implemented within 3 to 6 weeks, indicating quick wins for logistics teams.

The slide also emphasizes the role of AI technologies in enabling these opportunities. For customer communication, tools like GenAI for message drafting, RAG (retrieval-augmented generation) with TMS/OMS data, and tone control are highlighted. For promise-date improvements, AI can streamline the explanation process, reduce cancellations, and improve post-purchase experiences. Each opportunity is grounded in practical examples, making the case for AI's tangible benefits in operational efficiency and customer satisfaction. The structured layout makes it clear that these opportunities are part of a broader framework, which is supported by a reference to a comprehensive PowerPoint deck.

Overall, the slide presents a strategic view of how AI can be integrated into outbound logistics to address specific pain points and unlock measurable value. It combines concrete examples, potential benefits, and realistic timelines, making it a useful reference for executives planning AI-driven logistics initiatives. The emphasis on quick deployment and targeted use cases suggests a focus on delivering immediate impact while building toward more complex implementations.

Source: Best Practices in Artificial Intelligence, Value Chain Analysis, GenAI PowerPoint Slides: GenAI Opportunities (across the Value Chain) PowerPoint (PPTX) Presentation Slide Deck, LearnPPT Consulting