Artificial Intelligence Opportunities (across the Value Chain (PowerPoint PPTX Slide Deck)

BENEFITS OF THIS POWERPOINT DOCUMENT

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

ARTIFICIAL INTELLIGENCE PPT DESCRIPTION

This product (Artificial Intelligence Opportunities [across the Value Chain) is a 100-slide PPT PowerPoint presentation slide deck (PPTX), which you can download immediately upon purchase.

Artificial Intelligence has moved from experimentation to everyday operations across industries—customer, supply chain, finance, and tech.

Organizations that adopt AI 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 generative AI in at least one function, up sharply from early 2024. This pervasiveness makes structured, reusable playbooks essential for value capture at scale.

There are numerous clear benefits to deploying AI, including:

Drive revenue and margin by targeting high-impact use cases with measurable KPIs.
Reduce cost-to-serve and cycle times through automation and better decisions.
Improve customer and employee experience with faster, more accurate responses.
De-risk scaling via repeatable patterns and governance.

This PPT presentation provides a catalog of 100+ AI opportunities. These 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 options and enables cross-functional prioritization tied directly to business outcomes.

This PowerPoint is organized into these 9 functions. For each function, we delineate a series of RPA opportunities, which includes clear descriptions, examples, AI technologies, business users, potential impact, and deployment horizon.

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

ARTIFICIAL INTELLIGENCE PPT SLIDES

Structured AI Deployment Opportunities in Marketing and Sales

This PPT slide outlines a structured approach to AI deployment opportunities within marketing and sales functions. It identifies 16 specific areas where AI can be applied, focusing on activities like pricing, personalization, recommendations, attribution, and sales co-pilots. The content emphasizes that these opportunities are organized into distinct segments, with detailed descriptions of each segment's objectives, target users, and practical examples.

Two segments are highlighted: "Segmentation & Propensity Modeling" and "Next-Best-Action & Offer Engine." The first aims to refine customer segments and improve media efficiency by leveraging AI to analyze behavioral signals and purchase tendencies. It targets CRM managers, performance marketers, and brand managers, with a potential uplift of 10-20% in targeted campaign conversion and a 5-15% reduction in CPA over a 6 to eight-week timeframe. The second segment focuses on optimizing customer interactions by predicting the best actions to increase conversion while protecting margins. It is aimed at lifecycle marketing, ecommerce, and product managers, with an expected 5-12% revenue increase per visitor and a 10-25% decrease in promo costs over an eight to twelve-week period.

The slide also briefly mentions underlying technologies like machine learning, feature engineering, and bias monitoring that support these AI initiatives. It underscores the importance of governance and model refreshes, suggesting that these are critical for maintaining accuracy and fairness. The overall message is that AI deployment in marketing and sales is not a single effort, but a series of targeted opportunities that can deliver measurable results within a relatively short period. This structured approach helps organizations prioritize and accelerate their AI adoption efforts, ultimately driving better customer engagement and operational efficiency.

AI Deployment Opportunities in Inbound Logistics Operations

This PPT slide outlines 16 specific opportunities for applying AI within inbound logistics activities, emphasizing predictive ETAs, dock optimization, and vision-enabled receiving. It begins by highlighting 2 key areas: supplier ETA prediction and dynamic dock scheduling. Each opportunity is described with a focus on the business objective, typical users, real-world examples, potential benefits, and implementation timelines.

The supplier ETA prediction initiative aims to improve accuracy by leveraging carrier telemetry, historical lane performance, weather, and port congestion data. The goal is to reduce stockouts by 20-40% and cut expediting costs by 2-4%. Technologies like machine learning, anomaly detection, and feature fusion support this effort. The timeline for deployment is 6 to ten weeks, indicating a relatively quick implementation cycle.

Dynamic dock scheduling focuses on optimizing dock door assignments and appointment windows based on predicted ETAs, load profiles, and yard congestion. This reduces dwell time and truck turnaround times, with potential reductions of 15-30% and 10-15%, respectively. The process involves integrating yard management, receiving leads, and transportation data, with an estimated 6 to eight-week deployment period. Both opportunities demonstrate how AI can streamline inbound logistics, improve service levels, and reduce costs through targeted technological interventions.

Overall, the slide presents a structured approach to identifying AI use cases, supported by concrete examples and realistic timeframes. It underscores the importance of leveraging data and advanced analytics to address operational inefficiencies, with a clear focus on measurable outcomes. For executives, these opportunities represent tangible steps toward digital transformation in logistics, with achievable implementation timelines and significant potential benefits.

AI Deployment Opportunities in Outbound Logistics Operations

This PPT slide outlines 16 identified opportunities for deploying AI within outbound logistics activities, specifically focusing on routing, promise-date reliability, packing/load design, and last-mile capacity. It categorizes these opportunities into distinct initiatives, with detailed examples, business objectives, and potential benefits. The first example emphasizes improving shipment ETA prediction and customer alerts by leveraging historical data, real-time telematics, and carrier behavior. This initiative aims to reduce delivery times by 2-4 points OTIF lift and decrease late deliveries by 10-20%, with a timeframe of 6 to ten weeks for implementation.

The second opportunity centers on dynamic route optimization and rescheduling, which involves generating and refining delivery routes based on constraints like time windows, driver hours, and customer priorities. The example highlights how rerouting can reduce delivery times by 8-15% and cut late windows by 10-20%, with a 6 to eight-week window for deployment. The slide emphasizes the importance of AI technologies such as machine learning, anomaly detection, and feature fusion in enabling these improvements.

Overall, the slide presents a structured approach to AI deployment in logistics, with clear examples illustrating how specific technologies can address operational pain points. It underscores the potential for significant efficiency gains and service level improvements within a relatively short period. For decision-makers, this provides a roadmap for prioritizing AI initiatives that deliver measurable results in outbound logistics. The slide also hints at a broader framework, referencing a comprehensive AI opportunity map across the entire value chain, which can serve as a strategic guide for targeted investments.

AI Deployment Opportunities in Human Resource Management Activities

This PPT slide outlines 15 specific opportunities for deploying AI within Human Resource Management activities, focusing on sourcing, hiring, learning, scheduling, and retention. It categorizes these opportunities into 2 main initiatives: Talent Sourcing Co-Pilot and Job Description & Skill Taxonomy Builder. Each initiative is described with a clear business objective, the typical users involved, an example of how it functions in practice, the potential benefits, and the expected timeframe for implementation.

The Talent Sourcing Co-Pilot aims to accelerate candidate sourcing by automating job requirement parsing and internal/external talent pool searches. It targets recruiters, talent sourcers, and hiring managers, with a goal of creating a 20-40% faster slate of candidates within 4 to 6 weeks. Technologies like GenAI, NLP, and bias monitoring are central to this effort. The second opportunity, the Job Description & Skill Taxonomy Builder, focuses on standardizing job descriptions and skill mappings to improve hiring quality and reduce bias. It involves HR business partners, recruiters, and hiring managers, with a similar timeframe of 4 to 6 weeks. The example provided highlights how the system rewrites job descriptions to remove jargon and attach competency-based guides, leading to higher candidate satisfaction and better interview rates.

The slide emphasizes that these initiatives are part of a broader framework of over 100 AI opportunities mapped across the entire value chain. The goal is to leverage AI to streamline HR activities, improve candidate quality, and reduce biases, with quick wins achievable within a month. The bottom banner references a detailed resource for further exploration, positioning these opportunities as practical, high-impact projects for HR transformation through AI.