{ "docId": "019dd923-5de0-76bd-a167-3fb4ca3d99cc", "docSlug": "76b23d172495f5b9", "documentTitle": "Beyond thenoise: Orchestrating AI-driven customer excellence", "authorId": "KPMG", "authorName": "KPMG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 24, "pageCount": 68, "prevPage": 23, "nextPage": 25, "slideType": "implementation_plan", "function": "plan_implementation", "density": "overcrowded", "nDataPoints": 1, "notes": "Part of a larger implementation framework; focuses on the data and insight layer.", "elementsJson": [ "headline_text", "paragraph", "bullet_list" ], "metadataConfidence": 0.9, "imagePath": null, "slideHref": "/slides/019dd923-5de0-76bd-a167-3fb4ca3d99cc/24", "deckHref": "/decks/019dd923-5de0-76bd-a167-3fb4ca3d99cc", "deckJsonHref": "/decks/019dd923-5de0-76bd-a167-3fb4ca3d99cc.json", "deckAnchorHref": "/decks/019dd923-5de0-76bd-a167-3fb4ca3d99cc#slide-24", "components": [ { "bbox": null, "kind": "callout", "text": "Organizations must leverage customer insights to develop robust AI capabilities, strategically aligning customer data, advanced analytics and AI technologies to enhance customer experiences and drive business growth.", "attrs": null, "subkind": null, "toolName": "Visual emphasis", "toolSlug": "visual-emphasis", "confidence": null, "componentId": "019dd951-c7c6-71dc-a481-519dbd0d53df", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.1, "w": 0.435, "x": 0.509, "y": 0.516 }, "kind": "list", "text": "To enable the AI to truly understand the customer's unique needs and provide tailored, efficient, and accurate support, it is possible to fine-tune large language models (LLMs) into smaller, domain-specific versions. Organizations can create specialized \"AI brains\" that align directly with their and their customer's objectives. This process is designed to bring greater efficiency and accuracy, transforming broad AI potential into focused support that grasps the nuances of the context.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "273e1310-6a4b-4acd-a178-ebaacf275abe", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.14, "w": 0.435, "x": 0.509, "y": 0.356 }, "kind": "list", "text": "Data needs to be labelled, creating an AI dataset where each data point is annotated with a specific label or category, providing the model with known outcomes to learn from during training. With AI-driven data governance, companies can classify sensitive information, track its origins, and flag unusual activity, categorizing, and analyzing it, making information more accessible and meaningful for decision-making. As data sources and volumes grow, an AI-driven approach to data management helps ensure that customer data remains secure and accurate.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "9140176c-207c-4584-898a-531eef88f216", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.06, "w": 0.435, "x": 0.509, "y": 0.636 }, "kind": "list", "text": "Key for marketing, sales and service teams is the use of classification, prediction, forecasting and optimization, using techniques such as machine and deep learning to uncover actionable insights. This enables them to identify patterns and trends that may not be immediately obvious.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "d0a4b08d-11fd-4d79-919c-181dcac0c1eb", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.08, "w": 0.435, "x": 0.064, "y": 0.656 }, "kind": "paragraph", "text": "To ensure customer data is of high quality, as the backbone of successful AI systems, organizations must invest in data management solutions that ensure the accuracy, completeness and relevance of customer data. Data quality assurance processes like data cleaning, normalization and validation are helping to ensure the integrity of data sets, eliminating noise, bias and inaccuracies in AI models.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "21ab72a3-9862-4b5a-ac39-3dec1993708e", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.12, "w": 0.435, "x": 0.064, "y": 0.356 }, "kind": "paragraph", "text": "Customer insights are derived from data collected through various touchpoints, offering a deeper understanding of customer behaviors, preferences and pain points. According to the KPMG global tech report 2024, 78 percent of technology leaders admit that insights collected from customers are not used in a meaningful way. Survey respondents also ranked targeting tech investments toward the strongest service pain points flagged by customers and employees as the number one tactic to help achieve quick wins from their technology investments.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "22fb5803-edc4-4889-870c-5ab9f01c1167", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.06, "w": 0.435, "x": 0.509, "y": 0.278 }, "kind": "paragraph", "text": "Robust data governance frameworks can also help maintain and protect data, aligning with regulatory requirements and ethical standards. Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) and Customer Data Management (CDM) are vital for managing and maximizing data use across the organization:", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "baf2bac0-2f3c-4554-91f0-0b244fae379a", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.14, "w": 0.435, "x": 0.064, "y": 0.496 }, "kind": "paragraph", "text": "To establish a comprehensive customer data collection framework, organizations need to begin by integrating structured and unstructured data from multiple sources, such as contracts, product images, and videos, drawn from internal and external systems such as websites, social media, customer service interactions, and sales data. Internal systems such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) are vital sources of key data. Increasingly, Customer Data Platforms (CDPs) are being used to draw customer data from these systems and make it suitable for AI.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "bd950d04-178b-49ce-9931-dfddd56f4323", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.06, "w": 0.435, "x": 0.064, "y": 0.278 }, "kind": "paragraph", "text": "Organizations must leverage customer insights to develop robust AI capabilities, strategically aligning customer data, advanced analytics and AI technologies to enhance customer experiences and drive business growth.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "dc2e359f-6a05-4625-91da-3fae743ab9d7", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.038, "w": 0.382, "x": 0.064, "y": 0.145 }, "kind": "title", "text": "Step 3: Inference and insights", "attrs": null, "subkind": "headline", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "a5925766-7ce3-4e48-bc65-46647d2e857b", "frameworkName": null, "frameworkSlug": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }