Real Results from AI in Services

McKinsey Quantum Black

Insights on Companies and Industries Experiencing AI

Based on the provided search results, here are the main points related to how companies and industries are experiencing AI, with specific examples and outcomes:

Key Insights and Examples

Adoption and Scaling of Generative AI:

• Overview: While generative AI (gen AI) holds significant potential for productivity and cost reduction, only a small percentage of companies have scaled its use. For example, in a survey of 150 executives, only 3% reported scaling gen AI in operations-related domains.

• Example: A bank implemented a gen AI agent to draft credit-risk memos, resulting in a 20% increase in revenue per relationship manager.

Strategic Deployment for Competitive Advantage:

• Overview: Companies are focusing on strategic deployment of gen AI to prioritize use cases that offer long-term value. This includes reimagining workflows and integrating AI with human capabilities.

• Example: A telecommunications provider used gen AI to optimize customer care workflows, reducing call volume by 30% and improving service quality.

Challenges in Scaling AI:

• Overview: Companies face challenges such as unclear roadmaps, talent shortages, and immature governance, which impede scaling AI solutions. Successful companies centralize their AI initiatives and focus on scalability and reusability.

• Example: Companies that have centralized their gen AI initiatives report better returns, with some attributing over 10% of EBIT to AI use.

Knowledge Graphs for Contextual Insights:

• Overview: Knowledge graphs enable companies to integrate diverse data sources, providing contextual insights and enhancing decision-making.

• Example: Financial institutions use knowledge graphs to connect data silos, allowing for a 360-degree view of risk and value, and improving compliance management.

Industry-Specific Applications:

• Financial Services: Knowledge graphs are used for fraud detection, compliance management, and investment research, enabling better risk assessment and regulatory compliance.

• Telecommunications: Gen AI is used to improve customer care by automating routine tasks and enhancing service quality.

Changes by Industry

• Finance: Increased adoption of gen AI for compliance and risk management, with a focus on scaling solutions for greater impact.

• Telecommunications: Use of AI to streamline customer service processes, reduce costs, and improve customer satisfaction.

• Healthcare and Life Sciences: Knowledge graphs are used for personalized health recommendations and drug discovery, enhancing patient outcomes and research efficiency.

Summary

Companies across various industries are experiencing significant changes with the adoption of AI, particularly generative AI and knowledge graphs. While challenges remain in scaling these technologies, successful companies are strategically deploying AI to enhance workflows, improve decision-making, and gain competitive advantages. Knowledge graphs play a crucial role in integrating data and providing contextual insights, enabling better risk management and compliance. As industries continue to evolve, the focus is on leveraging AI to drive productivity, reduce costs, and improve service quality.