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Mckinsey on driving revenue with GenAI

Mckinsey on driving revenue with GenAI

Plus, BMW case study, building AI multi-agents faster, and more.

Lewis Walker
November 11, 2024

Welcome, executives and professionals.

We identify and breakdown the top 1% of Generative AI for enterprises.

This week:

McKinsey on driving revenue growth in contact centers with GenAI.
BMW Group enhances procurement with GenAI.
Microsoft releases open-source multi-agent AI system.
Fast Fives: Transformation and technology in the news this week.
Career opportunities & events.

Read time: 4 minutes.

CASE STUDY

McKinsey on driving revenue growth in contact centers with GenAI

Brief: McKinsey article explores how GenAI can help drive contact center revenue by increasing sales conversions and reducing cancellations. It includes a short enterprise case study and tips for getting started.

Breakdown:

For many enterprises, contact centers remain an unoptimized source of revenue, with significant potential to improve conversions and reduce cancellations.
Current sales optimization methods rely on limited data (typically covering under 5% of calls) and fail to capture the nuances in agent performance that actually drive conversions.
A 4-week GenAI voice analysis pilot at a mobility company showed that primary skills like identifying customer needs such as urgency, flexibility, and trust were linked to higher conversions.
Secondary skills, like objection handling, had less impact on conversions. By comparing agents with top converters, analysis identified coaching opportunities.
The company expects a 5-10% increase in conversion rates, a 10-20% reduction in cancellations, and a 10% improvement in customer experience ratings from this initiative.
The report emphasizes managing GenAI risks, such as the risk of inconsistent results when simultaneously understanding a cancellation reason and determining the call's outcome in a single prompt.

Why it’s important: GenAI can help drive revenue growth in contact centers, but success requires careful implementation including managing risk and effective collaboration between tech and business teams.

CASE STUDY

BMW Group enhances procurement with GenAI

Brief: This case study outlines how BMW Group, with BCG and AWS, implemented its 'Offer Analyst' GenAI application to improve procurement efficiency and accuracy by automating offer reviews and comparisons.

Breakdown:

BMW Group's traditional procurement process involved three main steps: document collection, review and preselection, and offer selection, which include challenges like manual effort, risk of errors, and less meaningful work.
The 'Offer Analyst' GenAI application is designed to help enhance the offer evaluation process, with a user-friendly interface and tailored to the needs of procurement experts.
The enhanced process includes RfP document uploads, offer uploads, information extraction, initial analysis (standard criteria) and tailored analysis (ad hoc criteria), download analysis, and interactive analysis (chat with your offer).
Key solution architecture components of the 'Offer Analyst' include frontend/UI, document storage, integration layer, GenAI layer, API layer, and security features, all built on a serverless AWS architecture for scalability and resilience.
BMW benefits from reduced manual proofreading time, improved decision-making, reduced errors through automated compliance checks, and increased employee satisfaction by enabling more engaging work.

Why it’s important: GenAI applications like 'Offer Analyst' are improving procurement, leading to greater operational efficiency, enhanced employee satisfaction, and a more effective procurement process.

ACCELERATOR

Microsoft releases open-source multi-agent AI system

Brief: Microsoft releases Magentic-One, an open-source multi-agent AI system that helps automate complex tasks faster via a central Orchestrator managing various support agents. Github repository code here.

Breakdown:

Magentic-One is led by the Orchestrator agent, responsible for high-level planning, directing agents, and tracking progress throughout task execution.
The Orchestrator begins by creating a task plan and recording essential details in a Task Ledger.
As the task progresses, the Orchestrator updates the Progress Ledger, reflecting on progress and checking for task completion.
If a task remains incomplete, the Orchestrator assigns a subtask to one of the supporting agents.
Supporting agents include WebSurfer (managing web browsing and summarizing), FileSurfer (navigating local files), Coder (writing code and analyzing data), and ComputerTerminal (executing programs and installing libraries).
Magentic-One operates in a cycle of planning and progress checks until all tasks are completed, with agents being model-agnostic, though GPT-4 is the default. The system is built on Microsoft’s framework AutoGen.

Why it’s important: Magentic-One offers a system for automating complex workflows. By utilizing specialized agents and a central Orchestrator, it can tackle diverse tasks efficiently.

Transformation

In the news this week:

McKinsey explores how enterprises in the Middle East are adopting GenAI quickly, though few are yet seeing significant benefits. A different article explores how GenAI can help optimize product portfolios more effectively.
AWS published a blog on data governance for GenAI, covering data visibility, cataloging, iterative approaches, access controls, and more. IBM also released a two-part blog series on data governance (part 1, part 2).
PwC and Microsoft issued a 14-page report on deploying AI at scale, focusing on the roles of cloud and cybersecurity.
TCS highlighted how GenAI can help tackle challenges in IT application security assessments, providing solutions like automated vulnerability scanning, threat modeling, and personalized recommendations.
EY published a 40-page report examining the growth, current state, and policy landscape of GenAI in India.

Technology

In the news this week:

Google announced a new AI hub in Saudi Arabia for Arabic language models and regional applications. Saudi Arabia also revealed "Project Transcendence", a $100B AI initiative to grow its tech sector.
OpenAI launched a ‘Predicted Outputs’ feature for GPT-4o models to help reduce latency, and acquired the domain chat.com.
Anthropic announced a partnership with Palantir and AWS to bring its Claude AI models to U.S. intelligence and defense agencies, joining Meta and OpenAI in the tech’s shifting role within government.
T-Mobile will reportedly pay $100M to OpenAI over three years in the development of an AI platform for customer service.
Meta's plans for a nuclear-powered AI facility hit a setback after a rare species of bees were discovered at the proposed site, causing regulatory and environmental issues.