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How McKinsey built its GenAI platform

How McKinsey built its GenAI platform

Plus, top service providers, Uber's prompt toolkit, and more.

Lewis Walker
December 01, 2024

WELCOME, EXECUTIVES AND PROFESSIONALS.

Since the last edition, we've reviewed hundreds of the latest insights on best practices, case studies, and innovation. Here’s the top 1%...

In today’s edition:

How McKinsey built its GenAI platform.
Uber introduces its Prompt Engineering Toolkit.
Top GenAI service providers, according to ISG.
Transformation and technology in the news.
Career opportunities & events.

Read time: 4 minutes.

CASE STUDY

How McKinsey built its GenAI platform

Image source: McKinsey & Company

Brief: A new McKinsey case study reveals how its transformed work with its GenAI platform, Lilli. The objective was to build a platform powered by its proprietary knowledge to accelerate and improve insights for its teams and clients.

Breakdown:

Proof of Concept (March 2023, 1 week): A small team built a lean prototype in 1 week and secured investment approval.
Roadmap & Operating Model (April 2023, 2 weeks): Aligned on priority use cases based on value, impact, feasibility and requirements. Set up cross-functional agile squads for delivery.
Development Decisions (May 2023, 2 weeks): Guided by a five-point framework that evaluated cost, scalability, performance, security, and timing. Combined a hyperscaler’s prebuilt model with five of its own smaller expert models for enhanced answer relevance.
Build, Test & Iterate (May 2023, build: 5 weeks, test: 3 weeks): Alpha tested with 200 users, with rapid feedback improving response quality.
Firmwide Rollout (July 2023, 3 months): Gradual rollout over 3 months, available to all employees by October 2023.
The platform helps employees quickly learn new topics, access McKinsey frameworks, analyze data, develop presentations in McKinsey's style, create project plan drafts, and more.
72% of employees are active on the platform, saving up to 30% of time and processing over 500,000 prompts monthly.

Why it’s important: McKinsey's swift development of Lilli demonstrates how enterprises can leverage GenAI to enhance productivity and accelerate knowledge work. McKinsey's full case study also details lessons learned and further insights into the platform.

CASE STUDY

Uber introduces its Prompt Engineering Toolkit

Image source: Uber

Brief: Uber's case study explores its centralized Toolkit for building, managing, executing, and evaluating prompts across models. It details the prompt engineering lifecycle, architecture, evaluation, and production use cases.

Breakdown:

Uber’s Model Catalog offers descriptions, metrics, and usage guides for models, while the GenAI Playground allows users to test LLM capabilities.
The Prompt Builder automates prompt creation and helps users discover prompting techniques tailored to their specific use cases.
Prompts can be evaluated against datasets using LLM-based or custom code evaluators.
The architecture features a Prompt Template UI/SDK for managing templates and revisions, integrated with APIs like GetAPI and ExecuteAPI to interact with models.
Models and prompts are stored in ETCD and UCS, driving the Offline Generation and Prompt Evaluation Pipelines.
Prompt templates are reviewed before revisions, deployed with tags, and managed via ObjectConfig, Uber’s internal configuration system, for production deployment.

Why it’s important: Uber's toolkit enhances prompt consistency, reusability, and scalability, improving model performance while safeguarding production environments.

MARKET INSIGHT

Top GenAI service providers, according to ISG

Image source: ISG

Brief: ISG published its Generative AI Services quadrants, a 70-slide report analyzing the strengths, challenges, and competitive differentiators of GenAI service providers. It covers strategy and consulting, as well as development and deployment services.

Breakdown:

As part of its study, ISG surveyed 150 GenAI service providers, subsequently assessing 79 enterprises. ISG produced two quadrants focused on services and solutions enterprises commonly seek.
The Strategy and Consulting Services quadrant evaluates providers that guide enterprises through exploration, strategy development, governance and compliance.
While the Development and Deployment Services quadrant examines providers enabling the build and scale of GenAI applications.
Each quadrant compares service providers based on portfolio attractiveness, competitive strength, and includes commentary.
For instance, in strategy and consulting (see image of quadrant above), leaders such as Accenture, Deloitte, and IBM excel with comprehensive strategies, compelling use cases, and end-to-end frameworks driving enterprise adoption and transformation.
A version of the report can be found here for download if you wish to explore the insights in detail.

Why it’s important: While studies like this can help enterprises as a starting point to assess service providers, some providers don't participate, and often capabilities and synergies are nuanced based on specific circumstances.

Deloitte published a point of view on how Generative AI will transform customer service, released a 42-page report on sustainable AI, and announced AI Assist, a GenAI accelerator for software development.

McKinsey shared insights on explainable AI (XAI), highlighting its role in building trust, driving ROI, and effective implementation. It also published an article on accelerating Generative AI adoption in the context of the Dutch market.

Capgemini released a 76-slide report on GenAI in organizations, based on a global survey of 1,100 executives, with 21 use cases from enterprises like Mercedes and Pepsi. It also published a report on GenAI in cybersecurity.

Microsoft published a 57-page report on unlocking the potential of the US Generative AI ecosystem and a 50-page report on Canada's Generative AI opportunity.

Fractional shared a guide to scoping GenAI projects, based on lessons from "100+ projects", outlining an 8-step approach with three project examples. Nasscom published 'The Developer's Playbook for Responsible AI' in India, including a checklist to mitigate risks.

Anthropic launched the Model Context Protocol (MCP), an open-source standard for AI systems to connect with data sources and tools. It also introduced custom writing styles for Claude with sample text uploads.

Microsoft Azure is reportedly leading in cloud AI engagements, particularly in GenAI, according to a recent IoT Analytics report, with AWS and Google trailing behind.

Amazon has reportedly developed a new AI model, Olympus, focused on advanced video and image processing, with a potential release next week.

Databricks is reportedly raising $5 billion at a $55 billion valuation, aimed at helping employees cash out and delaying IPO plans.

/dev/agents, a new startup launched by former Google, Meta, and Stripe executives, secured $56M in seed funding and aims to create an open source system which they're calling an "Android moment" for AI agents.