AI Transformation: The CIO Playbook

Table of Contents

• Part 1: The Rise of the AI Native
• Part 2: Google Differentiation
• Part 3: Google's Journey
• Part 4: Transformation Examples
• Part 5: How to Start

Part 1: The Rise of the AI Native

The Basics of Generative AI

Generative AI involves several key components:

• Corpus: All written text on a given topic (e.g., "cats are very playful").
• Tokens: Text is codified into tokens.
• Prompt Tuning, Fine Tuning Embeddings: These techniques help models understand relationships between words (e.g., "are", "playful", "very", "cats", "dog").
• Training: Patterns emerge from the data.
• Trained Foundation Model: A model learns these patterns.
• Inference: The model can answer questions in human-like formats and create content.

Multimodality: The ability to reason across different types of input, including Images, Text, Sound, and Video.

History: Digital Natives, Digital Disruptors

• 2000: Digital Natives (Agile, Product and Cloud)
• 2012: Digital Natives (Powered by IT and modern working practices)
• 2015: Digital Parity (Many Traditional enterprises didn't survive)
• 2022: Digital Transformation Begins (Changed IT processes, governance, finance)

AI Transformation: New Mindset, New Challenges

Digital Transformation

• Digitised working practices
• Affected IT teams the most
• Modernised customer facing technology
• Speed to market > data quality
• Changed governance and finances

Data Transformation

• Initiatives to Democratise Data or become "data led"
• Data used to help decision making or to show past performance
• Early adoption of AI
• Especially valuable with customer and product data

AI Transformation

• Affects everyone. Most roles become augmented
• Data quality at the heart of everything
• Radical Self Service
• Organisational structures challenged
• Initiatives to Democratise Development
• Full adoption of AI
• All business data valuable
• Business chooses new propositions on ability to gather data

GenAI and Software Development

Generative AI is disrupting traditional software development ways-of-working. The graph shows a weekly average of questions posted on Stack Overflow, indicating a significant drop after the launch of tools like GPT-3, GitHub Copilot, and ChatGPT.

GenAI as a Double-Edged Sword:

• Creative product innovation: +40% individual performance improvement.
• Business problem-solving: -23% individual performance decrease.

Humans as Complementors to GenAI:

GenAI acts as a great leveler of talent on tasks it excels at. It collapses the distribution of performance, leading to convergence between high and low performers.

Creativity and Innovation Traps:

• While GenAI can boost individual performance, it can hurt collective creativity.
• The collective diversity of ideas decreases when GenAI is used (a -41% difference).

Part 3: Google's Journey

An AI-first Company

Sundar Pichai's vision in 2016 for Google to be an AI-first company.

AI Evolution Milestones

• 2016: AlphaGo
• 2017: AlphaZero
• 2019: StarCraft
• 2020: MuZero

Google's AI Innovations

• RT-2: Turning vision and language into action for robots.
• SIMA: Navigating complex virtual 3D environments.
• AlphaGeometry: Solving Olympiad-level maths problems.
• GNoME: Discovering thousands of new materials.

Part 2: Google's Differentiators

Google Cloud's Unified AI Stack

A Google-engineered, end-to-end open environment comprising:

• Agents & Applications
• Vertex AI
• Research & Models
• AI Hypercomputer

Asymmetric Value

Analysis of various AI models based on Intelligence, Speed, and Price:

• Intelligence: Higher is better (e.g., Gemini 2.5 Pro, Grok 3 mini).
• Speed: Output Tokens per Second; Higher is better (e.g., Gemini 2.5 Flash, Gemini 2.5 Pro).
• Price: USD per 1M Tokens; Lower is better (e.g., Gemini 2.5 Flash, Gemini 2.5 Pro).

Part 4: Transformation Examples

Gemini for Google Workspace

• Helps you write: Refines existing work or helps you get started.
• Helps you create images: Easily create images for presentations and meetings from simple prompts.
• Helps you organize: Analyzes and acts on your data quickly.
• Helps you connect: Improves appearance on video calls.

Google AI Studio

A platform for interacting with Gemini live, managing prompts, and building AI applications. It notes that Google AI models may make mistakes and outputs should be double-checked.

Trendspotting in Retail

Spotting trends by extracting fashion trends from social media and news articles.

Addressed Pain Points:

• Understanding emerging trends for data-driven styling and merchandising.
• Significantly reducing lead time to new design drafts by analyzing data from social media and fashion news.

Enterprise Customer Implementations

• Mondelēz International: Leveraging generative AI (Imagen 3, Veo) for content production, enhancing quality and reducing time-to-market and costs.
• WPP: Using Google Cloud and AI to empower people, producing high-quality, photo-realistic visuals efficiently.
• Agoda: Exploring AI for media generation to create unique visuals and streamline content creation, aiming to engage customers and inspire adventures.

Introducing Google Agentspace

The hyperscale platform built to help enterprises adopt AI agents at scale. It offers:

• Find: Google-quality search across text, images, websites, audio, and video.
• Understand: Synthesizing enterprise data with generative business intelligence using LLMs.
• Act: Accessing, building, and governing agents in a single view to streamline workflows.

Agentspace is the hub for first and third-party agents, including enterprise agents, user-built agents, expert Google-built agents, and Google Cloud Marketplace agents.

Part 5: How to Start

Standing Out in A Sea of Sameness

There is a strong correlation (0.79) between a retailer's level of differentiation and its Pricing Power.

• Generic GenAI models: Useful but accessible to everyone.
• Differentiating tasks: Require careful model selection based on cost, speed, and quality.
• Your unique data: The most valuable asset, providing a competitive advantage.
• Combining unique data with generic models: Creates differentiating outcomes.

The Journey to Value

• AI Augments Humans: Chatbots, Code Assistants, Image and Video Creation, Research, Productivity Tools.
• Humans in the Loop: Demand Forecasting, Customer Communications, Social Media Marketing, Promotions, Customer Support.
• Business Agents: Customer Retention, Assortment Planning, Buying, Inventory Management, Supplier Onboarding.

This journey represents increasing value, complexity, and risk.

No Regrets Next Steps

• Data Platform: Manage data maturely (governance, stewardship, definitions, ownership, consistency, security). Codify business knowledge and track business metrics.
• AI Platform: Centralized AI Platform, Centre of Excellence, AI Ops, model switching, use case repository, partnerships.
• Skills Transformation: Drive knowledge and understanding of AI value, encourage new mindsets, educate leadership on benefits and risks, foster collaboration.
• End to End Workflows: Radical Self-Service, analyze processes in the age of AI, span departments, process AI champions.
• Programmatic Approach: C-level led change management, responsible, ethical, legal framework, measure progress with transformation KPIs.

Mindset Shift: Digital Transformation vs AI Native