Welcome, fellow agent of innovation and progress.

This is your AI change management manual for business model enhancement. Here we map, index, and signpost every aspect of executive and organizational AI adoption to ensure you start smart and create value with artificial intelligence.
Let's light the way together.

The big news today was once again surrounding OpenAI with their announcement of a “mini” model. Keep reading to learn what that is but first read up on what a foundation model is if any of this is news to you. :)

In today’s Daily Spark

• OpenAI’s New GPT-4o Mini

• AI Foundation Models

• Integrating AI into Clinical Workflow

• Value Chain Segment Focus: Service

• BlackRock’s AI infrastructure

Navigate AI News

OPENAI
OpenAI’s New GPT-4o Mini

Executive Brief: GPT-4o mini is like a smaller, cheaper version of more advanced AI models. It's a big deal because it makes powerful AI technology available to more people and companies who couldn't afford it before. This means that developers can now use advanced AI in their projects without spending too much money. For example, Notion AI, which helps with writing and organizing notes, and Grammarly, which checks grammar and improves writing, both use foundation models like GPT. With GPT-4o mini, we might see many new AI-powered apps like these, but with even more advanced features. This could change how AI is used in everyday technology, making smart features more common in the apps and websites we use.

TODAY’S TERM
Foundation Model

GPT-4o Mini is an AI foundation model. A large-scale neural network trained on vast amounts of data to perform a wide variety of tasks. Models like this serve as a base upon which more specific applications are built. Other examples include BERT by Google and Claude by Anthropic. The key characteristics of foundation models are:

Scale: Trained on massive datasets.

Generalization: Capable of performing multiple tasks with minimal fine-tuning.

Adaptability: Can be customized for specific tasks or industries.

AI INSTRUCTIONS

Prompt with Purpose
AI Foundation Models

Copy and paste into ChatGPT. Edit the [text inside these brackets] to fit your preferred technical level.

OpenAI’s Foundation Model named “GPT” is accessed directly via what you know as “ChatGPT”. Uses are:

Chatbots: Individuals use models like ChatGPT for conversational assistance, providing instant answers, brainstorming ideas, recommendations, or even companionship.

Content Creation: Users leverage these models to draft emails, write articles, create social media posts, or generate ideas.

Condense complex topics: Find and parse research papers, and provide explanations on a wide range of subjects for learning and research.

Text Summaries: Collect notes from meetings, emails, and other documents and shorten the copy for quick consumption.

Data Analysis: Automatically identify patterns, relationships, and insights within large datasets.

Human Side of AI
Integrating AI into Clinical Workflow

This case study from Lahey Hospital & Medical Center demonstrates how AI workflows can be effectively integrated into clinical settings adding value without replacing existing staff.

The objective was to implement AI algorithms to help diagnose and triage imaging studies for potentially critical findings. The hospital integrated six AI algorithms into its radiology department's workflow over two years.

Key features:

• Case prioritization: AI-flagged cases with positive findings are moved to the top of the worklist.

• Visual indicators: A red (positive) and green (negative) badging system was created to display AI findings.

• Processing status: A gray badge was added to indicate when an algorithm is still processing a study.

• Results: The AI algorithms achieved sensitivity and specificity between 90-95% for intracranial hemorrhage detection.

• Benefits: Improved prioritization of potentially critical cases, faster reading times, and enhanced patient care.

This case study highlights an AI workflow successfully integrated into existing systems to improve efficiency and patient outcomes in a way that’s very human after all.

Value Chain Enhancement
Segment Focus: Service

Executive Brief: Bell Canada, a leading telecommunications company in Canada, has partnered with Google Cloud to implement Google Cloud Contact Center AI in their business operations. This real-world example demonstrates how a major corporation is leveraging AI to enhance its customer service capabilities.

Key points of Bell Canada's implementation:

• AI-powered contact center: Bell Canada is using Google Cloud's Contact Center AI to create an intelligent, AI-driven contact center solution.

• Improved customer experience: The AI system aims to provide faster, more efficient, and personalized customer service experiences.

• Enhanced agent productivity: The technology assists human agents by providing real-time information and suggestions, allowing them to focus on more complex customer needs.

• Multi-channel support: The AI solution supports various communication channels, including voice, chat, and messaging.

• Advanced analytics: The system offers insights into customer interactions, helping Bell Canada to improve its service quality continually.

• Scalability: As a cloud-based solution, it allows Bell Canada to easily scale its customer service operations as needed.

• Integration with existing systems: The AI technology integrates with Bell Canada's existing contact center infrastructure.

This implementation showcases how to partner with a vendor to strategically integrate foundation models into their value chain to create a sustainable competitive advantage. Other options include internal or outsourced development.

Scale Economics
BlackRock’s AI Infrastructure

Summary: One prominent example of a business that has successfully developed a data-driven culture and invested in AI infrastructure and talent for investment purposes is BlackRock, the world's largest asset manager.

BlackRock has made significant strides in incorporating AI and data analytics into its investment strategies and operations:

• Aladdin Platform: BlackRock developed Aladdin, an advanced AI-powered investment management and risk analytics platform.

• Data Science Team: In 2018, BlackRock established its AI Labs, dedicated to researching and developing AI applications for investment management.

• Machine Learning Models: BlackRock uses machine learning models to analyze alternative data sources, including satellite imagery and social media trends, to gain unique insights for investment decisions.

• Talent Acquisition: The firm has been actively recruiting top AI and data science talent from tech companies and universities to bolster its capabilities.

• Continuous Learning: BlackRock emphasizes ongoing education and training for its employees to keep up with the latest AI and data science developments.

• Data-Driven Decision-Making: The company has fostered a culture where data analytics inform virtually all aspects of its investment strategies and operations.

• Partnerships and Acquisitions: BlackRock has strategically partnered with and acquired tech companies to enhance its AI capabilities. For example, it acquired Scalable Capital, a digital investment manager, to boost its AI-driven personalized portfolio management offerings.

By investing in AI infrastructure, and talent, and fostering a data-driven culture, BlackRock has positioned itself at the forefront of AI adoption in the investment industry. This approach has allowed the company to enhance its investment strategies, improve risk management, and offer more sophisticated products to its clients.