SaaStr - LIVE SaaStr AI Day: Vertical AI - IBM's Tactics for Enabling Builders
IBM is leveraging foundational AI models, like those used in ChatGPT, to create vertical-specific applications that deliver industry-specific expertise and operational efficiency. This approach is particularly beneficial for software as a service (SaaS) companies, enabling them to provide enhanced customization and value to their clients. IBM's investment in vertical AI is driven by the fact that enterprise data represents less than 1% of public data in foundational models, presenting a significant opportunity for specialization. IBM's Watson X platform is designed to support this vertical AI strategy by offering tools for building industry-specific AI solutions. The platform includes a Lakehouse product for data connectivity, over 28 models for various applications, and a governance platform to ensure trust and explainability. IBM's approach allows for cost-effective and efficient model customization, as demonstrated by their Granite series, which offers significant cost savings and performance improvements over larger models. IBM encourages companies to start building and experimenting with AI to stay competitive, emphasizing the importance of integrating AI into core business values and processes.
Key Points:
- IBM is focusing on vertical AI to customize foundational models for specific industries, enhancing expertise and efficiency.
- Watson X platform supports vertical AI with tools for data connectivity, model customization, and governance.
- IBM's Granite series offers cost-effective model customization with significant performance improvements.
- IBM encourages companies to integrate AI into core business processes to stay competitive.
- IBM provides resources and support for companies to build and experiment with AI solutions.
Details:
1. 🔍 Exploring IBM's Vertical AI Strategy
- IBM is focusing on transforming horizontal AI foundation models, like Gemini or Chat GPT, into vertical-specific applications.
- The transformation aims to deliver industry-specific expertise, operational efficiency, and compliance.
- This approach unlocks unique opportunities for software-as-a-service companies to enhance value to clients.
- Key benefits include improved accuracy, scalability, flexibility, and enhanced customization for end clients.
- IBM's strategy is particularly focused on sectors such as healthcare, finance, and supply chain, where tailored AI solutions can drive significant improvements.
- For example, in healthcare, vertical AI applications can assist in patient diagnosis, treatment planning, and operational efficiency, while in finance, they can enhance fraud detection and risk management.
- The strategy also emphasizes compliance and regulatory adherence, crucial for sectors like finance and healthcare.
- This vertical approach enables IBM to differentiate its offerings from competitors and provide a competitive advantage by deeply integrating AI solutions into business processes.
2. 🏢 Leveraging Enterprise Data with AI
- Enterprise data represents less than 1% of all data in Foundation models, highlighting a significant opportunity for companies to leverage their data for specialized AI applications.
- IBM is investing in vertical AI to provide specialized, deep, and targeted functionality that can be integrated with clients' data and systems.
- The future of AI is increasingly vertical, focusing on domain-specific applications like drug discovery, which require unique datasets and expertise to drive innovation and efficiency.
3. 🤝 Collaborations with Industry Leaders
- IBM is structuring Watson X to enable software as a service companies and ISVs to leverage their data, expertise, and software for deeper integrations.
- The importance of AI use cases has grown, focusing on areas like IT automation, code development, digital labor, and AI assistance.
- Significant value is derived from first AI use cases, such as enhancing customer service and legal data analysis.
- The focus is on connecting data with foundational models, especially in specialized fields like pharmaceuticals and sales technology.
- Collaborations with industry leaders aim to enhance AI-driven solutions and facilitate better integration across various sectors.
- IBM's partnerships are designed to integrate AI capabilities into existing systems, improving efficiency and innovation.
- Specific use cases include optimizing sales strategies in technology firms and advancing drug discovery in pharmaceuticals.
- The strategy involves leveraging foundational models to tailor AI applications to industry-specific needs.
4. 🔧 Customizing AI Solutions for Industries
- IBM collaborates with strategic partners like ServiceNow, Adobe, and Salesforce to enhance AI applications, leveraging the Watson X platform.
- The partnership with Salesforce focuses on AI and autonomous agents to improve sales and service processes, potentially increasing efficiency and customer satisfaction.
- IBM supports ServiceNow by using AI for operations, ensuring models provide consistent, explainable, and trustworthy results, enhancing operational reliability and user trust.
- Digital-native companies like Applause use Watson X for automating and optimizing tasks such as software testing, including test case rewriting and summarization, leading to time and cost savings.
- The development of customized models that deeply understand specific industries such as sales, legal, and finance is prioritized over general models, highlighting a targeted approach to AI deployment.
5. 🔗 Watson X Platform: Features and Flexibility
5.1. Watson X Data and Models
5.2. Model Diversity and Governance
5.3. Developer Studio and Flexibility
6. 💡 Integrating Domain Expertise into AI
6.1. Deployment and Cost Efficiency
6.2. Model Customization and Techniques
6.3. IBM's Solution for Model Training
7. 🛠️ Enhancing AI Models with Instruct Lab
- Instruct Lab reduces the need for extensive data in fine-tuning by using a 1:1000 metric to generate synthetic examples from a smaller data set.
- Larger models with up to 400 billion parameters can transfer knowledge to smaller models (2 to 15 billion parameters), making them more affordable and faster.
- IBM's Granite series, when fine-tuned using Instruct Lab, demonstrated a 66% cost reduction and a 10% performance improvement compared to larger models like Llama 370 billion.
- IBM Granite and Instruct Lab deliver 98.5% cost savings and 35-week time savings compared to traditional tuning methods, enabling faster and more cost-effective model customization.
8. 🚀 Practical AI Applications and Success Stories
- IBM's Watson xate AI platform facilitates advanced data ingestion, model evaluation, and audit readiness through a low code/no code interface, allowing users to update models via a GUI, enhancing usability for non-technical stakeholders.
- The platform supports fine-tuning and leveraging different AI techniques like Rag, enabling users to tailor solutions based on specific use cases, providing flexibility and customization in AI applications.
- IBM recommends evaluating and leveraging multiple AI options for specific use cases, highlighting the importance of flexibility in adapting AI solutions to different business needs.
- The platform's demo and instruct lab are accessible via Watson X, allowing users to experiment with AI tools on personal devices, promoting hands-on engagement and understanding of AI capabilities.
- IBM emphasizes the importance of vertical AI, which focuses on integrating domain expertise into foundation models, offering differentiated AI solutions tailored to specific industries or sectors.
- Customizing foundation models is complex, but IBM offers unique solutions like instruct lab and common methods like Rag and fine-tuning to integrate domain expertise effectively.
- IBM invites potential users to engage with the platform to explore how it can accelerate differentiation and provide additional business value, emphasizing collaboration to achieve strategic outcomes.
9. 🌐 Engaging with IBM: Opportunities and Guidance
9.1. Successful Applications and Collaboration
9.2. IBM's R&D and Strategic Support
9.3. Guidance for Early AI Adoption
9.4. Communication and Contact
10. 👋 Conclusion and Future Directions
- The session on AI day concluded with an invitation to join two more sessions, focusing on future engagement and deeper exploration of AI applications.
- There is anticipation for the Sounder annual event with the IBM team, indicating ongoing collaboration and potential networking opportunities for attendees.
- Future sessions will delve into specific AI advancements and their practical applications in industry, providing attendees with actionable insights and strategies for implementation.
- Participants are encouraged to leverage these sessions for networking and gaining insights into cutting-edge AI technologies.