How to Accelerate Production Deployment and Embed AI with IBM
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At SaaStr’s AI Day, Exploring the Paradox of AI with IBM
At SaaStr’s AI Day, George Kreitler, Global IBM Embeddable AI Sales Leader, discusses the paradox of AI, equations related to productivity and AI, and the importance of responsible AI.
What Makes Up GDP Growth
From a macro perspective, different variables affect GDP growth in general:
It’s three things:
- Population growth
- Productivity growth
- Debt growth
We know that population growth won’t be seen until the medium-to-long term. With debt growth not serving as a catalyst, the focus is on leveraging productivity growth. This challenge will be crucial in the coming decades, making productivity growth essential for society. This significance highlights the importance of AI in the world.
Disruption and responsibility must coexist for successful progression in the future.
What Makes Up AI Success Today?
Today, building AI models has become faster than before. IBM believes in the diversity of AI models tailored for specific tasks to provide the most value to enterprises. Data plays a crucial role in success, allowing enterprises to differentiate and incorporate their data into AI models for competitive advantage.
Long-term success involves governance focusing on data and AI governance, ensuring that AI decisions are transparent and trustworthy. Use cases are essential for unlocking AI value, emphasizing the importance of choosing the right use cases.
The Current GenAI Stack
Aside from AI models, different components constitute the GenAI stack, including AI and data platforms like IBM’s Watson X. The governance of the end-to-end model life cycle ensures transparent and explainable AI decision-making.
Having software development kits and APIs to embed AI into various applications is crucial. Tuning AI models for specific tasks is essential for interactions and solving tasks efficiently. Data services are fundamental for effective AI, emphasizing the importance of managing enterprise data.
Hybrid environments are ideal for running AI across public and private clouds and at the edge.
The Top 3 AI Buckets
About 70% of current AI applications fall into three categories:
- Digital labor
- Customer experience
- App dev and IT ops
AI assists in automating front-end interactions and optimizing tasks across different sectors. Digital labor combines AI with automation to streamline tasks efficiently. App development and IT ops benefit from AI in modernizing applications and generating code.
Two Use Cases: Ovum and CrushBank
Ovum Medical and CrushBank showcase the value of AI in healthcare and IT support, respectively. Ovum Medical leverages telemedicine with Watson X to improve patient care, while CrushBank enhances IT support ticket handling with AI.
Key Takeaways:
- Embrace AI leadership with an understanding of risks for growth.
- Develop skills in AI prompting and management for future challenges.
- Stay updated on new AI models to leverage their capabilities effectively.
