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Transformation

Digital Transformation related for IT leaders driving Generative AI adoption

The Hidden Traps of "Business Value" in Data-Driven Decisions:

Overemphasizing “business value”—often a vague and subjective term—in data decisions can mislead organizations. Instead, leaders should prioritize projects based on criticality, deployability, and core needs.

The Impact of Conway's Law on Data Architecture and Data Engineering

Conway’s Law states that systems mirror organizational structures. In data engineering, this leads to data silos and fragmented architectures. Overcoming this requires collaboration and solutions like data mesh.

The Critical Role of Data Engineering in Unlocking Generative AI's Potential

Discover how CIOs can maximize AI value by investing in data quality, scalable infrastructure, and ethical practices. Learn how Data Engineering is essential for deploying AI models that drive innovation and competitive advantage.

AI-Native Outcome Delivery

AI agents are revolutionizing professional services, moving from simple automation to complex reasoning. From legal work to cybersecurity, these systems are making expert services more accessible and efficient.

From SaaS to VaaS/WaaS: The Next Evolution in Business Models

As SaaS faces commoditization, Value-as-a-Service (VaaS) and Work-as-a-Service (WaaS) focus on outcome-based pricing and AI-driven efficiency. These models deliver measurable value, automating workflows and redefining how businesses scale and compete.

State of AI Q3 2024

Discuss various topics from research to hardware and incumbents working on GenAI.

Business Strategy

Join us as we dive into crucial business strategies from our favorite books on entrepreneurship and startup building, offering insights and practical takeaways for founders and business leaders.

Top Generative AI Use Cases for Enterprises in 2024

The business technology landscape is experiencing a remarkable shift, fueled by the incredible advancements in Generative Artificial Intelligence (GenAI).

AI 2.0 Adoption

Our 4-Stage Maturity Model provides a snapshot to drive a conversation on AI 2.0 Adoption in an Enterprise around Decision Intelligence.

LLMOps Best Practices: Streamlining Large Language Model Management

To fully harness the potential of these large language models, it is crucial to streamline their deployment and management for real-world applications. This is where LLMOps, or Large Language Model Operations, comes into play.