🔗https://shre.ink/GenAI-Assessment-Framework-GAF
by Anushrut Gupta
1. Motivation
Enterprises are racing to integrate Generative AI, but many struggle to pick the right tools or prioritize effectively. This article introduces the need for a durable and actionable framework that helps map business needs to AI capabilities—bridging the gap between hype and strategy.
2. The GAF 3×3 Matrix
The heart of the article is the GenAI Assessment Framework (GAF)—a 3x3 matrix designed to help enterprises assess where their AI needs lie. It aligns what AI can do (capabilities) with how it’s delivered (customization model), offering a visual and practical lens to evaluate and compare AI use cases.
2.1 Axis 1: Capability Mode (What the AI does for the enterprise)
Search: AI turns structured/unstructured data into conversational answers. Great for knowledge bases, FAQs, and internal documentation.
Act: AI integrates into tools to trigger actions or workflows (e.g., booking a meeting, updating CRM). The agent acts on your behalf.
Solve: AI tackles complex, multi-step problems—like generating code or strategic planning—often in technical or decision-making contexts.
2.2 Axis 2: Customization Approach (How the AI is implemented & improved)
Off-the-shelf (Config-driven): Plug-and-play tools with minimal setup. Fast to deploy but limited in flexibility.
Framework (Code-driven): Requires developer effort to fine-tune and integrate; offers more control and customization.
Specialized AI (Feedback-driven): Continuous improvement through user feedback, retraining, or fine-tuning. High potential, but complex to manage.
3. What We Analyzed: 50+ Solutions Across the Market
The author evaluated more than 50 enterprise AI products (e.g., Notion, Slack AI, Microsoft Copilot) and mapped them to the GAF Matrix. This helped them identify clusters, gaps, and best practices across different industries.
4. Why Enterprise AI Projects Fail: The Framework Reveals All
Failure Mode 1: The “Learning Gap” in Enterprise Tools: Most internal systems weren’t designed with generative AI in mind. Without integrated context, AI tools lack the “memory” needed for quality output.
Failure Mode 2: The Misaligned Assistant: AI assistants often behave generically, failing to adapt to specific organizational workflows, tools, and language—leading to low adoption.
Failure Mode 3: Piecemeal Pilots Lacking Transformative Impact: Many enterprises run isolated pilots without long-term plans. This causes fragmentation and prevents systemic gains across the org.
Failure Mode 4: High Failure Rate of Internal “Build” Projects: Building custom AI solutions from scratch is hard. Without cross-functional teams, access to high-quality data, and proper maintenance, most internal builds stall or fail.
5. The Logical Evolution: Assistants → Agents → Co-workers?
The authors predict a shift from static “assistants” to proactive AI agents that take initiative, eventually evolving into co-workers capable of reasoning, memory, and autonomy. Enterprises must prepare for this paradigm shift by evaluating readiness at each stage.
6. Take Action: Get Your Custom GenAI Assessment Test
To operationalize the GAF Matrix, PromptQL offers a self-assessment to help teams understand where they are and what to prioritize next. It encourages readers to identify use cases, map them on the matrix, and define a roadmap toward scalable, transformative GenAI implementation.