AI generated graphic.

Your true competition? It's not the usual suspects. It's the Al-natives. No legacy baggage. Boundless scale. Autonomous agents. Entirely new workflows. No need for permission. They're not experimenting with Al-they're using it to rewire entire industries.

@allybex.bsky.social

Your real competition isn't traditional rivals. It's AI-native companies.

They have no legacy burdens, operate at immense scale with autonomous agents, and redefine industries with new workflows.

By the time you notice their impact, it might be too late. #AI #Disruption

This Gartner report, "Innovation Guide for Generative AI Technologies" (March 24, 2025), highlights the rapid enterprise adoption of Generative AI (GenAI), moving from pilot projects to full-scale production applications. The market is characterized by swift advancements in foundation models, especially Large Language Models (LLMs), and the emerging disruptive force of AI agent technologies. IBM is prominently positioned as an "Emerging Leader" across multiple critical GenAI submarkets evaluated by Gartner. These include: - Generative AI Specialized Cloud Infrastructure - Generative AI Model Providers - Generative AI Engineering - AI Knowledge Management Apps/General Productivity This consistent leadership placement underscores IBM's comprehensive offerings and strong future potential in the evolving GenAI landscape. The report notes that commercial models like IBM's Granite are part of the economic engines for developing companies. Several other major technology companies are also recognized as "Emerging Leaders" alongside IBM, indicating a competitive and dynamic market. Notably, Google, Microsoft, and Amazon Web Services (AWS) feature as leaders across these same GenAI categories, positioning them as key competitors and innovation drivers. Other significant players frequently appearing as "Emerging Leaders" or strong contenders in these segments include Alibaba Cloud, NVIDIA, Oracle, Databricks, UiPath, Salesforce, and Teradata. The report emphasizes that GenAI permeates the entire technology stack and most industry verticals. It advises enterprises to plan for managing technical debt from GenAI pilots, design loosely coupled solutions for model flexibility, and prioritize ethical and responsible AI practices. For technology buyers, the "Emerging Market Quadrants" aim to provide a dynamic view of vendor capabilities in this fast-moving space.

In the 2025 Gartner Innovation Guide for Generative AI Technologies, IBM is positioned as an Emerging Leader in Generative AI Model Providers†quadrant. This placement underscores IBMs growing strength and enterprise readiness in the AI space, driven by solutions like watsonx.ai, watsonx Code Assistant, and the IBM Granite models. IBM is closely positioned alongside key competitors Google, Databricks, and Microsoft, all recognized for combining robust features with strong future potential. This cohort represent the leading edge of generative AI innovation and deployment forÂI.

In Gartner 2025 Innovation Guide for Generative AI Technologies, IBM is recognized as an Emerging Leader in the AI Knowledge Management Apps / General Productivity quadrant. This recognition affirms IBMs strategic impact and strong product capabilities in enterprise productivity through generative AI, especially with solutions like watsonx.ai, watsonx Orchestrate, and the Granite models.

Generative AI Tools

Big news IBM just leveled up!

We’re re an Emerging Leader in the 2025 Gartner Guide for Generative AI in not one, not two, but three categories:

- Model Providers

- AI for Productivity

- GenAI Engineering

Serious tech, with a dash of fun. Let’s build the future securely & at scale.🧠🤖

#AI

Architecting AI: APIs for Agent Integration AI agents demand robust API strategies. Choosing between REST, GraphQL, or Anthropic's Model Context Protocol (MCP) is vital for performance, scalability, and intelligence. The Challenge: Autonomous AI agents must interact with external systems, maintain context, and use tools. Traditional APIs often weren't designed for these complex, stateful interactions. API Options: RESTful APIs: Stateless HTTP. Simple, widely adopted for basic CRUD operations and stable systems. Pros:Simplicity, native HTTP caching. Cons: Can be rigid, leading to over/under-fetching; statelessness limits multi-step context. GraphQL: Single endpoint with client-defined queries. Offers precise data retrieval and flexibility. Good for varying data needs or frequent API evolution. Pros: Efficient data fetching, self-describing schema. Cons: Query construction can be complex for AI; requires custom caching. Model Context Protocol (MCP): By Anthropic. An action-based protocol for AI agent interaction. Supports memory, dynamic tool use, and multi-step tasks. Ideal for advanced, autonomous agents needing contextual awareness. Pros: Built for context, standardizes agent-tool interaction. Cons: Newer ecosystem, fewer production tools; potential infrastructure overhead. Making the Choice: Use REST for simplicity, quick implementation, or legacy/stable system integration. Opt for GraphQL when agents need flexible, precise data from evolving or complex data sources. Consider MCP for advanced agents needing persistent memory, dynamic tool use, or multi-agent coordination. Conclusion: While REST and GraphQL serve many data access needs, MCP aims to unlock next-level AI agent intelligence by enabling sophisticated, context-aware interactions. The right API choice is foundational to an AI agent's success. IBM Master Inventor Martin Keen's YouTube video offers deeper API insights. https://bit.ly/43yMkTD

The Agents Are Coming.
REST? GraphQL? Anthropic’s new MCP?

Choosing the right API is critical for AI agents to think, act, and remember.

REST = simple
GraphQL = flexible
MCP = context-aware superpowers

🎥 Martin Keen breaks it mok loop down: bit.ly/43yMkTD

#AI #genai

RAG was search with style.
Agentic RAG is cognition at scale.

→ From static prompts → dynamic planning
→ From surfacing facts → synthesizing insights
→ From tool use → tool fluency

This isn’t just smarter answers.
It’s simulating thought.
You’re not scaling queries—
You’re scaling *intelligence*.

When should you use RAG? RAG is an AI technique that retrieves information from an external knowledge base to ground LLM on accurate, up-to-date information. Here are some reasons you might want to use RAG: 𝟭. 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 The knowledge of LLMs is limited to what they were exposed to during pre-training. With RAG, you can ground the LLM to the latest data feeds, making it perfect for real-time use cases. 𝟮. 𝗜𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗽𝗿𝗶𝗲𝘁𝗮𝗿𝘆 𝗱𝗮𝘁𝗮 LLMs weren't exposed to your proprietary enterprise data (data about your users or your specific domain) during their training and have no knowledge of your company data. With RAG, you can expose the LLM to company data that matters. 𝟯. 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 LLMs are not accurate knowledge sources and often respond with made-up answers. With RAG, you can minimize hallucinations by grounding the model to your data. 𝟰. 𝗥𝗮𝗽𝗶𝗱 𝗰𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗼𝗳 𝗟𝗟𝗠𝘀 RAG applications allow you to rapidly compare different LLMs for your target use case and on your data, without the need to first train them on data (avoiding the upfront cost and complexity of pre-training or fine-tuning). 𝟱. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘁𝗵𝗲 𝗟𝗟𝗠 𝗶𝘀 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝘁o RAG applications let you add or remove data without changing the model. Company policies change, customers' data changes, and unlearning a piece of data from a pre-trained model is expensive. With RAG, it's much easier to remove data points.