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Google DeepMind just released Gemma 4 12B, a dense multimodal model that strips out traditional encoders entirely. Vision and audio flow straight into the LLM backbone. The result is a model that runs agentic workflows on a consumer laptop with 16 GB of RAM. It ships under the Apache 2.0 license. Model Overview & Access […]
I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address the unique challenges introduced by Large Language Models (LLMs). What I expected
Breaking the Black Box: From Traditional QA to AI Assurance and Governance Read More »
Breaking the Black Box: From Traditional QA to AI Assurance and Governance I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address
Student retention has become a board-level metric for universities, bootcamps, and enterprise learning platforms. Yet many EduTech companies still struggle with fragmented LMS data, unreliable adaptive models, and FERPA compliance issues that slow releases and increase risk. This is where ADLC changes the conversation. An AI-driven software development lifecycle gives EduTech teams a structured framework
Predictive Analytics in EduTech Through an AI-Driven Software Development Lifecycle Read More »
In 2026, enterprise agentic AI has moved from pilot budgets to production commitments. Salesforce is closing Agentforce deals at 29,000 since launch with $800M ARR. Microsoft Copilot Studio has 160,000 organizations running 400,000+ custom agents. ServiceNow has restructured its entire commercial model around autonomous AI tiers. The question is no longer whether to deploy —
Best Enterprise Level Agentic AI Platforms for 2026 Read More »
By the time a company’s legal team finishes drafting its generative AI acceptable use policy, a meaningful percentage of its engineers, analysts, and product managers have already moved past it. Not deliberately. Not maliciously. Just practically. This is the core dynamic of what the industry now calls shadow AI: the unauthorized, ungoverned use of AI
Bain & Company has estimated a US$100 billion market in the US for SaaS companies using agentic AI. The firm said the market is tied to automating coordination work in enterprise systems. The estimate comes from the second report in Bain’s five-part series on the software industry in the age of AI. The report examines
Bain sees US$100 billion SaaS market in agentic AI automation Read More »
Why it matters: How to use AI as a business strategy: real case studies from Klarna, Morgan Stanley, and Unilever, with ROI measurement, governance, and adoption guidance.
How Can You Use Artificial Intelligence As A Business Strategy For Your Organization? Read More »
Two weeks ago at Google Cloud Next ’26 in Las Vegas, Google did something the enterprise AI industry has been dancing around for the better part of two years: it made agentic AI governance a native product feature, not an afterthought. The centrepiece announcement was the Gemini Enterprise Agent Platform, pitched as the successor to Vertex AI
Google made agentic AI governance a product. Enterprises still have to catch up. Read More »
There’s a pattern playing out inside almost every engineering organization right now. A developer installs GitHub Copilot to ship code faster. A data analyst starts querying a new LLM tool for reporting. A product team quietly embeds a third-party model into a feature branch. By the time the security team hears about any of it,
