AI Adoption & Strategy

While the resurgence of TDD in the age of Large Language Models offers a promising solution to the challenges posed by AI-generated code, there's another layer to this trust equation: the tests themselves.

AI-powered code review catches what manual review misses. Learn how automated tools handle security scanning, coverage gaps, and consistency enforcement, freeing senior engineers for higher-value decisions.

We all know that AI's potential is enormous, yet the way to win with AI is to embrace customization and deep integration. It is for this reason that I believe (and I’ve been running tech services companies for 25 years) that the entire software services industry is about to change for the foreseeable future.

We all know the adage: “Those who cannot remember the past are condemned to repeat it.”

Boost your engineering team's performance with the help of ChatGPT!

Every AI coding session rebuilds context from scratch. A persistent knowledge layer of domain models and retrieval tooling encodes your intent so it doesn't.

Learn how building a custom LLM can be the key to your AI Transformation

Let’s cut to the chase: Yes, you can absolutely generate code using generative AI models.

For decades, pair programming has been a much discussed aspect of Extreme Programming (XP): two developers working side by side with the goal of writing better code than either could alone.

Read about navigating AI integration with the goal of driving business impact. Unlock the potential of Artificial Intelligence (AI) and Large Language Models (LLMs) with Stride's expertise.

When companies talk about AI transformation, they usually focus on the tools: agents, chatbots, automation platforms, but the real shift is in how we work, who does the work, and how teams are structured. It’s flattening hierarchies, evolving roles, and exposing which organizational models can actually keep up.

Individual AI skill lifts individuals, not throughput. Why mob elaboration and mob construction turn one engineer's practice into how the whole team works.

Read why Stride's CEO, Francisco Martin, believes LLMs will transform the way most companies do business, and the way everyone builds software.

Stop AI "pilot purgatory." Discover why a disciplined crawl phase is essential for scaling AI agents, mapping autonomy boundaries, and ensuring enterprise ROI.

Boards are asking what the AI budget actually bought. Run every line item through three questions — owner, metric, review workflow — before 2027 planning locks.

Token cost efficiency is not a procurement problem. It's a design problem. Scoped context, agent decomposition, caching, exit conditions, model routing, instrumentation. These are structural decisions that compound over time. Get them right early and costs scale sublinearly with capability. Get them wrong and no pricing discount will save you.

Learn where we sit now on the LLM hype curve after less than a year of new developments in the wake of GPT-4's announcement.

In the ever-evolving landscape of software development, there are few constants. Yet, as the tech world becomes increasingly enamored with Large Language Models (LLMs) and their myriad applications, an old stalwart of software engineering is poised for a renaissance: Test Driven Development (TDD).

Unlock the potential of Large Language Models (LLMs) by blending product thinking with user-centric strategies to create unique and intuitive digital solutions.

Artificial Intelligence is reshaping industries at an astonishing pace. From automating repetitive tasks to enabling smarter decision-making, AI promises massive efficiency gains and competitive advantages. But here’s the hard truth: most organizations aren’t ready to adopt AI responsibly or effectively.

6 Lessons from Agile’s Hard-Won Transformation, for Today’s CTO.

Adoption hit 90%, but adoption isn't proof of return. Team AI upskilling measures delivery on every PR, so you can show leadership what AI actually returned.

Your AI governance policy stops where Microsoft stops. Learn how to enforce MCP governance across every vendor with a gateway and runtime monitoring.