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AI and Machine Learning

Engineering guidance on applying AI in software development, from model selection to governance, for technical leaders evaluating real AI use cases.

Latest in AI and Machine Learning

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Hand-picked by our editors and refreshed quarterly.

About this category

This hub collects our reporting and technical guides on AI in software development, written for engineering leaders who need to separate what works from what is still marketing noise. Some pieces compare tools head to head, like our chatbot capability comparison, while others walk through implementation, such as the six-step AI build process. We also cover the harder questions around adoption, including the ethics of AI development and what it means for how teams ship code. The goal is to give you enough technical grounding to make a build or buy decision, not to sell you on AI for its own sake.

If you already know which slice of AI you are dealing with, the subcategories go deeper. AI Applications by Function covers practical ai use cases by department and workflow. AI Strategy and Governance is for leaders responsible for policy, risk and compliance around model use. Generative AI and LLMs focuses on the current wave of language and image models. Machine Learning and Data Science stays closer to the underlying algorithms, from supervised learning to neural networks.

When the question moves from research to staffing, that is usually where we come in. Teams call on our AI development services when they need engineers who have already shipped production machine learning systems, not just prototypes. Others bring us in earlier, using AI consulting services to scope a project before committing to an architecture. For companies short on in-house data science capacity, we also supply AI engineers who can embed directly into an existing team and work against its own backlog and release schedule.

If you are new to this hub and trying to decide whether AI is worth building in-house, start with the six-step AI build process. It lays out what a real implementation requires before any code is written, which makes the build or buy question much easier to answer honestly, and it gives useful context for everything else linked above.

Frequently asked questions

  • How do we start using AI in our software development process without a big rewrite?

    Start with a narrow, measurable use case rather than a platform overhaul. Pick a workflow with clear inputs and outputs, like ticket triage or code review suggestions, and wrap it around your existing pipeline. Our six-step AI build process walks through this scoping work in more detail. Expanding scope after one working integration is far safer than trying to redesign everything upfront.

  • What are realistic AI use cases for an engineering team right now?

    Code review assistance, log anomaly detection, test generation, document search over internal knowledge bases and customer support triage are all in production today across different companies. The common thread is that a human still reviews the output before it matters. Full autonomy is rarer and riskier than most vendor pitches suggest, so scope your first use case to something with low blast radius if it fails.

  • Which AI model or tool should we standardise on?

    It depends on your workload, not on which model tops a benchmark that week. Latency, cost per token, context window and how well a model handles your specific domain language matter more than headline accuracy scores. Our chatbot capability comparison and roundup of ChatGPT alternatives are good starting points, but plan to run your own evaluation against real prompts before committing.

  • Will AI actually replace our developers?

    No, but it changes what junior work looks like. AI handles boilerplate, scaffolding and first-pass documentation well, which shifts developer time toward review, architecture and debugging. Our piece on AI’s impact on jobs covers this shift in more depth. Teams that treat AI as a force multiplier for existing engineers, not a replacement for hiring, tend to get better outcomes.

  • How do we manage the risk and governance side of AI adoption?

    Set a written policy before your teams start using AI tools informally, because that gap is where most incidents happen. Cover data handling, model provenance, acceptable use and who signs off on production deployment. Unresolved technical issues also matter here, see our note on unresolved problems in AI. For governance frameworks and policy templates, go to AI Strategy and Governance.

  • Where do I look if my question is about infrastructure or testing rather than AI itself?

    This hub stays focused on AI and machine learning specifically. For deployment pipelines, containers and infrastructure decisions, see Cloud and DevOps. If your concern is validating AI-driven features before release, QA and Testing covers test strategy in more general terms, and Data and Analytics covers the data pipeline work that usually sits underneath any AI system.