Avery Lane

The package ties process mining, build agents, App Engine and AI Control Tower into one loop. Pricing was not announced.

SAP says Joule Work is live for 110,000 of its own staff. No pricing or general availability dates were announced.

Paid plans get GPT-6 Sol and free users get GPT-6 Luna. For Enterprise workspaces, admin settings decide when staff see it.

GitHub Copilot, OpenAI Codex and LM Studio already run inside them. Central management through Intune is not ready yet.

The beta connector reads Workday's shared Iceberg tables directly and keeps Workday as the system of record. Access is read-only.

Pipelines, Catalog and SQL are now one product built on Apache Iceberg and R2 storage. You pay when data is ingested, processed or queried, and egress is free.

Putting calls, meetings and chat in one place saves time only if the company also changes how it uses them. Otherwise it just adds another app to check.

Most developers now use AI tools, and half of professionals use them every day. Fewer trust the output than a year ago. That gap is what IT policy has to manage.

Salesforce reports nine in ten sales teams use AI agents or expect to within two years. Agents working from a messy CRM just make mistakes faster.

Most CRM platforms can handle a mid-size company's basic needs. The right choice comes down to fit with your other systems, how your team sells, and what it costs in year three.

Staff are already using AI tools, approved or not. A strategy is how IT decides which uses to back, which to block and who answers for the results.

Real-time analytics answers questions while there is still time to act on them. It also costs more to build and run, so the first job is deciding which questions need it.

Most analysis mistakes aren't about math. They come from asking the wrong question of the data, or reading more into a result than it can support.

Data management covers eleven areas in the industry's standard framework. A mid-size company doesn't need all of them at once. Here is where to start.

The EU's AI literacy rule has applied since February 2025, and enforcement powers start in August 2026. It asks for less than most vendors' training pitches suggest.

An AI audit asks what data went in, how the system was tested, and who is accountable when it's wrong. Three frameworks now give companies a common way to answer.

Big data no longer means running a Hadoop cluster. It means cheap object storage, an open table format, and whichever engines you want to query it with.

Employment for young workers in AI-exposed jobs has fallen behind older peers. For junior developers, the response is to get good at the parts of the job AI handles badly.

Every layer that hides complexity also hides problems. The trick is knowing what's underneath when something goes wrong.

An audit of 965 commercial codebases found vulnerable open source in 86% of them. Open source isn't the problem. Not knowing what you're running is.

Traditional BI put IT between the business and its data. Modern BI removed that bottleneck and created a new problem: five dashboards with five different revenue figures.

Every open source license lets you use the code. They differ on what you owe in return, and that difference decides whether a library is safe to ship inside your product.

RPA software clicks and types through applications the way a person would. That makes it quick to deploy on old systems, and fragile when screens change.

Europe's Data Act turns data from connected products into something users can demand, and makes leaving a cloud provider cheaper. Companies selling into the EU have obligations either way.

Containers ended most "works on my machine" problems. Before rolling Docker out, check one thing: companies with 250 or more staff or $10 million in revenue need paid Docker Desktop seats.

A CI/CD pipeline turns every code change into a tested, deployable release. It also holds the keys to production, which makes it a target.

Each major cloud now sells one place to pick models, connect company data, build agents and monitor them. The differences are in model choice, integration and where your data already lives.

A warehouse holds cleaned data built for reporting. A lake holds raw data of every kind for whoever needs it later. Most companies end up needing both.

Gartner expects AI agents to resolve 80% of common customer service issues without a human by 2029. Getting there depends less on the AI than on the systems it can reach.

Sales teams spend a large share of their week on work that isn't selling. Automation tools target that time, one task at a time.

Business VoIP has merged with collaboration suites. The shortlist now depends on which suite you use and how central calling is to your business.

UCaaS replaces the office phone system with a cloud service that also runs meetings and chat. For most companies, the main decision is whether to use the collaboration suite they already pay for.

The average company in Zylo's 2026 index spends $55.7 million a year on SaaS across 305 apps. AI features are now the fastest-growing source of surprise charges.

Your SaaS provider keeps its service running. Keeping your data recoverable after a deletion, a ransomware attack or a bad sync is still your job.

Deployment tooling has settled into a few layers: a CI/CD platform to build and test, a deployment method to ship, and a release strategy to limit the damage when something goes wrong.

Big data projects fail more often from vague goals than from technology. The ones that pay off start with one decision the business wants to make better.

Drupal 10 sites have two months of support left. The upgrade is easier on a site that already deploys through a clean, automated pipeline.

NLP is the field that taught computers to work with human language. Chat assistants are its most visible product, but most enterprise uses are quieter.

No single tool tests everything. A working DevOps pipeline usually combines five or six, each chosen for one kind of test.

Continuous testing is the part of the pipeline that decides whether fast delivery is also safe delivery. Here's where each kind of test belongs.

Every report, dashboard and AI project depends on getting data out of business systems and into one place. There are four main ways to do it.

Many contact centers still run on platforms bought a decade ago. With AI agents arriving, the gap between old and new systems is widening fast.

Every team picking its own DevOps tools created sprawl. Platform engineering puts the common pieces behind one self-service platform.

Machine learning finds patterns in past data and uses them to predict new cases. Most business AI that isn't a chatbot is still this kind.

IT teams that automate the repetitive work get time back for projects. The best starting points are tasks done every week, by hand, the same way.

Once orders arrive from a website, marketplaces, stores and sales reps at the same time, spreadsheets and the e-commerce platform's back office stop keeping up. That's the job an OMS takes on.