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Modern.tech is a multi-disciplined technology team that builds highly-functional software products for businesses with complex needs.

09/01/2026

We keep hearing that generative AI is transforming business. MIT's Project NANDA studied more than 300 real AI initiatives and found something different: 95% of enterprise AI investment is producing zero measurable return.

The remaining five percent pull millions in value from the exact same models everyone else can access. Approach separates the two groups, not technology.

Swipe to learn what separates them.

08/18/2026

We've inherited enough half-built projects to spot the pattern in the first meeting. The scope is fuzzy, the timeline was set before anyone knew what they were building, and the plan got traded away early to save a week.

That trade rarely pays off. The week saved upfront comes back as a quarter spent recovering, because the wrong thing shipped first and now it needs to ship again. What looked like momentum turns into a run of expensive corrections, all tracing back to the same skipped conversation.

Planning-first isn't slower. It's the version where you ship once instead of three times, with scope that holds and a launch that actually lands. That's how we build, from concept to defect-free launch. Swipe through the real cost of skipping it.

08/13/2026

Only 19% of software teams reach elite delivery performance. The rest aren't working less hard. They're working inside systems that quietly slow them down.

DORA's 2024 research shows how much distance sits between the top tier and everyone else. Elite teams ship far more often and move from commit to production far faster, and that velocity compounds into quicker learning, faster correction, and more value reaching customers.

What creates the difference comes down to how the work is structured. Small batches that are easy to review and release. Automated pipelines that strip out manual friction. Feedback loops tight enough to catch problems while they're still cheap to fix.

None of that is reserved for companies with hundreds of engineers. It comes from deliberate choices about architecture, tooling, and process, and the technical leadership to hold the line on them.

That's the capability we build with the teams we partner with. If it's on your roadmap this year, let's talk.

08/11/2026

The custom enterprise software vs SaaS decision looks like a budget question. It's a strategy question.

SaaS is the right call for commodity work. Email, payroll, CRM, project tracking. The market solved those problems long ago, and no custom build will beat a mature platform on cost or speed for a generic need.

The math shifts when a tool built for the average customer starts treating your competitive edge as an edge case. Per-seat fees climb with headcount. Manual workarounds creep into the daily routine. A feature you need sits on a roadmap you don't control.

That's the signal to run the numbers over three to five years, not three to five months. For most scaling companies, the cost lines cross earlier than expected, and ownership compounds from there.

Our latest breakdown covers when to buy, when to build, and the hybrid path most teams miss. Read full piece in our blog!

08/04/2026

Everyone bought in on AI this year. The returns tell a different story.

Spending is headed toward $2.5T. Adoption is nearly universal. Yet most leaders are looking at the same line item and finding no revenue gain and no cost cut to show for it. The real cost of AI was never the model or the license. It was skipping the work that makes either one pay off.

A small group got both revenue and cost gains. What set them apart was sequence. They defined the problem, built the data and infrastructure foundation, then deployed. The rest bolted AI onto systems that couldn't support it and called the result a loss.

Swipe through the five numbers that explain the split.

07/23/2026

Missed the headlines this week? We've got you.

Here are the stories we've been reading lately. Swipe through for the keynotes.

Subscribe to the Modern Monthly newsletter and stay ahead of the trends shaping AI and technology.

04/24/2026

Most AI integration projects stall in the same place: the whiteboard.

Teams spend months debating architecture and future-proofing a system that hasn't been built. Meanwhile, the actual integration work never begins.

The assumption driving this pattern is that adding AI to existing software means tearing everything down and starting over. It rarely does. Your existing platform already has what AI needs most. Real production data. Established workflows. Users who understand how the system works.

The real decision is architectural: where does intelligence happen relative to your application? API-based integration gives you flexibility, at the cost of latency. Embedded models eliminate network calls, but shift operational burden onto your team. Event-driven architectures scale well for workflows where AI augments the critical path rather than controlling it.

Each pattern has a place. Picking the wrong one is how organizations end up with lasting technical debt instead of lasting value.

Read full blog here: https://hubs.ly/Q04dkQmx0

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