The promise of generative AI is clear: it can dramatically accelerate how teams work. Yet many companies, from early-stage startups to established enterprises, are struggling to capture this value. While some teams ship new features in days instead of weeks, others spend months debating which AI model to use or navigating approval processes.
For small companies, the barriers often come down to uncertainty. When you’re running lean, every tool choice matters. Teams can get stuck evaluating endless options, worried about committing to the wrong platform. Meanwhile, without clear guidance, different departments might adopt conflicting tools, creating future technical debt.
This hesitation is particularly ironic because small companies stand to gain the most from AI adoption. A marketing team of two can now produce content at the scale of a much larger organization. A solo developer can generate test cases and documentation that would typically require a dedicated QA team.
The real game-changer? AI lets startups compete directly with industry giants. A small e-commerce store can now generate SEO-optimized product descriptions at the scale of Amazon—without hiring a dedicated content team. A boutique SaaS startup can use GenAI-driven chatbots to provide 24/7 customer support that feels as responsive as a Fortune 500 help desk. These aren’t future possibilities—this is happening today, and the companies that embrace AI early are the ones closing the gap fastest.
Large organizations face a different challenge. Their size and regulatory requirements mean any new technology needs extensive vetting. A simple tool adoption that might take days at a startup can stretch into months of security reviews, compliance checks, and stakeholder approvals.
This caution isn’t entirely misplaced. Enterprise data security and regulatory compliance are serious concerns. But many organizations have overcorrected, creating approval processes so stringent that meaningful innovation becomes nearly impossible.
The result? Speed is outsourced to smaller, more agile competitors:
By the time slow-moving companies act, they’re not just behind—they’re losing market share, talent, customers, and revenue. AI adoption isn’t a theoretical risk, it’s a business risk. The biggest danger isn’t AI itself—it’s standing still while competitors move forward.
The path to successful AI adoption requires balancing speed with thoughtful implementation. Here’s what works based on real-world observations:
Instead of trying to transform everything at once, pick a specific problem where AI can show immediate value. For example:
When evaluating AI tools, focus on these key criteria:
If a tool checks these four boxes, test it. If not, move on. The biggest mistake isn’t choosing the “wrong” AI tool—it’s spending months debating and choosing nothing.
This framework helps cut through analysis paralysis.
Success with focused pilot projects builds confidence and creates internal advocates. A single successful implementation—whether it’s cutting development time by 30% or doubling content output—provides the evidence needed to expand adoption.
For small companies, the priority should be quick experimentation in areas where resources are stretched thin. A small marketing team might start with AI-assisted content creation, measure the impact on output and quality, then expand to other marketing functions based on results.
For enterprises, the key is creating safe spaces for innovation within governance frameworks. Some effective approaches include:
The technology landscape will continue evolving rapidly. Forward-thinking companies are already seeing results: faster development cycles, more efficient operations, and better customer experiences. The gap between these early adopters and those waiting on the sidelines grows wider each month.
Starting small doesn’t mean moving slowly. It means being deliberate about where and how you implement AI, measuring the impact, and scaling what works. The companies that thrive will be those that find this balance between thoughtful implementation and decisive action.
AI adoption isn’t a luxury—it’s now a competitive necessity.
If you’re exploring GPUs and their role in AI or machine learning, check out Modal’s GPU Glossary. It’s a simple, no-nonsense guide to GPU basics—great for beginners or anyone needing a quick refresher.
From CUDA cores to TensorRT, the glossary explains key terms clearly and concisely. Perfect for cutting through the noise and getting the essentials.
Take a look—it’s worth it!
After using Classic Mac OS at school for some time, it took me several years to return to the Mac. It was 2004, and I was happy to discover Mac OS X 10.3 Panther. It was completely new and better. Been using it until today (not Panther, though) and will, for the coming years. Some behind the back usages of other OSes, but let him who is without sin cast the first stone.
Today we are announcing support for a new proposed DNS standard — co-authored by engineers from Cloudflare, Apple, and Fastly — that separates IP addresses from queries, so that no single entity can see both at the same time. Even better, we’ve made source code available, so anyone can try out ODoH, or run their own ODoH service!
They say old habits die hard. That must be the reason why so many of my developer colleagues like using Vim (mostly) to develop these days. I must admit I never understood the why of it, but if you’re ok with it, use whatever makes you happy.
This year’s AWS conference, re: Invent, is a fully virtual one. If you never heard of re: Invent, it’s the conference where AWS announces what they’ve been working on, and what the competition has to catch-up with (the list is long).
I’ve been waiting a long time for something like this to come along. It shouldn’t take that long for it to become available in my usual supplier. Someone’s getting one for Christmas, now that the Arduino interest is starting to pick up some steam.
For a while, I was trying to get back to publishing some content in this space. As things start to come together on other parts, I finally found some time to overhaul the site. After some changes, I settled with Hugo. Pretty fast, simple.
For the theme, I’ve made some modifications to the Kiera theme by Daniel Saunders.
As you can easily see, I’ll have to check how to display links in excerpts. Soon.
The high level stucture of Cello projects is inspired by Haskell, while the syntax and semantics are inspired by Python and Obj-C. Cello isn’t about Object Orientation in C, but I hope that with Cello I’ve turned C into something of a dynamic and powerful functional language which it may have once been. Although the syntax is pleasant, Cello isn’t a library for beginners. It is for C power users, as manual memory management doesn’t play nicely with many higher-order concepts. Most of all Cello is just a fun experiment to see what C would look like when Hacked to it’s limits.
A nice experiment taking C to a whole other level.
