(also, we overdelivered by 67% on the same budget & timeline)
First off, let’s be clear. Efficiency is about using less inputs to gain the same amount of outputs.
Productivity is about achieving more outputs with the same input.
If you look at it with that understanding, you’ll immediately see why chasing efficiency is…. well, unproductive. Why save a few dollars, when you could leverage that to gain outsized value?
If you’re focused on reducing your inputs, and maintaining the same output, then you’re not growing. You’re maintaining your current position. You’re in the rat race, where you’re running faster and faster just to keep up. You’re stagnating. And that’s not a pleasant place to be.
LLM based AIs do not automatically result in improved efficiency
The assumption that “having AI” increases efficiency shows a fundamental misunderstanding of how LLM based AI work. Investing in AI to “save time” is a trap.
Just because AI can automate things, and thus reduce the time needed to do things, that doesn’t mean its increasing efficiency. It’s just another form of created automation that improves efficiency on some things, but inevitably leads to a new asset that needs to be maintained.
LLM based AI by itself cannot find efficiencies. It is not creative, it can’t think, it can’t understand the big picture. Years of research have proven that creativity is needed to find new efficiencies in problem spaces – LLM based AI does not have that creativity.
So what CAN it do?
LLM based AIs are an enabler to increase productivity.
They enable you to get drudgery done faster so that you can move on to the actual value generating work. This is especially obvious in the software development world where there are big impacts being seen everywhere.
LLM based AI are extremely good at extracting context out of something that exists and generating more things within that context. Which is fundamentally what writing code is about (cue the angry gnashing of teeth).
Developers & the innovation tax
When developers are told to go build something that will generate value, they can’t just go and do it. They’ve suddenly got to deal with a wave of overhead.
In the pursuit of efficiencies, they need to:
- ensure the new system & features comply with policies
- find the company approved tools, generators, and patterns and then figure out how to use them
- utilise scaffolds and leverage frameworks that support good development practices
- use modern deployment methods that result in additional code for testing and infrastructure
- document everything to support ongoing operations & knowledge transfer
So time passes as the developer aligns with generators, guidelines and guardrails before they can really progress on the new features.
And those generators that might have helped the developers get started faster? Those are another asset that the company has to maintain.
Engineers! Not developers
Here’s the thing. Software Engineers want valuable outcomes. Software Developers just write code. For the first time, it’s actually easy for engineers to focus on engineering and deliver outcomes. For too long, they’ve spent more time discussing the appropriate way to do things and writing code, rather than just delivering on those valuable outcomes.
These LLM based IDEs make that possible by getting the repetitive, statistically similar tasks out of the way.
That enables your engineers to actually shine and to be PRODUCTIVE. They won’t need to seek ways to be efficient so they can get to the things that deliver value. They can just step straight to engineering solutions that deliver value.
Speaking of (over) delivering on value…
Which brings me back to my byline. SRC Innovations – using its Augmented Intelligence Delivery Model – had a 9 week project to build a client an MVP application that required many typical e-commerce functionalities, as well as integration with on-device hardware.
We completed their MVP in about 5 weeks, and kept on giving them MORE features so that at the end of that 9 weeks, they’d gotten ~67% more revenue generating features than they’d planned for in their MVP.
It meant some of their musings about “if we had this, we could do real world marketing campaigns”, became “Hey! We now have this feature that we can use for this style of marketing AND to make money!”
SRC delivered improved productivity which led to faster revenue generation.
What else more is there to say?
Talk to us
(Actually I’ve been told that there is one more thing to say) If you wanted to increase your PRODUCTIVITY. If you want a significantly faster path to value. If you wanted to use your current resources to get more value. Talk to us.
Our methodology is proven and has over delivered in the real world.