By now, a lot of people would have heard how an MIT report says that 95% of AI initiatives have failed. Looking purely at that headline, it looks like AI is a bad ROI, and people should stop trying to get it going in their organisations.
This is the furthest thing from the truth: Now is not the time to disengage from AI tech.
However, it IS time to properly understand what AI offers, when to leverage it, where to deploy it, and how to secure it.
It’s a huge topic, so let’s cover some of that in the rest of this blog!
“What Normalisation Are You Referring to?”
The “AI bubble” is not like the Dot Com bubble, nor any other bubble.
It’s not running on debt, hype-coins, or subprime mortgages. The major players – Microsoft, Google, Meta, Amazon – are all profitable through multiple revenue streams. Even the pure AI operators like OpenAI & Anthropic are being funded by deep-pocketed investors, not by banks handing out risky loans.
It’s not a bubble at all, and therefore not going to pop. It’s more like an overfilled helium balloon and it will instead calm down, deflate a little, and continue to exist in the background, hovering in the corner of a giant room after a child’s birthday party…
The normalisation that is going to happen is when people realise that their prior excitement about LLMs as the replacement of all things and greatest revenue driver of all time actually isn’t true. They’ll flip the lens around to examine AI, LLMs & GenAI as productivity enhancers instead, and then the new initiatives are going to be heaps more successful, and start being treated as just another tool that effective workers will be using to drive productivity.
About GenAI, LLMs, and Productivity Growth
Here’s another thing about GenAI, and LLMs in particular: They are a statistical machine designed to use historically trained data to predict what’s going to happen next. It’s a giant well of un-lived experience and a “knowledge” base that has been carefully curated based on previous learnings.
One day, I really need to do a post about the difference between “information” and “knowledge”, and how the concept of a website called a “knowledge base” is just fundamentally wrong…
If it helps, think of librarians in a closed stack library… Closed stack libraries don’t let you walk in and pick up any item you want. Instead, they have librarians that know enough about the books in the stack to give you an overview on any related topic, and maybe to even loan you the actual book. These librarians make your search faster, but it is still you who needs to do the reading, the deep thinking, and the application of this knowledge. All that execution is still your job.
LLMs (and all AI) are similar. They should be leveraged as a new tool to speed up information gain, knowledge creation & execution. It’s about productivity growth, eventually leading to revenue growth.
“Great, Show Me How”
Sure! Here’s 3 steps to normalising AI
1. Flip the Narrative
Stop doomsaying and nay-saying on AI. It isn’t here to replace people, and LLMs being a statistical machine isn’t a bad thing. AI is here to offload execution so humans can focus on what we do best: Assessment, Critical Thinking, and Creativity.
Nobody shames a plumber for using a pipe-bending wrench, they just appreciate the job was done faster and cheaper.
2. Identify where it helps
A recent (Feb 2025) analysis of Claude conversations found that
57% of usage suggests augmentation of human capabilities while 43% suggests automation
Find those areas of automation & augmentation in your organisation through curiosity driven exploration. Don’t guess or dictate. Actually map out where your people spend time and effort. Then you can pick the right tool to support it.
LLMs are good at turning messy human processes & information into structured, codified, units of execution. So focus on use cases where those pre-requisites exist.
Other times, your AI needs aren’t ChatGPT or Claude. Sometimes your AI needs are classical models, or ML solutions like classifiers.
The real skill isn’t in using AI everywhere – it’s in knowing where, how, and when to use it.
3. Start small, prove value
Don’t force workflow overhauls. If people are forced to change the way their work before they see value, they’ll resist the change.
Instead, meet your people where they hang out and show quick wins. Slip AI into existing tools and workflows and show value that make their current habits easier.
Small iterative improvements compound over time, just like interest in a savings account. That always results in longer term gains.
In Closing
I have intentionally not discussed why others have stumbled – that wouldn’t be flipping the narrative.
Instead, I’ve shared a proven 3 step playbook that will help normalise the use of AI in your organisation: Shift the conversation, find where it will actually help, and finally prove value with small, compounding wins.
If you’re on this journey – or if you like help spotting where AI can deliver real impact – reach out. Whether it is brainstorming quick wins, or sharing what we’ve already deployed, SRC Innovations has a library of proven & in-production AI solutions that fit into real workflows.
Let’s not look to disrupt for the sake of disruption. Instead let’s look for how we can leverage new tools to deliver value & productivity – quickly and consistently.