Stop Wasting Money on the Wrong AI Tools
TLDR: Most teams are still running every task through one massive AI model. It’s slow, wasteful, and costly. Two recent advances fix that: model routing (picking the right tool for the job) and memory-efficient optimisation (making big models lighter to run). These changes make AI faster, cheaper, and usable without needing new infrastructure or deep technical help. This post explains what it means for non-technical teams using AI for content, admin, or internal automation.
Why It Feels Like AI Isn’t Helping
You bought the tool. You paid for the licence. You tried using ChatGPT or Copilot for team tasks, but it’s:
- Slower than you expected
- Costing more than it saves
- Still needing heavy editing or manual review
You start wondering if the problem is you. And to be honest, it probably is...
Or if you just don’t “get” AI. Which to be honest..you probably do not...
Gotcha!! It’s neither!!
The truth is, most businesses are using AI the wrong way. Fact. Not because they’re careless, but because no one told them how to size the tool to the task. That’s what model routing and memory optimisation fix.
1. Model Routing: Using the Right Model for Each Task
Think of model routing like choosing the right-sized ladder. You don’t need A 5 star michelin chef to do a family pizza night.
AI works the same way now. Instead of sending everything to one heavyweight model, modern systems can choose which model to use:
- A small, fast model for document tagging or pulling out short answers
- A medium model for writing first drafts or summarising transcripts
- A large model for high-risk, high-context tasks like reviewing contracts
This is called model routing. It’s the quiet logic layer that decides which model runs what so you don’t have to.
The result?
- Faster turnaround
- Lower costs
- Less overkill (no more using GPT-4 for a two-line email draft)
For content, internal comms, and client service teams, this means you can automate more without wrecking your budget or waiting 30 seconds per task.
2. Memory-Efficient Optimisation
These are making large models use less power.Even the biggest models are starting to slim down. Because....you pay, and you are going to be paying big time in the future.
A new technique has cut memory usage by up to 75%. That means:
You don’t need a $10,000 server to run complex tasks
You can use more AI features on standard laptops
Costs drop, especially if you’re using AI across your team
This doesn’t change your workflow obviously, but it changes what’s possible in your stack.
For internal tools, low-code builds, or vendor platforms that previously lagged, this is the difference between usable and abandoned.
Why It Matters If You’re Not Technical
You don’t need to understand the technical details. But you do need to understand what’s changed.
If you’re using AI in these ways:
- Drafting client emails
- Generating social content
- Writing policies or SOPs
- Tagging documents
- Summarising reports or transcripts
...you’ve probably hit a wall. Either it’s too slow, or too generic, or just doesn’t fit into your workflow.
These two improvements, routing and memory reduction, change that. They’re the backend fix that makes AI practical and you better get to using them.
What You Should Do Now
You don’t need to build a model. You just need to ask smarter questions about the tools you’re using.
- Map your use cases
What tasks are you trying to automate? Break them into small, medium, and complex. - Choose tools that use model routing
Many AI builders, like Claude, Writer, or even internal platforms, now support this logic. - Ask about memory efficiency
If you’re buying enterprise licences or working with dev teams, this matters. Ask whether the tools are optimised — or if you’re paying extra for wasted compute. - Book support that knows how to size your AI stack properly
That’s what our No Code and Low Code AI Implementation support is for.
You’re Not Wrong. Your Tools Are Just Oversized.
If AI feels like a letdown, it’s probably because you’ve been sold a hammer for every job when what you needed was a toolkit.
Model routing and memory optimisation finally bring that toolkit within reach. You need working workflows, faster outcomes, and AI that fits your business not the other way around.
That’s what we build.
FAQ
Q: Do I need to switch tools to use model routing?
A: Not necessarily. Some tools like Claude, Writer, and enterprise setups with OpenAI now support routing in the backend. You just need to know whether it's enabled.
Q: Is this only for developers or tech teams?
A: No. These changes help non-technical users the most, especially those building workflows in Notion, Zapier, Airtable, or Google Docs.
Q: Will this save me money?
A: Yes. Routing reduces unnecessary compute costs. Memory-efficient models lower infrastructure requirements. Together, they reduce the cost of doing more with AI.
Q: How do I know if my AI tool is using memory-efficient optimisation?
A: Check product release notes or ask the vendor directly. If you’re running tools on local hardware, you’ll notice less lag and resource use.
Q: Can I implement this myself?
A: You can if you know what you’re doing. But most teams benefit from tailored guidance. That’s what our AI Implementation offer supports, designing fit-for-purpose system