AI isn’t magic. It’s not a silver bullet. And it 100% isn’t going to “revolutionise your business overnight”. Even if the LinkedIn hype and newest batch of tech-bros are trying tell you otherwise.
But this doesn’t mean it’s not powerful and full of potential. Of course it is. In fact, when applied properly, at the right scale, with realistic expectations it can quietly become your most valuable teammate. No hype, just honest results based on leveraging AI in ways that actually make an impact on your business.
The problem with AI hype
We’ve all seen it: LinkedIn posts filled with “AI transformation” promises, start-up landing pages boasting “AI-powered everything,” and tech influencers betting big on the next miracle algorithm.
The reality? Many AI projects fail. Not because the tech isn’t good, because it is, but because the expectations are broken from the start. AI is treated like a product you can buy off the shelf, rather than a capability you develop with intention, data, and clear goals. You need skilled people (I know, people, who knew 🤣) with the vision, experience and expertise to deliver.
What realistic AI looks like
Realistic AI doesn’t mean thinking small. It means thinking smart and with real purpose. Realistic AI skips past “ChatGPT can do everything, so lets implement it into out workflow now” and considers where the real value is in adopting AI into a business.
Here’s what that looks like in practice:
- Start with a problem, not a model. AI is a tool, not a strategy. The best projects begin by deeply understanding a business problem that’s worth solving. That’s different for every business and often for the departments withing a business.
- Use your data, not someone else’s magic. Pre-trained models are impressive, but their real power is unlocked when combined with your own context, data, and domain knowledge. Providing your data as context, adding your knowledge at the inference stage to augment the answers that are retrieved via the LLM (Large Language Model).
- Embed AI into workflows, not just interfaces. Real gains come when AI quietly makes decisions better, faster, or more consistently and not just when it looks impressive in a demo. Think about agentic AI here where the systems you build are given the agency to reason, make decisions and perform tasks autonomously.
- Measure success like any other new tech you adopt. ROI, efficiency, employee or customer satisfaction. If your AI project isn’t positively impacting at least some of these, it’s simply not working.
Realistic AI isn’t boring, it is just considered
As this post seeks to outline, AI implementations should be considered and realistic, but that doesn’t mean you wont be getting your hands on some exciting new tech.
- It doesn’t mean ignoring new tools. Realistic doesn’t mean boring. Foundation models, agent-based systems, and synthetic data can all play a role in your project. And they really should, I just urge you to do so with a clear purpose and goals.
- It doesn’t mean going it alone either. The ecosystem around AI is growing fast, but pick your partners carefully. Look for those who prioritise outcomes over “wow” factor.
Your first steps toward real AI value
If you’re thinking about bringing AI into your organisation, here’s a simple, realistic checklist:
1 Find one process that’s repetitive or inconsistent
For your first AI project this doesn’t have to be line of business or even customer facing. Better if you can identify a process that is time consuming for a human to carry out but that represents a low-risk when moving to AI automation.
Processes like data transformation or simple reporting are good starting points. Or even retrieval and augmentation of non-critical data would be a good fit too.
2. Figure out what data powers it
Next you need to understand the data that drives the process. Where is it stored, what type is it, how much do you have, what are the policies and permissions around its use. Is there a cleansing or deduping step that’s needed before you can run AI against it?
How can you be sure that AI is getting the right kind of results from this data. Have you benchmarking in place or real results to use later to validate results.
3. Can we augment this with automation or prediction?
With the process and data understood the next step is to think about where AI can do a better job for you. You can probably build a solution that does the automation of the process, but how about going further. AI can predict, infer and augment as part of your new new workflow. Have a think about what you could achieve.
4. Pilot. Measure. Iterate
Before committing significant resources to a production-ready AI solution, it’s often wise to begin with a Proof-of-Concept. An AI Proof-of-Concept helps de-risk future development by uncovering unknowns and potential challenges early, ensuring a smoother and more informed full-scale implementation.
It’s never a good idea to hide away for weeks building the everything-solution. Just start small, measure success (or not), and then iterate it to improve.
5. Only scale if it works
AI projects take many forms ranging from full digital products to AI agents designed to automate tasks across systems. You should only scale up your project if you see significant gains at step 4. If you don’t. Stop!
If your solution is having an impact then by all means start to scale it up in terms of throughout and scale it out to more parts of the business. As you introduce more processes you’ll learn that the realistic AI feedback loop starts to work. Start small, be realistic in your expectations, pilot and only scale up when you have proven that the AI you are implementing really is “transforming your business”….but obvs not overnight 🤣
That’s it. No moonshots. No buzzwords. Just practical wins, one realistic use case at a time.
Final thought: The quiet power of AI
The best AI doesn’t shout. It’s the kind that lives in the background of your business answering questions, flagging risks, nudging decisions. Slowly but surely making your business smarter and hopefully freeing up more time for your teams to what they are best at.
So forget the miracles. Focus on the mechanics. That’s where the real value lies.