AI
AI for growing businesses: where it actually helps
Skip the hype. A practical look at the places AI tends to earn its keep in small and mid-sized businesses.
For a growing business, the useful question about AI isn’t “how do we use AI?” It’s “which of our problems is AI genuinely good at?” Start from the problem and the right answer is sometimes AI, sometimes simple automation and sometimes neither.
Here are the areas where AI tends to make a real difference.
Recommendations and personalization
If you have a catalog of products, content or services, recommendation systems can help people find what’s relevant to them. Examples include similar products, items that go together and content based on what someone has engaged with.
This is one of the most mature uses of machine learning. It works best when you have reasonably structured data about your items and some signal about what people like.
Making sense of unstructured information
Emails, support tickets, documents, call notes and form submissions are full of useful information that’s hard to search or report on. AI can help to:
- Categorize and route incoming requests.
- Summarize long threads or documents.
- Pull structured fields such as names, dates and amounts out of free text.
These tasks are tedious for people and often good enough for AI, especially with a human checking the edge cases.
Supporting campaigns and content
AI can help draft, adapt and personalize outreach and marketing content at scale. The key word is help. The best results come from treating AI output as a first draft that a person who knows the audience then reviews.
Smarter automation
Traditional automation follows fixed rules: if this, then that. Adding AI lets a workflow handle inputs that don’t fit neatly into rules, like working out what a free-text inquiry is about before routing it.
Where to be careful
- Accuracy. Some decisions need to be right every time. Use AI to assist with them, not to make them alone.
- Data. Know what data you’re sending where, especially customer or patient information.
- Maintenance. Models, prompts and integrations need ongoing attention as tools and data change.
- Cost. Some AI features carry per-use costs that grow with your usage. Model this early.
A sensible first step
Pick one workflow where your team spends a lot of time reading, sorting or matching information. Map how it works today. That map will usually show whether AI, simple automation or a better process is the right fix.
We build AI-powered applications and recommendation systems, and we automate the workflows around them. If you have a use case in mind, or just a hunch, we’re happy to talk it through.