Every vendor is slapping “AI-powered” on their product. Every LinkedIn post promises AI will revolutionise your business. Most of it is noise.
Here’s what works, based on what we’ve seen helping businesses adopt AI in practice.
Where AI delivers real value today
Not every AI application is worth the investment. These are the areas where we consistently see genuine returns:
Document processing and extraction
If your team spends time manually pulling data from invoices, contracts, or forms, AI can handle this reliably. Modern document AI can extract structured data from unstructured documents with high accuracy, and the cost per document is pennies.
Customer support triage
AI won’t replace your support team, but it can route tickets to the right person, draft initial responses for review, and handle common questions automatically. The key is keeping humans in the loop for anything complex.
Internal knowledge search
Most companies have critical knowledge buried in wikis, Slack messages, and shared drives. An AI-powered search that understands natural language questions and returns relevant answers from your existing documentation is one of the highest-ROI AI investments.
Code assistance
For development teams, AI coding assistants speed things up. Code completion, test generation, and documentation are all areas where the technology is mature enough to trust (with review).
Where to be cautious
Decision-making without oversight
AI should inform decisions, not make them. Any system that automatically takes action based on AI output, such as approving loans, screening candidates, or adjusting prices, needs human oversight and clear accountability.
Customer-facing chatbots (without guardrails)
We’ve all seen the horror stories. If you deploy a chatbot, make sure it can gracefully hand off to a human, has clear boundaries on what it can discuss, and doesn’t make up information.
Replacing domain expertise
AI is excellent at pattern matching and summarisation. It’s poor at novel reasoning and nuance. Don’t use it as a substitute for genuine expertise in your domain.
How to get started sensibly
Identify a specific pain point. “We want to use AI” is not a goal. “We want to reduce invoice processing time by 50%” is.
Start with a pilot. Pick one team, one process, one measurable outcome. Prove value before scaling.
Use existing tools first. Many AI capabilities are built into software you already use. Check what Microsoft 365 Copilot, Google Workspace, or your CRM offers before building custom solutions.
Budget for integration, not just the AI. The model is the cheap part. Connecting it to your data, building the interface, and handling edge cases is where the real work (and cost) lives.
Measure everything. Before and after metrics are the only way to know if your AI investment is delivering value.
So what should you do?
Pick a specific problem that costs your team real time. Build a small proof of concept. Measure whether it helped. The businesses getting value from AI right now are not chasing the bleeding edge. They are solving their own problems first.