When starting your AI journey, the wrong first question is: “How can I fit AI into our business?”
That approach leads to shoehorning technology into workflows where it doesn’t belong, producing pilots that look impressive but fail to transition into day-to-day operations.
The better strategy is to treat AI as another useful tool in your box. By starting with your biggest pain points, you can decide which problems truly need AI and which can be solved faster, cheaper, and more reliably with other technologies.
Know the Range of AI Technologies
AI is not just ChatGPT. It is a broad set of technologies, each with its own strengths:
- Computer vision for images, video, and camera feeds
- Stable diffusion models for creating synthetic images or video
- Large language models for working with natural language
- Anomaly detection and predictive models for spotting patterns in data
- Recommendation models for suggesting products, movies, or music based on users past choices
- Fraud detection models for spotting unusual activity in banking or payments.
… and so on.
Where Traditional Methods Still Win
Document processing is a good example. Instead of using AI vision to “read” a PDF as an image, structured parsing can directly extract the embedded data, often more accurately, much faster and most importantly much cheaper than AI solutions.
But AI can still add value on top of this, such as enabling natural language queries about a document or converting drawings into 3D renders. The key is to let the outcome dictate the tool.
The Real Lesson
AI is not a universal fix. Misaligned deployments lead to fragmented tools, low adoption, and wasted budgets. Real value comes when AI is integrated into workflows with clear purpose, not when it is forced into them.
