Stop Chasing AI Hype. Start With Your Bottlenecks.
The conversation around AI in business has gotten noisy. Every vendor promises AI-powered everything, but most businesses don't need a chatbot or a generative AI tool. They need their existing operations to run faster, with fewer errors, and less manual work.
Here's how to identify where AI will actually help your business, and where it's just a distraction.
The AI Readiness Framework
Before implementing any AI, answer these three questions:
- Do you have clean, structured data? AI is only as good as the data it's trained on.
- Is this a repeatable process? AI excels at pattern recognition in high-volume, repetitive tasks.
- Is a human currently the bottleneck? If the delay is waiting for a person to review, classify, route, or approve, AI can often handle it.
High-ROI AI Use Cases for Mid-Market
Based on our implementation experience, these are the AI applications that consistently deliver measurable ROI:
Document Processing: Invoices, contracts, applications. AI can extract data, classify documents, and route them to the right workflow in seconds instead of hours.
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Lead Scoring & Routing: Instead of static rules, AI models can analyze historical conversion data to score and route leads dynamically.
Anomaly Detection: Financial transactions, inventory levels, system performance. AI can flag outliers faster than any manual review process.
Customer Communication: Not chatbots, but AI-assisted email drafting, ticket classification, and response suggestions that keep humans in the loop.
Reporting & Insights: Natural language queries against your data warehouse. Ask questions in plain English, get answers with charts.
Where AI Doesn't Help (Yet)
Be skeptical of AI for:
- Complex negotiations or relationship-dependent processes
- Situations requiring deep domain expertise with limited data
- One-off creative work or highly variable processes
- Anything where explainability is legally required and the AI can't provide it
Getting Started
Pick one high-volume, data-rich process where a human is the bottleneck. Build a proof of concept. Measure the results. If it works, expand. If it doesn't, learn why and move on.
The companies winning with AI aren't the ones deploying the most models. They're the ones deploying the right model on the right problem.
