Most AI tools built for B2B companies fail for the same reason: they were built around what AI can do rather than what the business actually needs to do differently.
The Discovery Process
Before writing a single line of code, we map the workflow. Where does data come from? Who touches it? What decisions get made? Where are the bottlenecks? Which steps are high-judgment and which are mechanical? The mechanical steps are the automation targets.
Common B2B Automation Wins
Proposal generation from CRM data and client questionnaires. Competitive intelligence monitoring and weekly summary. Client onboarding document preparation. Support ticket categorisation and routing. Monthly reporting from multiple data sources compiled into a single document.
What Makes a Tool Actually Get Used
It fits into the existing workflow rather than requiring a new one. It has a clear, immediate time saving. It handles the failure cases gracefully, when the AI produces a wrong output, the user knows how to catch it. And someone in the organisation owns it and is responsible for keeping it working.
The Build Approach
Start with the minimum viable automation. Get it working reliably. Measure the time saving. Extend from there. A simple tool that runs daily beats a complex tool that gets used twice and then abandoned.