From Prototype to Production: The Four Keys to AI Success

Many businesses struggle to move past the AI prototype stage for these 4 simple reasons.

Creating an initial AI prototype can be a quick process and can feel like a significant achievement, but many organizations fail to advance the prototype into a reliable system that delivers meaningful business outcomes. 

Integrating AI into your existing systems requires a combination of disciplined software engineering and business process design.

You must account for a range of factors including unpredictable inputs, context handling, compliance guardrails, unreliable integrations, and organizational change management.

Here are four concrete strategies for improving the chances of success with your AI project:

1. Establish a defined vocabulary - You need to provide the AI specific definitions of terminology to prevent confusion with other terms and acronyms in your industry-specific language. Many businesses mistakenly assume that AI will intrinsically understand their unique operational context.

   
   

2. Clean and complete your data - Identify and remove duplicate information, populate missing fields, and standardize formatting. Remember to standardize data for the context it will be used in, such as ensuring phone numbers don't include a "+1" prefix if that format is not desired as part of an AI’s output.

   
   

3. Require structured outputs from your AI - Any handoff of information between an AI process and another system should utilize a predefined structured format, such as JSON.

   
   

4. Narrow down the capabilities of your AI process - Instead of creating open-ended prompts, limit your AI to specific tasks it was designed for. Start with core business needs, prove them out, and expand capabilities incrementally.

Sometimes you need to slow down in order to move faster. With AI, software and business process engineering principles are more important than ever. 

Remembering this is the key to ensuring your AI project’s success.