Bridging the AI Proof-of-Concept to Production Gap: A Technical Leader's Guide

Cowritten by John B. Cizmar

The Reality of AI Implementation 

"POC is easy. Production is difficult." This straightforward observation from industry experts captures a fundamental challenge in enterprise AI implementations. While proof-of-concept (POC) demonstrations often generate excitement and showcase potential, the journey to production deployment presents a complex set of challenges that technical leaders must navigate carefully. 

Understanding the POC-Production Gap 

A POC is a tool to help you articulate your vision and reveal real-world complexities that cannot surface in controlled demonstrations.  A gap forms between POC and production isn't just about scale—it’s about realizing a vision to achieve your goals.  AI POCs tend to demo very well but getting them into production leads to many challenges like you are going to need more hardware, or integrations are more complex than expected, or you discover that your models are underperforming and need to change. 

The major blocker is a failure of purpose and approach.  Companies are looking at AI to solve problems versus having problems and looking at how AI can assist in the solution.  While you can approach it from both angles, you need to have a well-grounded business case that will weather the challenges that come when you move forward. 

Common Challenges in Production Implementation: 

Infrastructure Requirements 

Earlier this year, we had a customer abandon their AI initiative when we determined that their cost of computing resources on an ongoing basis was going to increase 20 times. Infrastructure requirements must be addressed and must include understanding source consumption patterns, which vary significantly based on model complexity and workload. Scaling is another consideration that must be addressed, accommodating for growing data volumes and demand fluctuations.  

Tip 1 – Adapt the infrastructure pattern like how your organization already uses to maximize economies of scale and reduce latency. 

Performance Issues 

For a system to be adopted successfully, it must outperform the legacy system or process it is replacing in some measurable way. Going from a POC with a few transactions to a production load is a momentous change in the characteristics of the system. A simple inference taking 20 seconds in your POC might demonstrate the functional nature of the system, but in production this is not going to be acceptable.  These types of performance challenges require considerations for response time degradation, where model inference times increase under heavy loads, affecting application performance and user experience. As AI workloads can be resource-intensive, understanding and planning for sufficient allocation is important. Additionally, concurrent user impact can cause bottlenecks and should be planned for. 

Integration Complexities 

Integrating AI solutions into existing systems introduces several complexities that can require careful management and planning to mitigate. Older infrastructure may not support modern AI technologies as seamlessly as we would like. Effective data management is essential to ensure that the right data is processed, routed, and utilized by AI models effectively.  

The Critical Role of Testing 

One of the most significant insights from industry practitioners we have talked to is the importance of comprehensive testing. A common point you will hear from people with implementation experience and a point we strongly agree with is "If you're not testing, you can't scale." 

Essential Testing Components: 

1 - Automated Testing Infrastructure 

Proof of concept to production requires testing to ensure stability and performance. A foundational component to testing needs to include unit testing and integration tests that verify the correctness of individual components and their integrations. Aligning to your performance benchmarks (you should have them) as well, testing the system on some level of load is crucial to assess how the solution handles expected and peak workloads.  

2 - Data Validation 

Data validation is a critical aspect of deploying AI to production. You need to validate data quality, including input data to ensure that prompts are efficient, and any training data is accurate and reliable. Output validation is required to confirm that any generation from the models is meeting expectations. Handling edge cases and implementing error recovery mechanisms are essential to maintain robustness under unexpected conditions.  

3 - User Acceptance Testing 

User acceptance testing (UAT) is vital for AI solutions. Real users should validate workflows, outputs, and usability. If you have time and budget, a user experience assessment should be conducted to confirm that the AI solution meets end-user needs and expectations. We cannot stress enough the importance of testing, under real conditions. The insights it provides into how the AI model will function in a live environment is vital. 

Creating a Production-Ready AI Deployment 

Success in production requires a systematic approach to addressing common challenges: 

Business Case

Best Practice: Develop a business case that addresses the investment and expected benefits.  This will establish the vision but not necessarily the how. 

Challenge: Many companies do not have metrics and an understanding of the costs and benefits of specific business outcomes. 

Solution: Establish and vision.  Break the vision down into stepwise goals and be knowledgeable about what metrics you currently have. 

Infrastructure  

Best Practice: Consider production infrastructure from the start 

Challenge: AI models are super greedy. They'll use all the RAM, and maybe not efficiently. 

Solution: Detailed resource monitoring and optimization. 

Data Management 

Best Practice: Establish robust data governance early 

Challenge: It's always about the data and the quality of the data and what's going into the models 

Solution: Validation of data quality with automated tests 

Model Management 

Best Practice: Version control for models and data.

Challenge: Every time we released a new version we can see drastic differences in output. 

Solution: Model performance comparison against “golden” queries and prompts. 

Security and Compliance   

Security is a critical in the enterprise and should not be an afterthought. You’ll need to consider access control (ACLs) and identity management to ensure that the outputs are properly scoped to the correct entitlement of the user making the inquiry. Where is the data going and how is it being transmitted are always a concern in the enterprise. There are plenty of early adoption horror stories that highlight lack of thoroughness in security and compliance. Your organization will need to understand and decide on data security and privacy controls to safeguard sensitive information.  

AI Scaling  

To successfully scale an AI solution to production (and post launch), it requires a well-architected infrastructure that can handle increased workloads and data volumes without compromising performance, with RAM being a typical bottleneck. As we mentioned earlier, AI models can be super greedy. Careful testing and tuning of models to dial in its resource needs is, unless you have infinite budget, strongly encourage.  

KPIs for Production 

Visit MC+A to more information regarding KPIs for Production.