Getting an AI proof-of-concept to work can feel like a breakthrough. A stubborn process suddenly moves faster. A model extracts information that once required manual review. A team sees enough potential to start imagining what AI could do across the business.
But when it’s time to move the model into production, the stakes change. There’s a shift from asking questions about whether the AI can work to asking whether it can actually become a dependable part of the business.
That next step was the focus of the third webinar in RapidScale's All systems go AI webinar series, From decision to scale: Deploying and sustaining AI in production. Our experts talked with CTO Domingo Pichardo from onPhase, a RapidScale client, and dug into what happens after the proof-of-concept and the data governance it will require are solidified.
The conversation explored the less flashy, but arguably more important, work of turning AI into something the business can actually depend on: scaling it, controlling costs, building for failure, monitoring performance, and continuing to improve it after deployment.
Part 3 of the webinar assumes you already have a good use case and strong data governance in place. Let’s take a moment to revisit those lessons.
In Part 1, our AI SMEs discussed Finding and justifying the right AI use case. The purpose of a proof-of-concept is to determine if your AI solution is worth operating at scale. That means testing more than output quality.
Before investing in an AI use case, you should be able to answer five key questions:
The biggest takeaway: Before you invest in your next AI initiative, make sure it’s solving the right problem.
In Part 2, we welcomed a special guest from Forrester to talk about Where AI governance meets data governance. There are six requirements that define the new AI governance model:
Once you have the right use case and a strong governance foundation, the next challenge is making AI work reliably in the real world. Here are eight lessons to keep in mind as you move from proof of concept to production.
One of the most useful lessons from the webinar was also one of the least sensational: AI doesn’t always need to replace the existing system.
In the webinar, onPhase shared how RapidScale used traditional intelligent document processing to provide a strong foundation for extracting information from invoices at scale. Large language models (LLMs) were then added to address cases in which data was missing or confidence was low.
Instead of forcing one technology to perform every task, RapidScale and onPhase combined multiple capabilities:
The result was a 61% decrease in the number of documents requiring human intervention, which allowed onPhase to process information more quickly, support greater volume, and reduce operational costs without removing people from the process entirely.
This is a more practical vision for enterprise AI. The goal isn’t automation at any cost. It’s the right balance of speed, accuracy, scale, and human oversight.
Read the case study to learn more about how RapidScale helped onPhase achieve a real business impact with AI-driven invoice automation, AWS-native architecture, and scalable financial operations.
An AI model is part of a broader system, and every part of that system can experience disruption.
A solution that depends on one model behaving perfectly will eventually encounter trouble. Models can become unavailable, latency can increase, providers can impose throughput limits, and a model update can change the quality or structure of its responses.
Production-ready AI needs fallback paths. Depending on the workflow, that may include:
The goal is to design the workflow so that a component failure does not become a business failure.
That principle reflects RapidScale’s unbiased approach to cloud and AI. The best architecture isn’t automatically tied to a single model, platform, or predetermined path. It’s the architecture that delivers the right outcome for the workload.
Traditional application monitoring focuses heavily on operational health: Is the system available? Is latency within an acceptable range? Are requests failing?
Those questions still matter, but AI requires a second category of monitoring.
A model can be available and technically healthy while producing results that become less useful. Data changes, customer behavior shifts, and inputs that were once familiar can become more varied. Costs can also rise quietly as prompts, documents, or context windows become larger.
A production AI system should therefore monitor several dimensions at once:
In the webinar example, the team compared captured invoice information with completed, paid invoices. Customer corrections also became feedback that could reveal where the system needed adjustment. The organization monitored confidence, operating health, scale, and cost as interconnected measures rather than treating AI accuracy as a stand-alone metric.
That feedback loop is critical because AI isn’t a “deploy it and forget it” workload. It must be evaluated and tuned over time.
AI costs can appear trivial at the transaction level. Processing one document may cost only pennies. A few extra tokens may seem insignificant. But at 10 million or 15 million transactions a year, things change. This is why operational AI requires the involvement of both technical and business leaders.
Cloud engineers should examine model selection, payload size, throughput, caching, and architecture.
CFOs and operations leaders will want to see the effect on unit economics, productivity, and total cost of ownership.
Useful AI cost optimization questions include:
These decisions are rarely dramatic on their own, but together, they can determine whether an AI solution remains economical as adoption grows.
Many organizations assume they must replace their existing systems before they can benefit from AI. But this can turn a targeted initiative into an expensive, multi-year transformation program.
onPhase had acquired multiple companies and inherited a mix of legacy, on-premises, single-tenant, cloud, and SaaS environments. Instead of waiting to replace everything, the team built a scalable, serverless capability that could work across several existing products and systems.
This offers an important lesson: AI readiness does not always require starting over.
The better question may be whether your organization can create a secure, well-governed data and integration layer around what you already have. APIs, event-driven services, queues, cloud-native components, and carefully controlled access can make AI useful without forcing a major replacement of core systems.
Modernization can happen incrementally, guided by business value rather than by an abstract need to make everything “AI-ready” at once.
An AI agent cannot overcome inaccessible, unreliable, or poorly governed data.
Before building an agent, organizations may need to address fundamental questions like:
Without a well-designed data layer, even an advanced model will struggle to produce dependable results. With it, organizations gain the flexibility to test different models and approaches as the market evolves.
A well-designed data layer may be more important than the model itself.
That flexibility is increasingly valuable. As AI changes rapidly, the most popular model today may not be the right model for a particular workload tomorrow. But an optimized data layer helps your organization be more adaptable.
AI doesn’t reach a finished state when it enters production. Models change, data changes, costs change, and business expectations change. The solution that performs well today may need a different model, prompt, workflow, or architecture six months from now.
Organizations that create lasting value from AI treat it as an operational discipline. They establish ownership, monitoring, governance, feedback loops, and a regular process for evaluating improvements.
They also remain willing to iterate. A successful team doesn’t become attached to a single tool or assume the first architecture will remain the best one. It measures real-world performance, learns from exceptions, and changes course when the evidence points in a better direction.
There’s no shortage of pressure to “do something with AI.” But urgency shouldn’t be confused with recklessness.
The strongest AI programs move quickly while remaining grounded in business evidence. They begin with a valuable workflow, run a focused proof-of-concept, establish clear success criteria, test production realities, and maintain human oversight where it matters.
Most importantly, they treat AI not as a one-time deployment, but as a capability that must be operated, measured, and continuously improved.
A successful pilot proves that an idea can work. Operationalization proves that it can keep working, at the right cost, under real business conditions.
That distinction is what separates an interesting AI experiment from a capability the business can actually rely on. Getting there means more than just having a successful model. It means having the right architecture, governance, economics, and operating model working together.
Want to go deeper into what it takes to make AI production-ready? Watch From decision to scale: Deploying and sustaining AI in production to hear the full conversation and see how these principles play out in practice.
Already thinking about your own path to production? RapidScale can help you assess your data, infrastructure, governance, security, and operational readiness, then design a practical path forward across the cloud platforms and AI technologies that best fit your business. Explore how RapidScale helps organizations scale AI.