Why AI Applications Struggle Getting to Production

At Tecton, we’ve observed a consistent pattern working with companies trying to operationalize AI. Organizations invest heavily in AI initiatives, building strong data science teams and developing promising models, yet they struggle to reach production. The issue isn’t model accuracy or lack of technical talent – it’s the complexity of productionizing data for AI.

The Data Science-Production Gap

Here’s what we see happening: A data science team develops an impressive model in their notebooks. The accuracy looks great, stakeholders are excited, and everyone is ready to deploy. Then reality hits. Moving to production means completely rebuilding their entire data engineering pipelines. What worked in Pandas needs to be re-architected for real-time serving. The estimated engineering work stretches from weeks to months, and momentum stalls.

This scenario plays out so frequently that it’s become predictable. Companies invest heavily in data science teams and AI initiatives, only to discover that the path to production is blocked by a massive data engineering challenge. The result? The bulk of AI projects never make it to production.

The Hidden Data Challenge

Working with ML teams from F100 to digital natives, we’ve identified the fundamental blocker: the vast difference between experimental and production data infrastructure. During development, data scientists can craft sophisticated data engineering operations (like time-window aggregations) and handle point-in-time consistency for training data in notebooks, taking their time to perfect each calculation. But translating these same transformations into production-ready systems becomes a massive engineering undertaking.

We see this pattern repeatedly: What starts as a few lines of Python in development becomes a six-month engineering project in production. Teams suddenly need to build distributed systems to handle real-time data streams, ensure exactly-once processing, manage state, and maintain consistency across training and serving.

We consistently see engineering teams discover:

• Data pipelines need complete re-engineering
• Data freshness requirements demand new infrastructure
• Scale requirements force architectural changes
• Training-serving consistency becomes a major challenge
• Engineering resources get overwhelmed

Every time a team solves one data pipeline issue, three more appear. Application teams end up stuck waiting for data pipelines that are months away from being ready. The cycle of delays becomes self-perpetuating. This issue perpetuates as better models require more data engineering iteration.

The Do-Nothing Different Impact 

Let’s be frank: engineering teams could build data pipelines for AI by hand with enough time, effort, and persistence. But the consequences of maintaining the status quo in productionizing AI are far-reaching and costly. When companies stick to traditional approaches, they find themselves trapped in a cycle where data scientists’ work sits in development limbo while engineering teams struggle with growing backlogs of data to productionize.

As project timelines stretch and infrastructure costs mount without delivering value, stakeholder confidence begins to erode. This loss of credibility often leads to reduced support for future AI initiatives, creating a vicious cycle. The true cost lies not just in delayed projects, but in the missed opportunities and competitive advantages that slip away while teams struggle to bridge the development-production divide

A Better Path Forward

At Tecton, we’ve built a platform specifically designed to bridge this data science-production gap. Instead of teams needing to build complex data pipelines for production, we provide a unified platform where all model context (e.g. features, embeddings, prompts, etc) can be easily defined in a notebook from any data source and automatically productionized at scale for low-latency retrieval – with all the data engineering happening under the hood.

This approach eliminates the main blockers to productizing AI:

• Elimination of handoffs and code-rewrites across dev and prod
• Data is consistent across offline/online environments
• No need to build complex data pipelines for production
• Automatic scaling and optimization of computing infrastructure
• Built-in production-grade reliability for mission-critical applications
• Dramatic reduction in engineering overhead and system complexity

Looking Ahead

As companies continue investing in AI initiatives, the ability to move quickly from development to production becomes increasingly critical. We’re seeing organizations that solve this through modern data platforms that dramatically accelerate their AI initiatives.

The path to successful AI deployment requires more than just good models and good people. It requires a platform that can turn experimental data into production-ready systems without massive engineering effort. With Tecton, engineering teams focus on building valuable AI applications, not data plumbing.
Want to learn more about how Tecton can help your team get AI into production? Check out our on-demand webinar “Productionize AI Faster, and with Less Tech Debt” or try out Tecton yourself for free through our interactive demo.