Traditional Data Pipelines Were Built for One Direction and Production AI Needs Feedback Loops

Most enterprise data pipelines were designed to do one thing well: move data from point A to point B. That linear architecture works for traditional analytics. It does not work for production AI, where the system needs to learn from its own outputs, absorb new data sources on the fly, handle schema drift without breaking, and feed results back into the pipeline to improve over time. The gap between what most organizations have built and what production AI actually requires is architectural.

Shivali Naik is a Senior Technical Architect at Snowflake, where she leads post-deployment AI architecture engagements with Fortune 500 clients across insurance, healthcare, and regulated technology industries. With experience spanning dozens of enterprise AI implementations, she is one of the architects Snowflake deploys on its most complex, high-stakes engagements where regulatory constraints, PII exposure, and multi-system integration make standard approaches unworkable. Her role sits at the point where strategy meets production: after the deal closes, she is the person responsible for making the architecture actually function at enterprise scale. Across nearly every engagement, she encounters the same foundational gap.

"A lot of organizations have a streamlined approach with their data pipelines, where data comes in and goes out in a traditional format," Naik says. "But with AI, there's so much feedback loop and how you can train your data and ingest it again to make informed decisions. That type of architecture is where we are lacking with a lot of customers." Naik has formalized this distinction between static, directional pipelines and feedback-loop-capable architectures into a diagnostic framework she applies at the start of every enterprise engagement to assess whether an organization's data infrastructure can support production AI at all.

Clean data and ownership come first

Naik describes a consistent pattern across her client base. Organizations want to implement AI, but the data foundation underneath it is not ready. Data is not clean, governance is not in place, and ownership is not defined. "Creating a model is not the most difficult part," Naik says. "The main key points are people and data. When I say people, I mean who owns what. If something goes down, who should they be calling at 2 am so the whole infrastructure stays tight?"

That ownership question becomes acute when multiple systems are integrated. When a failure occurs across two connected platforms, diagnosing which side caused the problem involves significant back-and-forth before anyone reaches a resolution. "There has to be proper ownership and governance in place," Naik says. "This person with this role owns this part of the data. Anything that fails in this integration, this person has to go in and fix it. That's still the hardest piece."

Test the failure modes before production

For Naik, the POC is where failure modes should surface. "You have to lock in how you're building your failback mechanism," she says. "If there's schema drift happening, how is your model going to handle that and still keep running? Those things need to be taken care of initially when you're doing a POC rather than waiting for production data and then failing in production."

She also pushes for compressed build-crawl-run cycles. "It doesn't have to be six months. You can have one month of development and testing, then test whatever is wrong and move to the second phase with more users." Speed is fine as long as the coverage holds. "We're identifying issues faster," Naik says. "It depends on how your test cases are and how comfortable you feel. If they're not covering enough, take your time and build that variety."

Skipping best practices guarantees a rebuild

Many of the data deployment frameworks that would prevent architectural rework already exist, but Naik finds that most clients do not follow them. "A lot of clients that I work with, none of them follow any of the industry best practices," she says. "Then we come in as architects and we have to guide them through what becomes a rebuild of the whole system."

For organizations handling PII and PHI, the trust barrier is even steeper. Naik sees insurance companies and healthcare organizations hesitate to engage with AI at all because they fear data will be shared with external models. The architectural answer is to bring the model to the data instead of sending the data out. "There are so many guardrails in place now," Naik says. "The data doesn't go to the model. That's how you can have the governance framework around it."

The divide comes down to sequence. Teams that get the architecture right up front avoid the rebuild the others walk straight into. "If you follow basic frameworks when developing your architecture, even when something is added on, it's not going to be a major redo," Naik says. "It's just going to be a small tweak, and the pipeline is still going to run."

For enterprises now on their second or third attempt at deploying production AI, Naik's diagnosis is consistent: the failure was architectural, and it was preventable. The organizations that got it right did so not because they had better models, but because they worked with architects who identified the structural gaps before the first line of production code was written.