Behind the Interface: How Natural Language Is Transforming Marketing Workflows

Enterprises are navigating a complex landscape marked by evolving challenges in privacy, economics and the rapid advancement of AI. Consumer data privacy is no longer just an expectation — it’s a nonnegotiable, the foundation of consumer trust. Economic volatility has pushed companies to do more with less, demanding greater efficiency amid ever-changing regulations. Meanwhile, AI, particularly generative and agentic AI, is revolutionizing data access and decision-making, compelling businesses to adapt quickly.

While 2023 was the era of discovery for generative AI, 2024 was the year of experimentation, proof of concept and development of new gen AI tools for customer service and marketing. At first, gen AI may have seemed like an easy, cost-effective catchall solution for myriad use cases, but we’ve since discovered that the reality is far more nuanced. Gen AI can be complex to build and use effectively and expensive to implement, and it's evolving at an unprecedented pace. But for enterprises that are able to meet these challenges, 2025 will be the year of applied AI, where natural language interfaces (NLIs) will become more prevalent in everyday marketing workflows, democratizing data access and helping accelerate business outcomes.

“The hottest new programming language is English," OpenAI founding member Andrej Karpathy famously Tweeted. The way we interact with data has changed radically. Historically, we moved from paper records to digital data and data storage systems, then to SQL-enabled data access, which is powerful but requires technical expertise, preventing marketers from having direct access to data. In this process, marketers had to use natural language to make requests in IT ticketing systems. IT would then interpret the requests, translate them to SQL and complete them. That could take days, weeks or even months as the backlog grew, since these tickets required manual attention.

Then came the drag-and-drop click interface, the one we are most familiar with today in martech. These interfaces, usually referred to as “no code,” are relatively self-serve for marketers, but they require an understanding of technical data structures. For instance, what is the name of the “customer” table in our systems? What is the name of the column that indicates the amount spent in the last 12 months? Marketers are relatively self-sufficient in this system, but they can’t use their natural language and business semantics for direct access to data they can easily understand. 

Now, natural language interfaces promise that marketers can both be self-sufficient and use natural language. They won’t have to rely on other teams or wait for the result of a manual action. That’s a game changer for nontechnical users. NLIs promise to transform marketing workflows for campaign planning, decision-making, analysis and optimization. They can facilitate ideation, audience-targeting, content selection, channel decisions, reporting and a lot more. Though the pace and priority of this shift will vary from enterprise to enterprise, the trend is clear: NLIs are poised to become the new interface of choice.

Part of the appeal and efficacy of an NLI is its simplicity: a text box where you type your request. But mastering NLIs will still require education. Today, model performance depends heavily on the quality of the request or prompt. Even natural language interaction will demand refinement to achieve satisfactory results. And successful gen AI adoption will take time. It's crucial to explore and adapt early and often. Practitioners will have to get comfortable with the uncomfortable changes to their ways of working and learn early to efficiently leverage the new technology that will inevitably alter the marketing landscape. 

However simple the user interface, underneath that simplicity lies great power and complexity. Just as Google's search engine relies on a sophisticated algorithm, NLIs require a robust data strategy and integrations on the backend. We’re learning that just plugging your interface into an LLM won’t be enough. It won’t meet the enterprise security requirements, nor will it perform. 

Enterprise organizations are recognizing that their AI success is contingent on data success.

An organization’s AI and interface are only as good as the data it has access to, and the tools that are integrated to amplify the possibilities offered by that data — without compromising data governance, compliance and security requirements. 

Snowflake’s AI Data Cloud empowers marketers in this evolving landscape. Snowflake offers a comprehensive solution to facilitate the development of gen AI capabilities, including the ability to power and enhance these next-generation interfaces. 

Snowflake for AI brings AI to governed data to run analytical workflows on both structured and unstructured data, develop agentic apps and train models— all with minimal operational overhead. You can easily accelerate unstructured data processing, gen AI apps and model development with a single platform made of modular, pre-integrated AI services, tools and compute infrastructure. 

With Snowflake Cortex AI, you can quickly and easily analyze unstructured data and build generative AI applications using fully managed LLMs, retrieval-augmented generation (RAG) and text-to-SQL services and enable multiple users to use AI services with no-code, SQL and REST API interfaces. It’s also efficient, allowing you to skip the infrastructure management with serverless AI to analyze unstructured data and build data agents and other AI apps. And Snowflake protects the value of your data and models with industry-leading security and unified governance trusted by thousands of organizations.

Power Digital Marketing

“We’ve embedded Snowflake Cortex into our marketing intelligence platform to enable natural language querying directly within our client and internal workflows. What this unlocks is a real shift — from analysts writing SQL to strategists and marketers instantly retrieving multi-source data with plain language. We’re seeing a ~30x improvement in speed to insight and have already saved over three full workweeks across just a few hundred queries. As large language models become more deeply integrated into enterprise data stacks, this approach will define the new operating model for service businesses.”

— John Saunders, VP of Product, Power Digital Marketing

GrowthLoop

“GrowthLoop integrated a natural language interface — powered by Snowflake’s Cortex AI — to give marketers and operators true self-serve access to their data. Whether it's a performance marketer launching a new A/B test, a lifecycle marketer finding lapsed customers or a marketing ops lead reducing ticket volume, natural language removes the technical barrier. It accelerates time to insight, speeds up activation and ensures data remains governed and consistent — all directly on Snowflake.”

— Anthony Rotio, Chief AI Officer, GrowthLoop

Dataiku

“Dataiku enables data experts to build and deploy sophisticated AI, from intricate customer segmentation to predictive analytics. Leveraging Snowflake Cortex, these complex insights can now be made intuitively accessible to marketing teams through natural language. This crucial link translates deep analytical power into real-time personalization, dramatically improving customer engagement and business outcomes.”

— Jed Dougherty, Head of Platform Strategy, Dataiku

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