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 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 AI (gen AI) and agentic AI—is revolutionizing data access and decision-making, compelling businesses to adapt quickly.

While 2023 was the era of discovery for gen 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. Plus, it's evolving at an unprecedented pace.

But for enterprises able to meet these challenges, 2025 will be known as the year of applied AI, where natural language interfaces (NLIs) became more prevalent in everyday marketing workflows, democratizing data access and helping accelerate business outcomes.

How We Interact With Data Is Changing

"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 we're 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, NLIs promise that marketers can be both 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 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. But for marketers, the goal isn't just to ask for a new report—it's to finally understand the "why" behind metrics without waiting on a data analyst.

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 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 a large language model (LLM) won't be enough; it won't meet 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 Intelligence: Your Knowledge, One Trusted Enterprise Agent

Snowflake Intelligence, now generally available, changes the dynamic. It is an enterprise intelligence agent that puts insights at your fingertips. Because it connects to your entire data estate, including data from third-party apps like Salesforce and ad platforms, you can ask complex questions in natural language and get verifiable answers instantly.

Enterprise-Ready Intelligence with Snowflake

For teams building custom solutions, Snowflake's AI Data Cloud empowers marketers to choose how they adopt AI. You can start immediately with a ready-to-use agent or build custom AI applications tailored to your specific needs.

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.

And with Snowflake Cortex AI, you can quickly and easily analyze unstructured data and build gen 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.