Imagine this: you’ve invested $400,000 into an AI initiative, and your CEO wants to know the results. You’ve promised data driven AI implementation across the org. And while you know this is attainable, your team has been at it for months and you’re not much closer than where you started. The data is clean, governed, and structured. The model is ready to go. But the business stakeholders aren’t using it. Why?
Because what was produced doesn’t help the business. No one on the team knew how to translate business problems into data platforms. As a result, your CEO wants to know where those agentic insights are and why $400k didn’t get us to actionable data.
If you’ve been in this situation, or you never want to be in this situation, this is for you.
What You Actually Need to Transform
Everybody knows AI can be revolutionary to a company’s output and results, but not a lot of people know how to make that happen. In April, Gartner reported that “Only 28% of AI use cases in infrastructure and operations (I&O) fully succeed and meet ROI expectations, while 20% fail outright, according to a Gartner Inc. survey of 782 I&O leaders in November and December 2025.” (Gartner, April 2026)
At this point, it often isn’t a question of whether you’ll invest. The question is, will it be worth it?
The determining factor isn’t the dollars spent or the tools selected. It’s whether your data team can translate business problems into data platform decisions, and if they can steer AI through those processes once it hits production.
The Real Implementation Challenges
The team doesn’t listen to the business. If your mantra has been, “Well the data is accurate and that’s all that matters,” you’ll be staring down that budget situation before you know it. AI initiatives often fail because they address the concerns of the data team instead of the concerns of the business. If you build a team that understands the business processes and can apply the reality of the business to the data, then you have a recipe for success.
Institutional knowledge is disregarded.
Human nature is only so predictable, and it varies by human.
How is AI going to know that John hadn’t updated that spreadsheet in 3 months, so he’s probably backdated insights that are less reliable than prior data? Don’t say that once we automate John’s spreadsheet, the agent will get it perfect. There’s a reason it hasn’t been automated yet.
You need a human eye to see that reality. If you end up letting AI run the show without a human to guide it, all of that institutional knowledge is lost. That means your data’s ability to reflect reality is lost with it.
No Human in the Loop
Leaving AI to its own devices already has a poor track record. Hour long outages of core cloud platforms, entire production databases wiped in an instant, token bills that make that $400k AI budget vanish in a month. It’s now the job of data leadership to guard against those possibilities and ensure that there is human monitoring of AI infrastructure. Someone needs to be tracking token usage, monitoring model drift, and checking for output quality. AI has never been a “set it and forget it” product at an enterprise level. If that risk isn’t managed with human awareness, you risk AI going Amelia Bedelia on your data.
Shadow AI
Your team is already experimenting with AI, and commonly those experiments are without oversight. Even if you haven’t signed a contract, AI is already hidden in your stack somewhere. If you want visibility into what tool your team is using and how they use it, you have to communicate. Selecting a vendor is important, but it’s more important for you to know your tools are safe and reliable.
How Snowflake Helps
Snowflake’s platform is built for this. Three capabilities matter most:
Snowflake Cortex and Snowflake Intelligence bring AI directly into the data warehouse you already have, eliminating the integration work. AI can live in an environment you already trust. Rather than slapping a third-party model onto your data, Cortex Code and Snowflake Intelligence work where your data already lives, in a platform that your team is already familiar with. Their institutional knowledge naturally applies.
Snowflake’s unified data governance layer makes sure that the guardrails your team has already put in place extend into AI workflows automatically. Access policies, data quality rules, and lineage tracking are set up at the beginning of the process so AI can scale. This brings human oversight into AI tooling, and Snowflake makes that easy.
Native integration with structured and unstructured data means AI agents can touch all kinds of data from all over the business. Structured rows and tables and unstructured call transcripts and contracts. Those agents can reach into all corners of business documentation, helping the team pinpoint the business processes that matter most and integrate them with agentic workflows.
Case Study: Delta Defense, Membership & Sales Operations
Delta Defense’s business leaders were waiting hours or days for critical data. Like most companies, they were dependent on centralized reporting and ad-hoc analyst support. Using Snowflake Cortex-powered natural language queries and a multi-agent anomaly detection framework, 7Rivers helped the team achieve a 90%+ reduction in time-to-insight and enable self-service analytics for 4x the users. Snowflake and 7Rivers brought the data to the business, instead of forcing the business to chase the data. Happy business, happy data team.
Most organizations come to us somewhere between the Foundation and Insights stages. They’ve built the data infrastructure foundation, maybe completed a Snowflake migration, and now the C-suite wants to know why the AI thing isn’t happening. Oftentimes the answer is that the platform is ready, but the team isn’t ready to leverage it. Those insights are still out of reach.
That gap is where the Action stage begins. For Delta Defense, moving from passive dashboards to AI-driven, role-specific insights delivered straight into Slack was about implementation. Snowflake Cortex and a multi-agent framework were the tools. The work was building the knowledge alongside the solution so the team could own it after we left.
The destination is something 7Rivers calls the Augmented Enterprise. This is where human judgment and AI capability come together. It happens when data leadership makes the deliberate decision to build their team’s capability in parallel with the platform.
The AI Landscape is Moving Fast
But the organizations that will report measurable AI ROI in the next 18 months aren’t the ones dumping cash into the newest fanciest tool. They’re the ones who invested in building their team’s ability alongside their platform’s ability. That is the kind of investment that scales.
AI is already doing data engineering work. What AI can’t do is understand your business, hold your institutional knowledge, or make the organizational decisions that determine whether a model succeeds or fails in production. That’s a human job.
Ready to build your team’s AI capabilities from the inside out?
