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Anahita Tafvizi, chief data and analytics officer at Snowflake. Photo courtesy of Snowflake.
AI has gotten very good at sounding confident.
However, as we all know from listening to some politicians, that does not automatically mean it knows what it’s talking about. Or that it’s not just making up stuff it thinks you want to hear.
A conversation with Snowflake’s chief data and analytics officer, Anahita Tafvizi, which I wrote about this week, gets at why that distinction matters for businesses.
Not all questions are equal. Some can tolerate approximation. Others, like financial reporting, regulatory disclosure, and operational planning, not so much.
If a CFO asks an AI system for Q2 revenue, the answer needs to be accurate down to the penny. Probabilistic responses stop being clever very quickly if you’re reporting financials to your board.
The problem is not that AI moves too fast. It’s that it moves too fast inside systems that were never particularly clear to begin with.
If data is poorly defined or inconsistently documented, and then you throw AI at it, you risk a garbage-in, garbage-out situation on steroids.
Accuracy is not optional
What if the stakes are clinical rather than corporate?
Another piece this week, AI helps doctors spot breast cancer in scans: World-first trial, covered a world-first trial where AI helped doctors detect more cases of breast cancer from screening scans without increasing false positives.
Importantly, the system operated within clear limits, was evaluated continuously, and worked alongside clinicians. Accuracy was designed into the process.
In both stories, it’s clear that the technology works best when its role is clearly defined, and its boundaries are taken seriously.
The closer AI gets to decisions with consequences, the less room there is for confident improvisation.
AI works best when the underlying mess gets cleaned up
Documentation debt highlights a mess that already exists.
AI exposes ambiguity that organizations used to live with.
The work that makes AI reliable happens long before anyone types a prompt.
Just like a relationship, trust comes from clear boundaries and access controls.
A system that fosters better questions trumps one that only delivers speed.
Watercooler links
Some light reading for when you meant to check one thing and somehow ended up five tabs deep.
Shockingly, investors respond positively to growth. Advertising growth, cost discipline, and AI investments are combining into a narrative markets are ready to reward again, even after years of skepticism.
Cut through awareness-day platitudes and focus on how privacy expectations are becoming operational requirements. The message is less about compliance checklists and more about how data practices are now shaping trust with customers and regulators alike.
AI in life sciences lives or dies on whether datasets actually align. This article walks through why harmonization is foundational work, especially in environments where accuracy is not negotiable and errors carry real-world consequences. (See feature articles for more examples.)
A major legal test is taking shape around whether platform design choices can be held responsible for user harm. The outcome could reshape how accountability is defined across the social media industry. Sorry, what was that? Busy scrolling Instagram reels.
What once sounded politically impossible is now being openly debated. Child safety concerns are pushing European policymakers toward more aggressive regulation of digital platforms.
From the Digital Journal Insight Forum
The Insight Forum is Digital Journal's thought leadership platform, offering experts a dedicated space to share their perspectives with our audience across Canada, the U.S. and abroad. Members publish monthly articles showcasing industry insights and what they’re learning and seeing in their space.
An exploration of what happens when AI systems are increasingly trained on outputs from other models, and how that feedback loop can degrade performance and originality over time.
As carbon capture projects scale globally, subsurface models are becoming the basis for investment confidence, regulatory approval, and long-term risk assessment.
A look at how leaders are navigating the tension between being visible contributors and strategic decision-makers, particularly in environments where workloads keep expanding but expectations do too.
Final shots
AI is moving fast, maybe not AGI in 2025 fast, but fast enough that organizations are rightly becoming more protective of their data and how their teams use AI to access it.
As we move into a world where AI mistakes carry a price tag, speed is great, but accuracy is critical. As Frank Robinson said, “close only counts in horseshoes and hand grenades.”
Have a great weekend.
- David