Artificial Intelligence | Thread Categories

Has AI become part of your daily work or is it still mostly talk?

I keep hearing teams talk about being “AI-powered,” but in practice it often feels uneven. Some people use AI constantly for decisions, analysis, or automation, while others barely touch it or don’t trust the outputs enough to act on them. In a few cases, AI helps speed things up but the final call still comes(Read More)

I keep hearing teams talk about being “AI-powered,” but in practice it often feels uneven. Some people use AI constantly for decisions, analysis, or automation, while others barely touch it or don’t trust the outputs enough to act on them. In a few cases, AI helps speed things up but the final call still comes down to human judgment like it always did.

Curious how this looks in your world. Where has AI genuinely become part of daily workflows, and where is it still more of a talking point than a real shift? What made the difference between adoption and resistance?

Last reply by Ishan. 0 1 20 3 days ago

Subscriber

HitEsh

2 days ago

Anyone else feel like BI dashboards look great but don’t really change decisions?

seen this across teams again and again. We build dashboards, polish metrics, align KPIs… and yet, in meetings, decisions still come down to gut feel or last week’s Excel sheet. On paper, BI is “live” and “data-driven.” In reality, half the dashboards are opened only during reviews, some metrics are tracked but never acted on,(Read More)

seen this across teams again and again. We build dashboards, polish metrics, align KPIs… and yet, in meetings, decisions still come down to gut feel or last week’s Excel sheet.

On paper, BI is “live” and “data-driven.” In reality, half the dashboards are opened only during reviews, some metrics are tracked but never acted on, and everyone has a slightly different interpretation of the same number.

I’m curious how this plays out in your teams. Was there a moment where you knew BI was genuinely helping decisions?

Last reply by Miley. 0 1 31 1 week ago

Subscriber

Priya Nair

December 18, 2025

What is the best visual technique to uncover hidden weaknesses in an AI model?

As the rollout expands, you’ve accumulated millions of interaction logs showing how the AI models behave across different scenarios, user types, geographies, and operational conditions. While the overall performance metrics look strong on paper, leadership is increasingly concerned about subtle issues that don’t appear in dashboards: inconsistencies in how the model makes decisions, rare but(Read More)

As the rollout expands, you’ve accumulated millions of interaction logs showing how the AI models behave across different scenarios, user types, geographies, and operational conditions. While the overall performance metrics look strong on paper, leadership is increasingly concerned about subtle issues that don’t appear in dashboards: inconsistencies in how the model makes decisions, rare but high-impact misclassifications, and sudden performance drops triggered by specific data patterns. The dataset is huge, highly unbalanced, and influenced by real-world noise such as seasonal traffic spikes, evolving user behaviour, and model drift. You’re tasked with performing a deep investigation to determine where and why the AI might be behaving unpredictably.

Last reply by Caleb Grey. 0 2 60 4 weeks ago

Subscriber

Vidhi Shah

December 10, 2025

Is AI Making Analysts More Valuable or Replacing Their Work?

The impact of AI on data roles is no longer theoretical it’s happening in real workflows every day. Modern AI systems can pull metrics, run comparisons, detect anomalies, and even generate full narrative explanations without human intervention. Business teams are already asking tools like ChatGPT, Gemini, and enterprise AI agents directly for insights that once(Read More)

The impact of AI on data roles is no longer theoretical it’s happening in real workflows every day. Modern AI systems can pull metrics, run comparisons, detect anomalies, and even generate full narrative explanations without human intervention. Business teams are already asking tools like ChatGPT, Gemini, and enterprise AI agents directly for insights that once required an analyst’s time and expertise.

This shift is reshaping what “analysis” even means.
Routine tasks cleaning data, building dashboards, running SQL queries, summarising trends are becoming automated. Analysts are now expected to operate at a more strategic level: validating insights, understanding business context, influencing decisions, and designing data frameworks rather than manually producing outputs.

But it also raises a very real concern:
If AI keeps getting better at the doing, where does that leave the human analyst?

Last reply by Shahir. 0 1 59 4 weeks ago

Subscriber

Xavier Jepsen

November 25, 2025

Will conversational AI replace dashboards as the primary interface for analytics?

The modern BI experience is shifting from building dense dashboards to asking questions in plain English: “What changed in user retention last week?” or “Which product line is underperforming and why?” Tools like ChatGPT, Gemini, and enterprise AI agents now sit on top of data warehouses, offering contextual insights instantly. If conversational analytics becomes the(Read More)

The modern BI experience is shifting from building dense dashboards to asking questions in plain English: “What changed in user retention last week?”

or “Which product line is underperforming and why?” Tools like ChatGPT, Gemini, and enterprise AI agents now sit on top of data warehouses, offering contextual insights instantly.

If conversational analytics becomes the new norm, do traditional dashboards and static reports become obsolete—or do they still serve a crucial role?