An announcement recently crossed my desk about an upgraded, purpose-built AI (artificial intelligence) analyst and I have so many questions and thoughts. I am guessing at first blush so do you. Let’s take a closer look at this announcement and what this could potentially mean for the analyst community and the market segment.
But, first, let’s be honest: Analysts have always relied on tools. Years ago, it was spreadsheets and databases. Later it became data platforms, predictive analytics, and automated research tools. Now we are entering the era of AI-assisted analysis.
The promise here is obvious. AI can process enormous datasets faster than any human analyst. It can scan thousands of research papers, financial filings, patents, news reports, and industry announcements in minutes. It can synthesize trends, identify correlations, and even generate reports at scale. In theory, this could dramatically accelerate the research cycle.
But the real question isn’t whether AI can assist analysts because as I am seeing it right now change has already happened. The 64,000-dollar question now is whether it will begin to replace the human judgment that has traditionally been at the center of analyst work and all the hype around forecasts and predictions.
As readers of this column know, I’ve spent years calling out what happens when analysts miss the mark. It’s far more than an academic misstep. Corporate leaders make real financial bets based on those forecasts. Factories retool for markets that never arrive. Companies pour money into technologies that never take hold. And the fallout isn’t theoretical. It reshapes jobs, investments, and entire communities.
The concern I have is businesses make key financial decisions that alter the way we live, work and play. So, here’s the question, will this continue to be acceptable in a world where forecasts shape billion-dollar budgets, workforce vision, and technology strategies that define our future?
No Need for Analysts
Meet Ava. She is part of AI Hub at GlobalData, which supports its clients in navigating insights. While Ava isn’t new, she does have a lot of enhancements this year including agentic AI capabilities that enable Ava to reason, plan, and act. She is now an active research partner in the organization, according to the company, capable of managing complex workflows from request to delivery.
Here’s a hot take: Are we saying we no longer need analysts anymore? Surely, Ava is only one wheel in the cog now; but what comes next?
Analysts don’t just collect data. They interpret it. They debate assumptions. They challenge conventional thinking. They call executives, speak with vendors, attend events, and gather context that doesn’t always exist in structured data. That human element matters. At least that is what they tell us. That has been how it has been done.
Could an AI platform really replicate that experience? As I see it, that’s the question we all need to be asking. There are already early warning signs. Matthew McConaughey secured eight trademarks this year alone to prevent unauthorized AI voice modeling and image usage. And he’s not the only one doing it.
The point is pretty simple. These systems can sound and even look like authentic individuals. But as people, would we truly be able to tell the difference? And even more disconcerting, if an AI agent is “recording” or generating those answers, how would we know whether the responses are accurate, manipulated, or entirely fabricated, when we already know AI is not consistently accurate?
I am not talking about a celebrity issue here. This is simply a trust issue—trust in the data itself. I am no longer just questioning the data; I’m questioning how the data was collected in the first place. Because when AI can convincingly mimic a human voice or persona, it begs the question of whether the information is artificial or real. Let alone how it was constructed.
This brings us to the much harder truth: if the inputs are flawed, incomplete, or biased, the outputs will be as well. And this spells disaster for business executives making critical decisions based on what they believe is reliable data.
Feet to the Fire
What if Ava gets it wrong? Many might remember I challenged the big IoT (Internet of Things) prediction that famously fell flat: the hype that we’d have 50 billion connected devices by 2020. I called it out then and I have called it out many more times ever since. On my podcast, The Peggy Smedley Show I regularly invite analysts to defend their numbers and to help us understand what’s behind these forecasts.
But what about Ava? Who do we challenge when the forecast comes from an AI system? Traditionally, analysts put their reputations on the line. A prediction with a name attached to it carries accountability.
With AI-generated insights, accountability becomes less clear and just plain muddy. If the prediction is wrong, where does the blame fall?
• Was it the data?
• The algorithm?
• The prompts?
• The training set?
• The human oversight that approved the report?
When the “author” is a system, responsibility becomes diffuse. And that’s exactly where the danger lies. More questions and certainly no one to ask.
If AI begins producing analyst-grade reports, organizations will need new governance structures around those insights. And let’s face it, confidence in the numbers matters. Executives use analyst reports to guide investments, partnerships, product strategies, and even acquisitions. That’s a lot riding on the accuracy of the forecast.
What’s Next?
Corporate leaders already need to tread carefully when reviewing reports. It can be dangerous—even reckless—to base company decisions solely on analyst projections without doing some other due diligence.
To be clear, I believe AI certainly has a place in every industry, including analyst work and consulting. We are already seeing it elevate and accelerate the scope of work of what’s possible. But how far is too far? And when will we know when we have crossed that line that we have gone too far? These are some of the hard questions we need to be asking now before we are so entrenched we just stop asking forevermore.
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