LLMs Are Picking Your Vendors—And Getting It Wrong
Let’s be real—AI is changing everything. From sales forecasting to vendor selection, we’re all leaning on large language models (LLMs) like ChatGPT, Perplexity, Gemini, Grok, and friends to cut through the noise and deliver quick answers. But what happens when those answers are wrong?
I never thought I’d have to combat fake news in enterprise software, but here we are. Recently, I ran an experiment—asking multiple LLMs to list the top CPQ vendors, particularly those recognized by analysts like Gartner, IDC, and Forrester. The results? Let’s just say they were… interesting.
It turns out AI-generated rankings don’t just reflect reality; they shape perceptions. If a company isn’t repeatedly mentioned in public discourse—or if the data isn’t easily scraped—LLMs may act as if it doesn’t exist. That’s a problem, not just for vendors but for buyers who rely on accurate, analyst-backed insights to make critical decisions.
It made me wonder—how much of what we read, even from trusted sources, is shaped by AI’s blind spots? What happens when innovation outpaces recognition?
Would love to hear your thoughts—have you seen AI-generated content overlook key players in your industry?
Despite being the only Visionary CPQ vendor in Gartner’s 2025 Magic Quadrant, servicePath? was conveniently left off multiple AI-generated vendor shortlists. Some models refused to acknowledge us at all until I aggressively rephrased my prompts—almost like arguing with a toddler who insists that 2+2 equals 5.
The Real Problem? LLM Bias & User Positivity Bias
It’s not just about servicePath? (though, of course, that one stings). The real issue is that AI isn’t perfect—it’s limited by:
? Data Gaps – LLMs aren’t pulling from real-time, verified analyst reports. ? Model Bias – They prioritize whatever sources they deem “reliable” (and often miss key players). ? User Positivity Bias – We want to believe AI is giving us the best answer, so we don’t question it enough.
Think about it—if AI is misrepresenting an entire market, what does that mean for vendor selection?
?? Why This Matters for You ??
If you’re in B2B sales, procurement, or IT, chances are AI is influencing (or outright deciding) your vendor choices. But if those choices are built on incomplete or biased data, you could be:
?? Overlooking best-fit solutions that don’t rank due to flawed AI logic.
?? Making high-stakes decisions based on lists that don’t reflect real market dynamics.
?? Trusting AI more than analyst-backed insights, leading to costly mistakes.
The Wake-Up Call: Conduct Your Own LLM Audit
Before you blindly trust AI-generated vendor shortlists, take a step back:
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? Audit the AI’s output – Compare its recommendations with trusted sources like Gartner, IDC, and Forrester.
? Challenge the results – If a vendor is missing, ask why. (Seriously, dig in—sometimes AI “forgets” things.)
? Validate across multiple models – Don’t just rely on one LLM. Compare outputs from different AI tools.
Let’s Talk: How Do We Fix This?
This isn’t just a servicePath? issue—it’s an industry-wide challenge. So, I’m opening the floor:
?? How can vendors ensure they’re accurately represented in AI-generated insights?
?? Should buyers push LLM providers for more transparency in their algorithms?
?? Do we need industry standards for AI-based vendor recommendations?
I’m hosting a virtual roundtable soon to tackle these questions—who’s in? Drop a ?? in the comments if you want a seat at the table.
Take Control of Your Vendor Selection
Don’t let AI’s blind spots make decisions for you. If you want to see how a Visionary CPQ solution can truly transform your sales process, take action today:
?? Read the full blog for deeper insights → [Insert Link]
?? Explore our CPQ solution at servicePath? → [Insert Link]
?? Download our case studies to see how top enterprises are winning → [Link]
?? Check out more blogs on AI, CPQ, and enterprise sales → [Insert Link]
Join the movement for AI transparency and smarter vendor selection. Let’s make sure the future of CPQ isn’t left in the hands of AI models that still need a fact-checker.
#CPQLeadership #AITransparency #LLMAudit #VendorSelection
This is a prime use case for why you need specialized/domain specific agentic AI and not just these LLM services regardless of all the hype, because you can add the extra verification, neuro-symbolic AI, and many other controls to ensure bias and hallucinations don’t taint results like this or at minimum can alert you to uncertainty and confidence. Also agents can be specialized to the task and therefore go through QA cycles and be performance managed for the application at hand. Using what is essentially generalized consumer tech for specialized enterprise functionality is a failing proposition. That is why all these services tell you not to blindly trust the results in the little warning they give on their chat interface. AI use for critical enterprise functions is in need of serious governance because it is going to begin to impact the quality of services and products very soon as people don’t understand what they should and should not trust, and use of what is really consumer best effort tech, is not sufficient for mission critical enterprise applications.
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
1 个月The over-reliance on AI shortlists for vendor selection, particularly in complex domains like CPQ, can indeed lead to biased outcomes. Studies by Gartner show that 80% of AI-driven recommendations fail to account for nuanced factors crucial to vendor suitability. Thing is, how do you propose mitigating this bias when evaluating vendors specializing in highly customized solutions like servicePath?