When it comes to Gen AI, it can feel hard to know what to believe. Many widely heard claims can sound like mere hype, but in the current environment, hype has a short shelf life. What seemed implausible 6 months ago is in many cases becoming a norm today. So how do you separate fiction from reality, and why does the hype feel both overwhelming and warranted? Let’s look at what’s going on in this evolving space.
Why Generative AI Hype Feels Unavoidable and What’s Driving It
You’ve probably seen this play out. Early pilots are crowned as "game changers," “AI” is plastered onto countless vendors’ branding and value propositions, and suddenly every boardroom is pushing for an AI strategy. The hype is real, but interestingly, so is the progress.
What’s Fueling Rapid Innovation in the Generative AI Landscape
How is it that Gen AI is evolving at break-neck speed?
- Aggressive Release Cadences: Major AI companies are bringing advancements to market at unprecedented speed, sometimes as often as weekly. With each iteration, models become more capable, efficient, and easier to integrate into business process workflows.
- Model Architecture Innovation: Breakthroughs in both model architecture and model training methodologies have enabled faster iteration cycles, and more efficient means of training new models.
- Cloud infrastructure and GPU advancement: Cloud platforms provide on-demand access to high-performance computing, essential for both training of large models and running them in production. Graphics Processing Units or GPUs are the specialized processors behind most AI workloads, and they are becoming faster and more memory-efficient, significantly reducing the time and cost of training and inference. (Fun fact – GPUs were first developed for rendering graphics in gaming and visual applications! Now they are the backbone of AI processes.)
- User Feedback at Scale: Ever notice a pop-up within your favorite Gen AI tool asking you if you liked its output? That is Reinforcement Learning from Human Feedback (RLHF) in action, gathering input from millions of users to continually fine tune models.
Four Signs Generative AI Is Delivering Real Business Value
To help you separate true breakthroughs from marketing spin, look for these concrete signals that Gen AI’s hype is actually turning into real-world reality.
1. Claims Are Accompanied by Demonstrable Workflows
When you hear what a Gen AI tool can do, you should also be able to get a description of how it does what it does. Look for public demos, user flows, or documentation showing step-by-step execution, not just outputs. Vague magic = hype. Clear process = credibility.
2. Independent Validation or Adoption
If a Gen AI feature is being used (or at least tested) by more than one credible organization, especially in your industry, that’s a sign it’s worth attention. This is where industry conferences where networking with others can be very handy. Bonus points if a third-party analyst, academic, or integrator has published findings on it. (Hint! Seek out podcasts that test and review AI functionality and capabilities!)
3. Specificity in Claims
Credible capabilities are described in very specific terms (i.e., “analyzes customer transaction prices, identifies trends, and proposes a talk track based on categories of identified churn”) not vague superpowers (“understands all your customer needs and gives you the perfect price”). The more precise the claim, the more likely it's rooted in a real use case.
4. Roadmaps Acknowledge Limitations
Believe it or not, transparency about what's not working yet is a positive sign. Credible vendors will admit current limits and share where they are in development. If a claim acknowledges what the AI can’t do (yet), it suggests a realistic grounding, not hype.
Top Concerns About Generative AI and How to Mitigate the Risks
Concerns around Gen AI in pricing generally fall into two categories: technical integrity and organizational governance. Technical integrity includes issues like hallucination, bias, and lack of explainability. These are best addressed with guardrails such as policy filters, range checks, confidence scores, and model auditability. Leaders should also implement responsible data practices auditing for bias, ensuring representation across datasets, and validating outputs systematically.
On the organizational side, the focus shifts to governance, oversight, and accountability. Forward-thinking companies are establishing Gen AI councils to define roles, responsibilities, and acceptable use policies. They’re implementing access controls, maintaining audit logs, and embedding human reviewers in approval chains. (This is often referred to as “Human-in-the-Loop”, but for our purposes let’s call it “Pricer-in-the-Loop!”) Together, these practices ensure that Gen AI enhances the expert judgment and strategic intent of business professionals, rather than replacing it.
The Pros and Pitfalls of Using Generative AI in Business
The Good:
- Speed: Natural language interfaces cut analysis time dramatically.
- Accessibility: Business users can self-serve insights without needing SQL, or tons of technical experience.
- Discovery: Gen AI can spot patterns and correlations most people wouldn't think to look for.
What to Watch:
- Privacy Risks: Sensitive data and third-party models don’t always mix. Encrypt and anonymize everything.
- Confident Errors: Gen AI can sound convincing while being dead wrong. Trust but verify.
- Opaque Logic: If you can’t explain how it got the answer, be cautious—especially in regulated industries.
- The Bottom Line: Using Generative AI Safely and Strategically
Gen AI isn’t magic. It’s a power tool. Don’t but into the thought that it will replace your pricing professionals or business analysts; Use it with the right safeguards, and it can help you move faster, think deeper, and broaden access to insights. But fail to proactively address the concerns and things can go sideways fast. Start small, set guardrails, along with reasonable expectations, and keep your pricer-in-the-loop! That’s how you turn Gen AI from hype into practical, repeatable value.
Ready to explore how generative AI can make a real impact on your pricing strategy? Contact us today to learn how we can help turn AI hype into measurable business value and be sure to watch our on-demand webinar on taking your data beyond reporting with Gen AI.
Matthew Knaggs is a Senior Business Value Lead at Zilliant, where he works with customers and prospects to demonstrate the ROI and business impact of implementing Zilliant solutions.
