The AI Hype Rollercoaster (You Are Here 📍)
But Gartner said there was only Peak of Inflated Expectations before the Trough of Disillusionment. What happened?
Ever feel like you’re strapped into a rollercoaster you didn’t quite sign up for? That’s the AI landscape—one minute, Generative AI is going to solve all of our problems, and the next, we’re wading through unmet expectations. Now, we’re hearing buzz about “Agentic Workflows” and “Reasoning Models,” each promising to be the next big thing. But before you let the hype sweep you away, let’s take a step back.
The real challenge for business leaders isn’t choosing the latest label—it’s figuring out how to solve real problems and deliver meaningful ROI with AI, rather than simply chasing the next trend.
The Whirlwind of AI: A Brief Look at Hype Cycles
Before diving into today’s innovations, a quick rewind is in order. We’ve seen this cycle before:
Dotcom Mania: In the late ’90s, sky-high valuations and unbridled expectations quickly led to a harsh reality check when the bubble burst.
Cloud Computing: Initially met with skepticism about “data in the cloud,” the practical benefits soon won over even the harshest critics.
3D Printing & VR/AR: Once hyped as revolutionaries for every household, these technologies found their value in more specialized, less flashy niches.
Crypto/Web3: Still hyped–real-world applications seem to always ‘go to zero’, but the hype cycle is less a cycle and more ‘to the moon’ (sorry).
The lesson? Hype cycles are intrinsic to tech evolution. Big promises eventually give way to grounded, practical adoption—and that’s where lasting value emerges.
Agentic Workflows: A Step Beyond the Hype
When Generative AI’s debut left many companies with more challenges than solutions, “Agentic AI” stepped in. Instead of merely following commands, these systems proactively make decisions to reach a goal—think of them as AI that not only assists but also manages end-to-end workflows.
Complex Decision-Making: Beyond simple automation, agentic systems can handle multi-step, nuanced tasks if configured correctly.
End-to-End Automation: From password resets to approval processes, they offer the promise of automating routine, repetitive tasks.
Fact Checking and Iteration: Sometimes pitched as a quick fix for hallucinations, it’s also a quick fix for boosting token consumption as prices fall, but does offer a way to mitigate hallucinations/confabulations as LLMs are very good at fact checking each other.
However, as promising as they are, agentic workflows must be adopted with a clear understanding of the problem they’re meant to solve. They’re not magic bullets, but targeted tools when used in the right context.
Real-World ROI: Case Studies that Cut Through the Hype
Understanding AI’s ROI isn’t just about abstract metrics—it’s about real-world results. A recent Techstack article on calculating AI ROI details key metrics and best practices for measuring value, reminding us that investments should be gauged on tangible improvements rather than just the allure of new technology. Here are two illustrative examples:
Case 1: AI in Hospital Radiology
In healthcare, integrating AI into radiology has shown remarkable returns. A study published in the American College of Radiology journal demonstrated that an AI-powered diagnostic platform could achieve a 451% ROI over five years. When radiologist time savings were factored in, the ROI climbed to an astounding 791%.
The Problem: Hospitals often struggle with high labor costs and workflow inefficiencies in diagnostic imaging.
The Solution: Implementing an AI system streamlined the workflow, reduced reporting times, and improved diagnostic accuracy.
The Results: Beyond measurable cost savings, the platform delivered additional revenue through increased follow-up procedures—though the ROI varied significantly depending on hospital type and specific operational settings.
Case 2: PayPal’s AI-Driven Cybersecurity
In the realm of cybersecurity, PayPal provides another compelling example. In an in-depth interview with VentureBeat, PayPal’s CISO detailed how AI and machine learning are revolutionizing their fraud detection and risk management systems.
The Problem: With massive transaction volumes and evolving fraud patterns, maintaining robust security while ensuring a smooth customer experience was a major challenge.
The Solution: PayPal implemented transformer-based deep learning models that can be retrained and deployed in as little as 2–3 weeks. This agile approach allowed them to quickly adapt to new fraud schemes.
The Results: Between 2019 and 2022—amid a near doubling of annual payment volumes—the company achieved an 11% reduction in losses. This success is a testament to how AI can be leveraged to significantly bolster cybersecurity defenses while contributing directly to the bottom line.
Leading with AI: Getting the Fundamentals Right
One critical mistake companies often make is confusing AI as a flashy feature with AI as the foundational technology that enables robust features.
Too many organizations start with the assumption that AI must be directly visible to the end user. In reality, the real value frequently lies in the backend—powering decision-making, automating data flows, and enhancing core processes without being overtly “AI-branded.”
AI as an Enabler, Not a Frontend Feature: Many high-impact applications—like fraud detection, intelligent routing, or risk management—are best deployed deep in your tech stack. The end user might never see the AI at work, but they benefit from its efficiency.
Avoiding the Hype Trap: Chasing after the latest “AI” label can lead to costly projects that don’t address your actual business needs. Instead, focus on how AI can be a tool that supports your existing processes and helps solve real problems.
2025 and Beyond: It’s All About the Right Fit
As we look toward the future, it’s tempting to latch onto the latest trends—agentic workflows, reasoning models, and other buzzworthy innovations. However, the key takeaway is that successful AI adoption isn’t about following the hype; it’s about understanding the specific problem you’re trying to solve and selecting the right approach.
No One-Size-Fits-All: Not every process needs an AI solution. Sometimes traditional automation or simpler technologies are more appropriate.
Tailored Approaches: Whether it’s a behind-the-scenes AI that optimizes logistics or an AI model that enhances cybersecurity, the solution must align with your business needs and scale appropriately.
Critical Evaluation Over Hype: Every new AI innovation eventually encounters technical complexity and unmet expectations. The secret to long-term success is to critically assess your operational challenges and invest in AI solutions that deliver clear, measurable ROI.
In essence, while agentic and other advanced AI methods have their place, the smartest move is to remain laser-focused on solving real problems—not just chasing the next big buzzword.
As a Tech or Product Leader, What do I do?
The AI landscape is a rollercoaster of highs and lows, but by staying grounded in your business objectives, you can harness its power without getting swept away by hype.
Here are the key takeaways:
Focus on Real-World Problems: Evaluate AI investments based on their ability to solve specific challenges and deliver measurable ROI.
Leverage AI as a Backend Enabler: The most impactful AI solutions often work behind the scenes, enhancing your existing operations rather than serving as a standalone feature.
Tailor Your Approach: Not every process needs an AI overhaul. Choose the right technology for the problem at hand, and remain cautious of one-size-fits-all solutions.
By carefully measuring and managing your AI investments—using frameworks like those outlined in the Techstack article—you can ensure that each dollar spent on AI contributes to sustainable, long-term growth.