The AI Hype Is Real — But So Is the ROI Problem
We’re past the “should we use AI?” phase. Now, it’s all about the “how.”
And despite record-breaking investments, the how is proving elusive.
Between 2021 and 2024, AI deployment rates fell from 55.5% to 47.4%, and the percentage of projects delivering significant ROI dropped from 56.7% to 47.3% (Appen, State of AI 2024).
In other words, you currently have a better shot at ROI by betting on red or black than by betting blindly on AI.
Why AI ROI Is Falling Short
Dig into why AI projects are underperforming, and the culprits aren’t new.
- Poor data quality
- Misaligned objectives
- Unrealistic expectations
- Lack of change management
- Untested benefit cases
These are the same pitfalls we’ve seen in past tech waves—from ERP to RPA. AI’s promise may be revolutionary, but the path to value still depends on solving familiar transformation challenges.
From Experimental to Enterprise: The Shift AI Must Make
When Gen AI entered the scene, the narrative was clear: give LLMs to employees, and they’ll discover valuable use cases from the ground up.
Sure, it’s delivered some quick wins—like summarized meetings, auto-drafted content, and faster first drafts. But these productivity boosts are often anecdotal—and not backed by measurable ROI.
Meanwhile, the organizations seeing real value are those targeting operational transformation, not just incremental convenience:
- Insurers reducing underwriting turnaround from days to minutes
- Banks strengthening real-time fraud detection
- Healthcare providers using AI-powered triage to prioritize care
These use cases are complex, regulated, and data-heavy—and they succeed because they’re grounded in well-understood business processes.
In fact, organizations that take a process-led approach are 3.3x more likely to scale Gen AI successfully
(Accenture, 2024).
What AI Leaders Get Right: The Power of Process
What sets successful AI leaders apart? They don’t start with tools. They start with process.
Business process is the lens through which they understand how the organization truly operates—where people, data, technology, and governance intersect.
It gives them a strategic foundation for AI investment:
→ Pinpoint AI-ready processes with reliable, high-quality data
→ Flag compliance risks early, before they derail implementation
→ Align stakeholders around how things work, reducing delays and confusion
→ Target investments where automation will yield material, not marginal, value
Process becomes the common language for executing AI at scale.
AI’s Role: From Assistant to Enterprise Engine
Used correctly, AI doesn’t have to be a footnote on a productivity report.
It can become the central driver of how your business operates—faster, smarter, more resilient.
But to get there, we need to stop treating AI like magic and start treating it like infrastructure.
And just like any foundational system, it needs blueprints. That blueprint is business process.
As the MIT Center for Information Systems Research (CISR) succinctly puts it:
“Powerful business processes are at the core of the future-ready company, and a requirement for automation at scale.” (MIT CISR – Getting in the Flow: How Companies Use AI to Build High-Performing Business Processes)
Final Thought: Familiar Problems. Proven Solution.
AI might be the newest tool in your digital toolkit, but the obstacles to value haven’t changed.
Fortunately, neither has the solution.
Process isn’t a sidekick to AI success—it’s the blueprint that makes it possible.
Before you chase the next promising AI use case, ask:
Ready to turn AI from hype into results?
Explore how BusinessOptix accelerates AI-powered process transformation →
