Practical AI Application

Is AI a Solution Looking for a Problem?

We've all seen the headlines. "AI will change everything!" "AI is the future!" While we're excited about the potential of artificial intelligence, at Insightfully Curious, we've found that many organizations start their AI journey backward. They get excited about the technology and then try to find a way to use it, rather than starting with a clear business challenge.

The Harsh Reality: Why So Many AI Projects Fail

It's a sobering statistic: a recent MIT study found that 95% of enterprise generative AI pilots fail to deliver a measurable return on investment. The problem isn't that the AI models aren't capable enough, but rather, the failures are due to flawed enterprise integration.

According to the study, the reasons are a little less glamorous than the headlines suggest:

A solution without a problem: Many companies jump on the AI bandwagon without a clear business purpose. They're sold a shiny new concept and then try to find a problem to fix, which often leads to "pilot purgatory" where projects stall out.
Weak data foundations: AI depends on accurate and consistent data. Projects frequently fail because of a lack of high-quality data. Poor inputs guarantee poor outputs.
Vague objectives and no clear ROI: Without specific, measurable goals, it's impossible to know if an AI project is truly successful. This lack of a defined ROI model can lead to projects that show initial promise but don't translate into real business value.

The Promise and the Potential: How to Ensure Your Success

While the AI landscape is littered with failed pilots, the potential for those who get it right is immense. The most significant and immediate value often comes not from chasing new revenue, but from eliminating existing costs and inefficiencies. We can help you navigate this journey with confidence, starting with the three questions that set you up for success.

This is where we come in.

Why AI? Workshop

Define the Problem: We start with a curiosity-first workshop to explore your business's most pressing pain points. We ask the right questions to identify the specific, high-impact problems that are ripe for an AI solution. This ensures we're not just adopting AI for the sake of it, but to solve a real, tangible challenge.

Questions to define the challenge:

Where do you have a tremendous amounts of qualitative data? Common applications include:
- Not enough personnel to train
- Large knowledge base that's difficult to search
What processes are repeated that leverage structured data? Common applications include:
- Consolidating information captured by forms.
- Automation for repeated processes.

AI Feasibility & ROI

Define the Feasibility and Foundation: Once we have a clear problem statement, we work with you to assess the feasibility of an AI solution. There are three portions that must be defined:

Data: This involves a deep dive into your data—the lifeblood of any AI project. We help you identify the data and determine what structural changes are needed.
AI application: In this portion of the project definition, we determine what models are most applicable and who will actual build it. It also takes into account regulatory requirements and security considerations.
ROI Definition: How will you know you're successful? This section is one of the most critical as you will define what success - and failure - look like for the project. Clear definitions of value realization are captured to track progress.

Rubber, Meet Road

Build and Implement for Impact: With a solid foundation in place, we help you select and implement the right AI tools. Whether it's a custom-built model or an off-the-shelf solution, our goal is to create a solution that drives measurable value, enhances productivity, and improves your bottom line. We focus on quick wins to build momentum and prove the ROI, ensuring your project is a success story.