Innovation vs. Creation: How Strong Is Your Hypothesis When Innovating with AI

This article is written by Paul Njonga MBA MCIOB

Have you ever wondered whether you're genuinely creating something new or simply innovating on what already exists? In the fast-paced world of construction, where claims management is both complex and critical, this distinction is vital. As AI tools become increasingly integrated into managing construction claims, it's worth asking: Are we really testing our ideas with strong hypotheses, or are we just hoping the technology will do the heavy lifting for us?

Innovation vs. Creation: What's the Real Difference?

First, let’s clarify the difference between innovation and creation. Creation is about bringing something entirely new into existence—a novel process, idea, or tool. Innovation, on the other hand, involves improving or refining something that already exists, making it more efficient, effective, or relevant.

In construction claims management, introducing AI could fall into either category, depending on your approach. Are you creating a brand-new system for handling claims, or are you innovating by enhancing your existing processes with AI tools? This distinction is more than just theoretical—it affects how you plan, implement, and measure your AI projects.

Crafting a Strong Hypothesis for Innovation

In both creation and innovation, a strong hypothesis is your foundation. But when you're innovating with AI in construction claims management, how robust is that hypothesis? Are you entering the process with a clear, testable idea, or are you crossing your fingers and hoping for the best?

A strong hypothesis in this context is one that’s grounded in a deep understanding of your current claims management challenges. It should be specific, measurable, and backed by data. For example, consider the hypothesis: "Implementing AI to automate the document review process in claims management will reduce the time spent on each claim by 40% and decrease errors by 25%."

This hypothesis isn’t just a guess—it's based on an understanding of the current manual process, including the time it takes and the typical error rate. Notice that this is an example of innovation. You're not creating an entirely new process, but rather improving an existing one by introducing AI.

The Dangers of a Weak Hypothesis

The risks of a weak hypothesis are significant. If your hypothesis is vague or untestable, you might end up implementing AI for its own sake, rather than achieving any meaningful improvements in your claims management process.

For instance, imagine your hypothesis is: "This AI tool will improve our claims management system." While that sounds promising, it’s far too broad. What does "improve" mean? How will you measure success? Without clear metrics or a defined outcome, it’s difficult to assess whether the AI implementation is genuinely beneficial.

Contrast this with a strong hypothesis like: "By using AI to analyse historical claims data, we will identify patterns that can reduce future claim occurrences by 15%." Here, you have something specific to test, measure, and refine—a clear path to innovation.

AI as a Catalyst for Innovation in Construction Claims Management

AI can be a powerful catalyst for innovation in construction claims management, but your approach will dictate the results. If you're focused on innovation, your goal is to enhance existing processes. Your hypothesis should be focussed on improving efficiency, reducing costs, or increasing accuracy.

For example, let’s say you’re using AI to streamline the negotiation phase of claims management. A strong hypothesis might be: "Incorporating AI to predict the outcomes of claims based on past data will lead to a 20% faster resolution time, reducing legal fees by 30%." This is innovation at its best—leveraging AI to refine and optimise what you’re already doing.

But what if you wanted to create something entirely new? Your hypothesis might shift to: "Developing an AI-driven platform that anticipates potential claims before they arise will reduce the number of claims filed by 25% over the next year." Here, you're not just improving an existing system—you’re creating a new approach to claims management.

Strengthening Your Hypothesis: Key Questions to Ask

Whether you’re innovating or creating, the strength of your hypothesis is crucial. To ensure it's robust, ask yourself these questions:

1. Is it specific? Can you clearly define the expected outcome?

2. Is it measurable? Do you have clear metrics to track progress and success?

3. Is it based on data? Are you using solid information rather than assumptions?

4. Does it align with your strategic goals? Will this hypothesis help you achieve your broader business objectives?

By rigorously testing and refining your hypothesis, you can make smarter decisions about how to use AI in construction claims management, ensuring that your efforts lead to real, measurable improvements.

Key Takeaway: The Power of a Strong Hypothesis

The true power of AI in construction claims management doesn’t lie solely in the technology itself, but in how you use it to drive innovation or creation. A well-crafted, robust hypothesis is the foundation of this process. It keeps your efforts focused, ensures that your AI initiatives are strategic, and helps you achieve tangible, impactful results.

So, the next time you're considering an AI project in your construction claims management system, ask yourself: How strong is my hypothesis? The strength of your answer will determine not just the success of your AI implementation, but its overall impact on your organisation’s efficiency and bottom line.