What does it take to create an AI-powered solution that genuinely solves a business problem?

Artificial intelligence (AI) is everywhere, and for good reason. It has the power to solve problems that weren’t realistic to tackle just a few years ago.

At Michigan Software Labs, we’ve helped companies create tailored digital products that make a real impact on their businesses. We want to share how we approach building an AI-powered solution, from discovery to deployment.

In this blog, we’ll walk you through a relatable simulation and the framework that guides our process.

Why companies seek AI

The companies we talk to have very specific goals. Most decision-makers want AI to:

• Automate processes to save time and increase efficiency

• Empower people to focus on high value tasks, rather than repetitive work

• Use predictive analytics to turn data into insights, uncovering trends that weren’t previously visible

• Stay competitive as they see peers adopting AI

• Drive innovation by solving challenges that were previously out of reach because of complexity

Consider this relatable example from the health care industry.

Corewell Health—a leading Midwest health care system—uses a tool called Abridge to address a common frustration: the time doctors need to spend on administrative tasks as a result of patient visits.

Abridge uses AI to transcribe conversations, organize them into notes, and integrate them into Epic (the electronic medical records system). This solution saves doctors time and allows them to focus more on their patients—the work they’re best at.

The success of this digital product is about the integration of AI with appropriate systems and processes. AI acts as a partner to the valuable health care professionals—empowering people, not sidelining them.

While we didn’t build this particular product, this is the same lens through which we approach AI projects.

Our framework for building AI-powered products

When we design and develop an AI-powered product, we use the same core process we’ve mastered for other software projects. Here’s how it works:

1. Discovery:

The first step is always to understand your business dynamics, goals, and challenges. Through product validation, we work closely with you and your stakeholders to:

• Identify inefficiencies in your current processes

• Define clear goals for the AI solution

• Focus on how AI can empower your people, not replace them

This last point means a lot to us. We believe AI can be developed to enhance human skills, rather than diminish them. As a team, we’ve been reading the book “Co-Intelligence: Living and Working with AI,” where Ethan Mollick writes that we can learn to harness the technology’s gifts rather than be replaced by it.

Thoughtful AI integration complements human expertise, while leading to smarter, more efficient workflows.

2. Proof of concept:

Many AI projects begin with the question: Can this actually work?

We tackle this by:

• Starting small: We build a prototype to validate the concept with minimal investment.

• Assessing data needs: Do you have enough high-quality data? If not, we help determine how to collect or source it.

• Testing feasibility: A proof of concept helps us spot potential roadblocks, fine-tune the functionality, and get quick wins before going all in.

This phase is all about building confidence before committing to full-scale development.

3. Building and testing:

Once we’ve validated the concept, we begin building the digital product.

Our approach includes:

• We refine the solution based on real user feedback to ensure it meets your business needs.

• We plan for scalability, so the AI solution can keep pace with your organizations’ evolving needs.

• We prioritize responsible AI usage, where the technology is designed in a way that users can trust and depend on for accurate, repeatable results.

A simulation: AI vision for manufacturing QA

Let’s see how our approach and framework can come to life in a real-world manufacturing scenario.

Imagine a manufacturer struggling with quality control on their production line. Occasionally, defective components make it through inspection unnoticed, causing costly issues. The company’s hardware team is strong, but they lack the software expertise to address this challenge.

Here’s how MichiganLabs would approach solving it.

Identify the challenge:

• Defective components occasionally slip through the manual quality assurance (QA) process, leading to wasted materials and production delays.

• The team needs a solution to catch these defects earlier in the process.

Propose a solution:

• Develop an AI Vision system to inspect components in real time and flag defects early.

Bring the solution to life:

1. Discovery: We would work with the client to map out pain points, define goals for detecting issues and improving efficiency, and identify where human expertise adds the most value.

2. Proof of concept: We would train an AI model to use cameras to recognize common flaws like cracks or misalignments, then deploy a small-scale prototype on one production line to test feasibility.

3. Scaling: Once validated, we would integrate the AI system into critical points on the production line. Dashboards and alerts could empower the QA staff to take action.

Examine results:

• Defects are caught earlier, saving time and reducing costs.

• QA staff focus on process improvement rather than repetitive inspections.

Opportunities for decision-makers to explore AI

For businesses exploring AI for the first time, the biggest opportunities often lie in automating tasks that disrupt daily workflows. Our advice? Start small.

• Focus on a proof of concept to validate feasibility before committing to full-scale development.

• Look at what your people are already great at, and find ways AI can help them do more of it.

What could your team achieve if AI took care of repetitive tasks? Let’s find out together. Start a conversation by completing our simple contact form or by reaching out to me on LinkedIn.