Checklist: Venturing Into AI/ML Projects

Before diving into AI/ML projects, it’s essential to take a step back and assess whether your business truly needs them. Can you identify many small decisions you need help with? AI is more than just a tool, t’s a strategic move. To determine how it aligns with your company’s objectives and potential ROI, you should start assessing the following areas:

• Core business goals & strategies
• Revenue and expense streams
• Human resources and their touchpoints

By critically assessing these areas and asking the right questions, you’ll identify where AI can have the most meaningful impact on your business.

High-quality data is key

Understanding the current state of your data is extremely crucial when venturing into AI/ML projects. Do you have high-quality data? Quality datasets form the backbone of successful AI models. Keep in mind that creating such data is a long-term effort, it’s rather a marathon than a sprint. The sooner you start, the sooner you’ll be ready to ride the AI wave.

Ensure a robust & reliable data infrastructure

In order to fully support AI/ML applications, you need a robust and reliable data infrastructure. Start by mapping out your data sources, storage systems, and data processing workflows. This helps identify any bottlenecks, inconsistencies, or vulnerabilities in your data pipeline. Cloud-based solutions provide scalability. Platforms like AWS, Google Cloud, or Azure offer flexible storage, computation, and AI services. They efficiently handle increasing data volumes, offer automatic backups and seamlessly integrate with other systems. Lastly, consider implementing automated monitoring systems for real-time tracking of data flow. These systems can help detect bottlenecks and errors early by sending alerts for issues such as inconsistent data formats, system downtime, or performance degradation.

Assess the skills of your team

AI/ML projects are complex areas that require a combination of technical expertise, data knowledge, and business understanding. To ensure their success, it is crucial to assess your team’s existing strengths and address any gaps in expertise. This may mean equipping them with the right tools, providing them with customized training, or bringing in external consultants/experts.

It’s time to gather some ideas! During this step, engage as many stakeholders as possible to understand their challenges. Oftentimes, it is a manual, repetitive, and prone-to-error task that consumes much time. However, it has been done like this forever, so nobody thinks of making a change. The goal is to ensure that the AI/ML solution addresses real pain points and aligns with experiences on the ground level.

As a result of the workshops, you will end up with a loose collection of ideas and perspectives, but patterns will emerge. If possible, validate the assumptions on which the chosen idea is based. Do this with as little effort as possible: as a general rule, it is cheaper to test the initial assumptions than the final solution.

Now that you have identified the process that might lead to potential quick wins, what is the next step? What if nobody believes in it? The easiest way to open people’s eyes is to use AI to make a (small) improvement. Once they experience how it works, notice the difference, other similar cases will follow. That’s when the ball starts rolling.

Starting with a Proof of Concept (PoC) is the best approach. A PoC lets you test critical elements of the solution in a controlled setting, pinpoint any technical or operational issues, and refine the strategy before scaling up. By doing this you can start to build confidence among stakeholders and secure buy-in for further development.

With the results from your PoC in hand, the next step is to create a detailed roadmap for transitioning from the initial concept to full-scale execution. A well-organized plan should clearly define the key milestones, deliverables, and timelines for your AI initiative. It should also address the necessary resources, skills and technologies at each phase of the implementation.

Additionally, your roadmap should include a thorough plan for scaling the AI solution across the organization. Identify the key factors that will help or hinder scaling, such as data quality, infrastructure readiness and workforce capabilities. Create strategies to overcome these challenges and ensure a smooth shift from a pilot to a full-scale rollout.

When exploring a new territory, it’s usually better not to be overly ambitious. There are often too many unknowns. That’s why starting with a Minimum Viable Product (MVP) is a smart approach. An MVP is a simplified version of the solution that only includes the most critical features. Essentially, it’s the minimum needed to perform its intended function in real-world scenarios.

A good example is implementing AI in customer service. Since customer support is often costly, using AI to streamline processes can lead to quick wins and increased efficiency. For instance, an MVP could involve automated email routing. You could start with a basic model that forwards emails to the right department—no complex interface, just working code. In no time, departments would begin receiving correctly routed emails (though some may still be misrouted), and the team would become more engaged with this AI-driven system.