Running an AI project is exciting, but success depends less on the model you choose, and more on how well you prepare.
I’ve been involved in many AI and machine learning projects over the years, and I’ve seen quite a few that made it past the proof of concept stage. Based on my experience, I’ve learned many things that can help enable success.
Here are a couple key things I would recommend doing to ensure success with your next AI project.
1. Define the Problem Statement
The very first stepand arguably the most important, is to define the problem you’re trying to solve. Too often, teams jump straight to building things without fully understanding what they are solving for.
A good problem statement is specific, measurable, and realistic. It frames the project in business and technical terms without being vague. For example, instead of saying, “We want to use AI to improve customer service,” a sharper problem statement would be:
“We aim to reduce average customer support email response times by 30% through an automated email categorization and routing system.”
Notice the difference: the second version specifies what you want to do (automate categorization and routing), for whom (customer support emails), and what success looks like (30% faster response times).
Before proceeding, validate your problem statement by asking:
- Is this problem actually solvable by AI?
- Do we have or can we get the right kind of data?
- How will we measure success?
If the answers are unclear, refine the statement until they are.
2. Set Clear, Actionable Objectives
Once the problem is well-defined, you need to lay out clear project objectives. Objectives translate your problem into a roadmap for your AI team. They should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound.
Good AI project objectives often include:
- Data objectives: Define the datasets you’ll use, any augmentation needed, and the expected quality of the data.
- Model objectives: Outline performance metrics, such as 90% classification accuracy or a 5% false positive rate.
- Deployment objectives: Specify where and how the model will be deployed (cloud, edge devices, integrated into existing systems).
- Business objectives: Link the technical outcomes to business impacts, like revenue increases, customer satisfaction scores, or operational cost reductions.
Having separate but aligned technical and business objectives ensures that your project doesn’t just achieve high model performance but actually creates value.
3. The Rest of the Journey
After defining your problem and objectives, the typical AI project flow looks like this:
- Data Collection and Preparation: Secure relevant, high-quality data and prepare it (cleaning, labeling, augmenting).
- Model Development: Train, validate, and iterate on different model architectures and approaches.
- Testing and Validation: Rigorously test your models against unseen data and under real-world conditions.
- Deployment and Monitoring: Deploy the model into production with strong monitoring tools to detect drift and maintain performance.
Throughout every stage, regularly revisit your problem statement and objectives. If they change, and they often do as you learn, update your plan and communicate the changes rather than just letting things drift off course aimlessly.
Increase your chances for success
In AI projects, success is determined at the very beginning. Invest the time to define a clear, achievable problem statement and a set of aligned objectives. Everything else — the models, the tools, the deployment — builds on these critical foundations.
AI is powerful. But without focus, it’s just potential energy waiting to be wasted. A little preparation can prevent months of wasted effort later.
If you want more information like this or have a question, or want to provide feedback about this post, please leave a comment below or send me a message!
