A five-step guide to your first AI proof of concept

Step 1: Identify the problem(s) you want to address with AI

Starting your AI proof of concept (PoC) journey can be thrilling. But first, you need a clear understanding of the problem you’re trying to solve with AI. The selected use case should be significant and something that AI can handle more effectively than other technologies. 💡 If you’re unsure, look at how others in your field are leveraging AI or examine your company’s challenges and compare them with AI’s potential.

Once you have a list of opportunities, ask yourself these questions:

• Is the problem specific enough? Can you measure success?
• Have you tried to solve this problem with other technologies?
• Do you have the talent and funding to support this project? If not, can you hire an external team?
• How will it impact your business? Is this impact significant enough to make the effort?
• Can you sell this idea to the executives? Is your organization ready for such projects?
• Does your company already have a data strategy? How will it align with this project?
• What are the potential risks and limitations of using AI for this problem?

The challenge here is selecting a use case that doesn’t add much value or doesn’t utilize AI’s full potential. AI is costly, and choosing an insignificant case means spending more than you gain. 🛠

Step 2: Prepare the Data

With a defined problem, it’s time to gather and prep the data for training your AI algorithms. You can:

• Check what data is available internally.
• Generate semi-synthetic data using ready-to-use applications or custom solutions.
• Purchase datasets from reliable vendors.
• Use open-source data.
• Hire people to scrap data that fits your purpose.

Using a combination of sources is often the best route. Data scientists can perform the initial data assessment, structure, clean the data, and perform feature engineering and data manipulations.

When data is ready, split it into three sets:

• Training set for model learning.
• Validation set for model validation and training iterations.
• Test set to evaluate algorithm performance.

Challenges here include ensuring your training data represents the entire population. Algorithms might perform well on common cases but fail on rare ones. 🏋️‍♀️

Step 3: Design, Build, or Buy the Solution

Decide whether to create your AI model from scratch or buy an existing solution. Build from scratch if:

• Your solution is innovative and doesn’t fit existing standards.
• Off-the-shelf solutions are too costly to customize.
• The nearest standard model is overkill.

Buy a pre-made model if buying and customizing is cheaper than building from scratch. Building gives you more control over accuracy and fits better with your internal processes. 🛠

Consider cloud infrastructure for training and deploying algorithms based on security, workload, cost, and accessibility needs. Each approach has pros and cons. 💡

Step 4: Assess AI PoC’s Value Generation Potential

This step involves evaluating whether the AI PoC meets expectations:

• Revisit your KPIs and test the solution against them.
• Gather data on system performance before AI PoC implementation.
• Compare your solution’s performance with industry benchmarks.
• Collect user feedback through focus groups or online surveys.
• Conduct a cost-benefit analysis to understand the financial impact.

The challenge here is avoiding errors in your evaluation, such as mathematical mistakes or misestimating potential business benefits. 📊

Step 5: Iterate or Scale the AI PoC

If the results weren’t as expected, consider tweaking the solution and iterating the process. You might adjust the ML algorithm or experiment with different hardware components or cloud services.

If satisfied with the AI PoC performance, consider scaling it:

• Apply the PoC to other business cases.
• Scale the infrastructure—more processing power or storage might help.
• Optimize the AI PoC—more training with new or better-labeled data can improve results.

Scaling can reveal new architecture flaws or model errors, so proceed carefully. 🚀

DIVERSITY can guide you through each step of your AI PoC and help you achieve outstanding results. With our expertise, turning your AI concepts into reality has never been easier! 🌟 Contact us today. 💼