The Biggest Challenges in creating AI Models

AI is all the rage these days, with companies running around figuring out how to integrate AI into their businesses. From healthcare to finance, AI models have proven their worth in automating tasks, making predictions, and extracting insights from vast amounts of data. But, do you have the data, infrastructure and strategic alignments to support what’s needed to generate AI models for your business? What I find is that it may differ greatly between industries and countries, but in Canada, most companies aren’t quite ready with the right data quality and infrastructure to support AI modelling yet, and most are still figuring out the best data management tools.

What are the top challenges when developing AI models?

There are 3 main challenges in creating the most supportive environments for your company to generate proprietary AI models:

• 1) High Quality Data

• 2) Efficient Data Infrastructure

• 3) ROI and Business Value

1) High Quality Data

Like they say: “Garbage in garbage out”, you need good quality data to be able to perform good analysis and train good AI models. In my years of working in the industry, I’ve seen many cases of companies making many manual changes to their data, data being incomplete, data seeming unreliable, and sometimes—data that is just out right wrong. When you train models, you want many cases of examples of something that you are trying to train it to do. Those examples must be very clear and easy to understand for the model to learn at its best. How many examples am I referring to? The more the better, in my line of work, usually over 10,000+ cases. 

To remedy the lack of quality data,  a considerable amount of effort is needed in data preprocessing, cleaning, and augmentation. Organizations should invest in data governance practices, ensuring data is consistently collected, stored, and labeled. Cross-functional collaboration between data scientists, engineers, and domain experts is crucial for defining data requirements and maintaining data quality. Dat6 Consultants can help with proper data collection, data governance, and data management projects. 

2) Efficient Data Infrastructure

Having good and efficient data infrastructure not only enables the collection of high quality data, but also allows for automated pre-processing of data that is required to train the AI models. As AI projects grow, so does the need for more data. An efficient data infrastructure can scale horizontally and vertically to accommodate growing data volumes. This scalability ensures that organizations can continue to leverage AI as their data needs expand. Some AI applications, such as fraud detection or recommendation systems, require real-time data processing. An efficient data infrastructure can handle streaming data and ensure that AI models can make rapid decisions based on the most up-to-date information.ML pipelines are required for efficient and timely retraining of your models and sound deployments of the latest versions. Dat6 Consultants can help guidance on data infrastructure and engineering projects. 

3) ROI and Business Value

Quantifying the return on investment (ROI) and demonstrating the business value of AI implementations can be challenging. AI projects often require significant upfront investments in data infrastructure, talent, and technology, and it may take time before the benefits become evident. Ensuring that AI initiatives align with strategic business goals and key performance indicators is crucial.

To address this challenge, organizations should establish clear success metrics, track performance, and communicate the value of AI projects to stakeholders. Demonstrating incremental improvements and tangible benefits over time can help build support for ongoing AI investments. Dat6 Consultants can help with crafting a customized data strategy that enables AI modelling for your company.

How can I still integrate AI without the best data quality and infrastructure?

It is still possible to leverage AI in your business without being the one to generate the AI model. Today there are many robust models such as ChatGPT that can be easily called and have been trained on many topics that may be well suited for use by your clients as well. For example, a recipe recommender is something that ChatGPT is already able to perform fairly well on and if your company was to deploy such a tool, then it just needs to connect to its API and create a front end for your customers to interface with.

Conclusion

While AI models hold immense promise for organizations, they also present substantial organizational challenges. Overcoming data quality issues, data pipeline infrastructure, and aligning with strategic goals are common challenges. It is important to set a good foundation with efficient data pipelines and high data quality to train great AI models. Furthermore, taking Agile implementations helps to confirm the value of AI models as it is being built. As AI continues to evolve, organizations must adapt and embrace these challenges to remain competitive in an increasingly AI-driven world.