Developing effective AI strategies requires a combination of careful planning and a thorough understanding of the technology and its potential applications.
In this article, we will discuss some key considerations when creating an effective AI strategy for your business.
How AI integrates with different industries and how it will grow in 2023
Artificial intelligence (AI) is rapidly integrating across every industry, from healthcare to finance and even retail.
And this fast growth is changing the way businesses handle their own data.
Realizing the seemingly endless power of this tech trend, companies are recognizing the value of their data and the role it plays in driving business growth and efficiency.
But as AI technology advances, so too does the amount of data that is being generated. And thus the need to comprehend the meaning of the data recollected and its potential applications.
With that, companies are finding it increasingly difficult to manage and make sense of all these figures to gain a powerful edge over competitors.
With that in mind, today, the most forward-looking companies are investing in data management and analytics tools (and in the development of AI models), to better understand and utilize their data in their favor.
AI models are considered to be the best analytic tool available due to their ability to process large amounts of data quickly and accurately. They can be trained on vast amounts of data, allowing them to identify patterns and trends that might be difficult for humans to detect.
Plus, AI tools can also be updated as new data becomes available, allowing them to continue to learn and improve over time.
The importance of using the correct data for the effectiveness of AI tools
Having the correct data for AI models is essential for the effectiveness and accuracy of the predictions and decisions made by such tools.
It is not just about having a large amount of figures, but rather having high-quality, relevant information that is tailored to the specific problem or task that the AI model is being used to solve.
In simple words, when selecting data for AI models, it’s important to consider the relevance of the data to the problem or task at hand.
For example, if you are building a model to predict customer churn rate, you will need stats on your customers’ behavior and demographics, rather than data on website traffic or product sales.
Let’s take the facial recognition system used by the London Metropolitan Police in 2018 as an example. The system was trained on a dataset of images that was disproportionately made up of white, male faces.
As a result, the system was found to be less accurate at identifying people with darker skin tones and women.
Because of this biased data, the system was found to be correct just 85% of the time when identifying white people, but the accuracy dropped to 65% when identifying black people.
This example highlights the importance of ensuring that the data used to train AI models is diverse and representative of the population it will be used on.
For effectiveness of AI models, your data should be:
- Accurate
- Up-to-date
- Free from errors or inconsistencies
When we talk about accurate or free from error data, it means the data is free from any errors, inconsistencies, or inaccuracies that could skew the results of an AI model.
In simpler words, all the figures should be representative of the population it is intended to model.
What’s more, the data you’re using must be properly formatted for use by the AI models. Missing or incorrect data should be appropriately handled, and everything must be always properly labeled and organized.
Plus, diversity in data is crucial, as a diverse set of information (i.e. different sources, features, and examples) will avoid the model to generalize due to unseen data.
ENAIA helps your company make the most of its existing data
Are you looking to integrate AI to gain a competitive edge?
Excellent!
Regardless if you’re a big or small company, the good news is that developing effective AI models doesn’t have to be expensive or overwhelming. Quite the contrary.
In fact, there are businesses like ENAIA that specialize in training effective AI models so that you don’t have to worry about gathering or interpreting data.
ENAIA is Multimodal Machine Learning as a Service that provides competitively priced AI models that can be trained and developed to meet the specific needs of small and medium-sized companies.
You’ll be able to develop a solid strategy around your performance objectives and data recollection. Plus, our team of experts can help you understand the capabilities and limitations of AI, as well as how to collect and organize data for AI models.
With ENAIA, small and medium-sized companies can take advantage of the many benefits of AI, such as automation, improved efficiency, and better decision-making, without the high costs often associated with developing AI models.
Gone are the days when AI was exclusively available for big companies with large budgets. Now, the time has come for smaller businesses to reap from its benefits.
