The application of artificial intelligence (AI) in enterprises can help improve productivity, performance and growth. Many organizations are aware of this but are faced with the problem of not really knowing where to start applying AI.
There are many companies facing this challenge, and while each has its own organization, needs, training and vision for AI, the first thing that is needed is to understand what artificial intelligence can do and how they can leverage it.
That is why we have created this article, to serve as a basic guide for those organizations that want to introduce AI and are not sure how to get started.
Differences between predictive and generative artificial intelligence.
First of all, we consider that it is important to understand these two main branches of AI, because although they share a technological base, their application and use cases are very different and therefore should be treated separately:
Among their main differences stand out:
- Their objective: generative AI focuses on content creation, while predictive AI is dedicated to anticipating future results based on historical data.
- Their application: predictive AI is especially interesting for decision making with more and better information of what may happen in the future, while generative AIs have a more important place in creative fields such as text generation, art or design.
Predictive artificial intelligence: what it is and what benefits it offers.
Let’s start by talking about predictive AI and how to apply it in enterprises.
This branch of AI focuses on predicting future outcomes based on historical data. Thanks to the use of advanced algorithms, it is capable of analyzing large amounts of data to identify trends, relationships and correlations that are imperceptible using other conventional analysis methods.
Among the main benefits that this artificial intelligence offers to companies we can highlight:
- Improves decision-making: as it provides crucial information, making it possible for management teams to anticipate changes in the market to minimize risks and better capitalize on opportunities.
- Optimizes processes: as it can identify areas for improvement to reduce operating costs and increase operational efficiency.
- Improves customer satisfaction by facilitating the customization of products or services by anticipating customer needs, behavior and preferences.
- Delivers sustainable competitive improvement by making companies more agile, adaptable and data-driven than their competitors.
How to introduce predictive AI in your company from scratch.
As we have said, each organization has a different nature and needs. This can affect the process of implementing artificial intelligence, although in a generic way we could define the following steps to follow.
Assessment of needs and objectives.
A predictive AI is in charge of foreseeing tomorrow to improve decision making today. Therefore, the first step to take is to identify where it has a place in your organization.
In other words, you need to identify what decisions are being made to see where AI could offer an improvement. This requires an inventory of both strategic and operational decisions to better understand where it can add value.
Data preparation and collection.
This type of AI relies on analyzing past data to make predictions, so an important part of the process is to collect and prepare relevant and high-quality data for use in developing predictive models.
Explain that sometimes the data needed to develop an AI project is not available at first, but when you are clear about the use case you can evaluate how to get the data you need and put in place the necessary mechanisms to start collecting that data.
It is advisable to start by selecting a project of an adequate size so that it can be developed in terms of resources but can show significant results, as a pilot action within the organization.
Predictive model building and training.
In this phase, predictive models are developed using algorithms appropriate for the company and its data sets, and validated before implementation.
In addition, a platform must be selected that offers ease of use, scalability, ability to integrate with other company assets, etc.
Implementation and monitoring.
Once the predictive model has been validated, it is implemented within the actual business process and monitored to evaluate its performance and make any necessary adjustments.
After completing this first AI implementation experience and based on the lessons learned, it is possible to consider extending its use to other areas of the company. The important thing here is to expand its use progressively and carefully, involving all relevant teams and ensuring alignment with the company’s strategic objectives.
Examples of predictive AI applications in companies.
The applications of this technology in the business world offer many possibilities. As we will see below, its application can be made at all levels within a company (from more strategic to more “everyday” decisions) and for organizations of all types of sectors and sizes.
For example, applications such as Netflix or Spotify use predictive AI for suggestions to their users to achieve more playing time and greater satisfaction. Amazon does something similar when making product recommendations, taking into account your purchase history, browsing patterns, user behavior, etc.
Tesla incorporates predictive AI in its self-driving vehicles, which analyze their environment in real time and predict traffic and pedestrian behavior.
Coca Cola uses this type of AI to optimize inventory management and distribution using demand prediction models, which allows them to minimize storage costs and maximize product availability.
If we talk about more uses in organizations of other sizes, we could mention these examples:
- Predicting customer behavior in retail: allowing for increased personalization of offers and predicting the impact of different marketing actions both online and offline.
- Disease detection in healthcare: AI has the ability to detect and correlate signals that can lead to a premature diagnosis that otherwise could not be made.
- Candidate selection in human resources: thanks to the optimization of the process of reviewing profiles and resumes, something that usually requires a lot of time from selection teams.
- Detection of financial fraud: the analysis of fraud patterns and real-time data processing make it possible both to detect cases as they occur and to help prevent them.
- Optimization of maintenance in industry: automated production lines are equipped with numerous sensors that collect data of all kinds. The processing of both historical and real-time data makes it possible to detect anomalies that anticipate the appearance of wear, defects or problems.
We help you move towards a future driven by artificial intelligence.
Adapting artificial intelligence prepares companies to adapt to a future where technological innovation will play an increasingly important role. Those companies that take advantage of this opportunity will be better prepared to succeed in a competitive and constantly evolving marketplace.
The path to successful implementation of artificial intelligence requires commitment, an innovative mindset and collaboration with partners that will foster success. If your organization is in this process and you are looking for a partner with experience in both the initial analysis and execution of artificial intelligence projects, do not hesitate to contact us.
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