For entrepreneurs and SMEs: how to apply Artificial Intelligence to your business

For entrepreneurs and SMEs: how to apply Artificial Intelligence to your business

The Artificial intelligence It has become a powerful ally for entrepreneurs and SMEs to scale sales, reduce costs, automate tasks, improve customer experience, maximize productivity, improve decision-making and, ultimately, to be more competitive and profitable.

This acquires special relevance in an uncertain and changing market, marked by constant technological evolution that modifies the way in which we work, produce and consume.

The ability of companies to quickly adapt to changes and evolve according to market needs is key to guaranteeing their competitiveness.

Below, we list five tips for entrepreneurs and SMEs to apply artificial intelligence successfully:

Develop a data culture:

Today, the ability of companies to extract valuable and actionable knowledge from their data is a strategic and intangible asset.

Artificial intelligence and especially machine learning models (AI sub-field) feed on data, allowing companies to process large volumes of data and make predictions on a scale and scope that is impossible for humans to achieve.

Therefore, to leverage this technology, it is essential that companies move from a culture of data accumulation to one that considers them as strategic assets of the organization.

Define business objectives:

When starting a machine learning project, the first thing is to define the business objectives and what problems or needs related to these objectives are to be answered.

This first step is key to obtaining good results. There are different methodologies for approaching machine learning projects, such as CRISP – DM and TDSP, and they all agree on this point: the first step is to understand the business and its objectives.

Define the business question to solve:

Machine learning models can be applied to answer different questions and business needs. The clearer this question is and the more aligned it is with the objectives of the company, the better results will be obtained. Some of the business problems that can be addressed with machine learning algorithms are:

  • Manage the relationship with customers, through segmentation and clustering algorithms, the so-called recommendation engines, life cycle value, abandonment prediction and price optimization, among others.
  • Improve marketing and advertising campaigns, through segmentation algorithms, among others.
  • Manage human talent, with segmentation and abandonment prediction algorithms.
  • Reduce costs, with demand prediction algorithms, credit scoring and fraud detection, among others.
  • Predict demand, sales and manage inventory stock, with demand prediction algorithms.
  • Sell ​​more, with segmentation algorithms, recommendation engines and price optimization, among others.
  • Automate administrative tasks, with Natural Language Processing (NLP) and business applications, among others.
  • Improve data analysis with different machine learning algorithms that can be combined with Business Intelligence to obtain powerful visualizations in real time to improve decision making.
  • 24/7 online support with chatbots powered by artificial intelligence. This is one of the use cases most used by companies to have an online and personalized presence that allows converting mere visitors to our website into customers.

Data availability and quality:

It is estimated that around 80% of the work in a machine learning project corresponds to the data extraction, exploration and preparation (ETL) stage. The availability and quality of the company’s data will be a key variable in the time and cost of the project, as well as in the results that can be obtained.

Define what will be understood by success and what will be the final deliverable:

Finally, it is important to define what the client’s expectations are, what will be understood as success and if the project will end with the issuance of a report with the results and recommendations; if the trained machine learning model will also be delivered to the IT sector of the company or if the implementation of the trained model in dashboards, boards, websites or applications will be contracted, which is known as MLOps (Machine Learning Operations).

Founder and CEO of LexRock. BI, Machine Learning and Automation.

Source: Ambito

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