In today's landscape, companies face increasing challenges due to high tax burdens, inflation generated by the uncontrolled expansion of the monetary base, and an aging population.

These economic pressures limit the ability of companies to pass on additional costs to end consumers, forcing them to seek alternatives to improve internal efficiency and maintain competitiveness.

Why Use AI?

To survive and thrive in such a challenging environment, companies have begun adopting Artificial Intelligence (AI) as a strategic solution. AI offers ways to increase internal efficiency, reduce costs, and improve competitiveness without the need to raise prices for consumers. Automating repetitive tasks, freeing employees for more complex activities, and analyzing large volumes of data are some of the benefits that AI provides. These improvements are essential for companies needing to optimize processes and increase productivity, especially in a context of an aging population and high inflation.

Workforce Distribution and AI Impact

In the labor market, a significant part of a company's workforce is composed of back-office functions, which handle bureaucratic and administrative services not directly linked to the core activity. On average, 34% of employees in companies perform back-office functions, while the rest are divided between operational and management roles.

Operational Roles

Approximately 70% of employees in companies across various sectors are in operational roles. These positions include daily tasks such as file organization, inventory management, customer service, and logistical support.

Management Roles

About 30% of employees are in strategic or management positions, responsible for developing policies, managing project budgets, overseeing strategic initiatives, and ensuring compliance with safety and regulatory standards. AI application can reduce the need for operational roles in the back office while providing support for decision-making without negatively impacting operations for management roles. Thus, it is possible to maintain the company's efficiency levels without increasing the number of employees (headcount)​ (ChiefExecutive.net)​​ (World Economic Forum)​.

The AI Revolution Compared to the Industrial Revolution

Just as the 18th-century Industrial Revolution profoundly transformed production and manufacturing, the AI Revolution is significantly transforming the business world. Both revolutions brought deep technological and economic changes, but there are important differences between them.

Similarities:

• Technological Transformation: The Industrial Revolution introduced machines, while the AI Revolution introduced algorithms and learning systems.
  

• Economic Impact: Both revolutions created new industries and business models while making others obsolete.
  

• Productivity Increase: The Industrial Revolution enabled mass production, and AI is enabling the automation of complex processes.
  

• Social Change: The Industrial Revolution led to urbanization and the rise of an urban working class, while the AI Revolution is changing the nature of work, with an increasing focus on technological and analytical skills.

  
  

Differences:

• Speed of Change: The Industrial Revolution occurred over decades, while the AI Revolution is happening much faster.
  

• Scope of Transformation: The Industrial Revolution focused on physical production, while AI is impacting virtually all sectors, including services, healthcare, finance, and education.
  

• Nature of Work: The Industrial Revolution replaced manual labor, while AI is replacing cognitive tasks.
  

• Immediate Global Impact: The Industrial Revolution started in specific countries and spread over time. The AI Revolution has an almost immediate global impact due to digital interconnectivity and universal access to technology.

Levels of AI Systems

To better understand how AI can be applied, it is helpful to consider the different levels of AI systems: Descriptive, Predictive, Prescriptive, and Cognitive.

1. Descriptive Systems
   

   1. Objective: Provide insights about past events, focusing on summarizing historical data.
      

   2. Technologies: Reporting tools, dashboards, data warehousing, and business intelligence (BI) platforms.
      

   3. Output: Summary statistics, graphs, diagrams, and detailed reports.
      

   4. Use Examples: Sales reports, financial summaries, and historical trend analysis.
      

2. Predictive Systems
   

   1. Objective: Predict future events based on historical data.
      

   2. Technologies: Machine learning models, statistical analysis tools, and data mining.
      

   3. Output: Forecasts, probabilities, and future trends.
      

   4. Use Examples: Demand forecasting, risk analysis, and predictive maintenance.
      

3. Prescriptive Systems
   

   1. Objective: Suggest specific actions based on predictions.
      

   2. Technologies: Optimization algorithms, simulation models, and decision analysis.
      

   3. Output: Action recommendations, optimized strategies, and action plans.
      

   4. Use Examples: Production planning, supply chain management, and marketing recommendations.
      

4. Cognitive Systems
   

   1. Objective: Simulate human thought processes to learn, understand, and interact naturally.
      

   2. Technologies: Advanced artificial intelligence, natural language processing (NLP), pattern recognition, and deep learning.
      

   3. Output: Contextual responses, natural language interactions, advanced insights, and intelligent automation.

      
      

   4. Use Examples: Virtual assistants, chatbots, sentiment analysis, medical diagnostics, and personalized recommendation systems.

Conclusion

This is the goal behind Volis AI, a company dedicated to transforming chaos into order and providing insights and products that generate scale and productivity gains.

At Volis AI, we transform the way companies operate using data science and artificial intelligence, delivering complete solutions that unlock the full potential of data and drive tangible business results.