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Artificial Intelligence is revolutionizing the business world, from the most technical tasks to strategy. Not surprisingly, the increased use and applications of AI in business are included in Gartner’s 2024 technology trends. This annual publication by the technology consulting firm aims to highlight the rapid expansion of AI and what innovations can accelerate business progress. In addition, Gartner predicts that by 2033, AI solutions implemented to improve or perform tasks, activities or jobs autonomously will have created more than 500 million new human jobs.

Likewise, on May 14, the Government of Spain approved the Artificial Intelligence Strategy 2024. This strategy reinforces Spain’s commitment to the technological vanguard, developing three axes: strengthening capabilities for the development of AI, facilitating the application of AI in the public and private sector, and promoting transparent, ethical and humanistic AI.

AI and business

When we talk about artificial intelligence in business, we can include a wide variety of practices, ranging from analytics, to task automation, from data analysis to personalization of customer experiences. AI is not limited to analyzing the present, but has predictive capabilities to anticipate potential future events. Thanks to the incorporation of historical data from

By incorporating historical company data, market trends and competitor actions, AI tools provide comprehensive insight to make informed decisions.

Despite the obvious advances in AI tools and their widespread use, according to the consulting firm McKinsey, their application in areas such as marketing, supply chain and service operations is as low as 25 to 30 percent. This figure drops to 7 percent when it comes to using AI for financial strategy and planning. This is due to management’s distrust of the substantial help AI would provide for core components of strategy, with the goal of improving, rather than directly delivering, the bottom line.

As Forbes and CapGemini highlighted in the event “AI Revolution II: Accelerating Business Value” held last March, the first challenge companies face when implementing AI solutions is the acceptance and adaptation of people to it. Likewise, it is important to highlight that “AI must be at the service of the company’s objectives, and not the other way around,” as stated by Ubaldo González, director of Data Analytics and AI at MAPFRE Spain, and Elena Gil Lizasoain, director of Artificial Intelligence and Big Data at Telefónica Tech.

AI and business strategy

According to McKinsey, there are six stages in the development of AI, three of which are not yet available today.

Starting with simple analytics, this is known as descriptive intelligence and includes the development of dashboards to carry out a comprehensive competitive analysis or to monitor some performance indicators of the business in question and compare it to strategic objectives. According to Gartner, business KPIs should focus on:

  • Business growth, with indicators on potential price increases and demand estimation.
  • Customer success, measuring customer retention and satisfaction or share of wallet.
  • Profitability, in terms of inventory, production costs and employee productivity.

At the next level we find diagnostic intelligence, which allows companies to make an assessment of their own history to understand the factors that led to the current situation. This level seeks to understand the causes of the results observed in the descriptive intelligence through advanced techniques such as data mining and root cause analysis. For example, you could analyze the reasons why sales in a specific region are declining,

The third level includes predictive intelligence. As the name suggests, this is about anticipating potential future scenarios and assessing in advance the possible impact of certain events in the future based on historical data. AI can generate more accurate forecasts about the future of a business, using machine learning techniques and statistical models to predict future trends and events to anticipate both risks and opportunities. This enables consultants to develop long-term strategic plans with greater confidence, with the aim of making proactive decisions and optimizing strategies. Examples of AI application at this level would be supply chain management or pricing, which can lead to greater efficiency and profitability. AI tools could also predict product demand to optimize inventory management or identify potential customers most likely to buy.

Thanks to AI, companies can shift their focus from tasks to data analysis. In the current context, AI must be understood as a knowledge engine, which brings a substantial advantage not only in terms of time and cost. However, AI tools have a capacity to deduce results faster and at increasingly lower cost. Nevertheless, the contribution of AI in the strategic field will greatly simplify the complexity of data, providing more accurate and structured information, and a more complete knowledge of the company.

Collaboration between AI and professionals

Overall, the use of AI in strategy has the potential to transform the way businesses operate and compete. However, it is important for organizations to be aware of the potential limits and challenges. The key to success is to look for human complementarity. AI does not replace human judgment, but enhances it. It does not make decisions for managers, but rather streamlines the most repetitive and automated work, and above all provides the knowledge needed to maintain a substantial competitive advantage.

As Yuval Atsmon, a senior partner in McKinsey’s London office, states in the podcast on “Artificial Intelligence in Strategy,” the most common question when a manager is confronted with AI is “What if the AI makes mistakes?” The first answer is that humans make mistakes too, and make a lot of mistakes. Therefore, involving people who have a full understanding of the business is imperative to solve potential strategy problems with AI. According to Orange Spain’s Digital Sales Director, Thibault Bonneton, “Human interaction is not going to change, but it is going to become a hybrid model, where we will have to learn to talk to the ‘machine’.”

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