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**How can AI Help Business Decision-Making Productivity Today?**

Artificial intelligence (AI) has moved from the realms of science fiction into the practical conversations of business people. However, with all the hyperbole and disillusionment surrounding AI, companies need to find ways to utilize AI effectively to improve business decision-making productivity. By breaking down the decision-making process into three phases – framing, deciding, and learning – companies can leverage AI to its full potential.

**Framing – How can AI help in decision framing?**

In order to make better decisions, it is crucial to clearly understand the problem and the questions that need to be answered. Unfortunately, decision-making biases often hinder this process. People tend to ask questions that align with their preconceptions, recent information, optimistic views, or the opinions of their team (groupthink). These biases lead to narrow decision-making frameworks.

To overcome this, decision-makers need a partner who can broaden their perspectives and stimulate out-of-the-box thinking. Generative AI models like ChatGPT can act as decision coaches and brainstorming partners. They can provide suggestions and prompt decision-makers to ask critical, insightful questions that they might have missed.

For example, a consumer goods company used ChatGPT to improve their change-management decision-making framework. Through interactions with ChatGPT, the decision-maker was able to update their questions and think more broadly about the implications of their proposed solutions. This partnership between imperfect AI and biased experts can revolutionize decision-making now and in the future.

**Deciding – How can AI provide insights for making the right decision?**

Creating a super-intelligence that can analyze data, understand situations, and give the “right answer” has always been an aspiration for AI technologists. However, generative AI models like ChatGPT do not fulfill this desire for a few reasons.

First, decision-makers require insights and recommendations from domain experts who possess deep knowledge of their specific industry and situation. ChatGPT’s insights might be thought-provoking, but they lack the expertise needed for confident decision-making.

Second, generative AI models are not designed to analyze large volumes of business data. They are not a substitute for data analysts who can interpret complex data sets. Instead, decision-makers need synthesis AI models that can extract insights and recommendations from vast amounts of business-specific information and present them in a straightforward manner.

Lastly, AI-generated insights and recommendations must be transparent and traceable. Decision-makers need to understand the logic behind AI recommendations and be able to connect them directly to specific enterprise data. This transparency and traceability build trust in the AI system.

For instance, a food company used AI-driven insights and recommendations based on their brand health data. The decision-makers could trace back the AI’s recommendations to specific data points and understand the underlying business dynamics that led to those recommendations. This transparency and traceability allow decision-makers to make informed judgments and confidently make the right decisions.

**Learning – How can AI improve decision-making over time?**

The true value of AI in decision-making is unlocked when companies incorporate decision intelligence systems. These systems create a record of past decisions and their outcomes. These detailed records enable companies to analyze the effectiveness of their decision-making processes and continuously improve over time.

By analyzing the outcomes of past decisions, decision intelligence systems can identify patterns, trends, and areas for improvement. AI can play a vital role in this process by providing insights and recommendations based on historical data. Decision-makers can leverage these insights to refine their decision-making frameworks and increase the overall efficiency of their operations.

In summary, to cut through the AI hyperbole and disillusionment, companies should view AI as a tool to enhance decision-making productivity. By leveraging AI in the framing, deciding, and learning phases, businesses can broaden their perspectives, gain valuable insights, and continuously improve their decision-making processes. AI, when used effectively, is a game-changer for companies seeking to make better, faster decisions.

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