Overview of Artificial Intelligence-Driven Business Decision Making

artificial intelligence

There are a lot of corporates that have adopted a data-driven approach for business decision-making. There is no doubt that quality data can improve business decisions than the intuitive approach, but it requires the right processes to get the most out of the data. A lot of them assume that the processor here is human. The concept of a data-driven approach may imply that the data is curated or summarized by people for the process. This perspective often overlooks the role that artificial intelligence can play. AI can significantly enhance data processing and analysis.

However, in reality, to fully leverage the value of data, organizations need to bring artificial intelligence practices into their workflow and get humans out of their way. The objective here is that we have to evolve from the data-driven approach to an AI-driven workflow now.

However, distinguishing between this data-driven versus AI-driven is not just about semantics. Each of these terms reflects different sets of assets. The data-driven approach focuses more on data and the artificial intelligence-driven one is largely dependent on processing ability. These ideally hold the insights that can enable better decisions, whereas processing is how to extract the insights and take them into action. Both humans and AI are processes with varying abilities. To effectively leverage each of these approaches, we need to review the evolution of decision-making in business. This topic will be discussed in detail in another article in this series.

Data-driven decision making

Thanks to the advanced data management tools available today, connected devices can capture vast volumes of data. This includes every transaction and all microeconomic indicators. Additionally, it can even track customer gestures. All this information can be compiled to make informed decisions. In response to this, data-centered companies have also adopted their workflows differently. IT departments support the inflow of information using machines, distributed file systems, databases, etc., to reduce the manageable volume of data to easily digestible summaries for instant consumption.

Humans further process these summaries using tools like spreadsheets and dashboards for advanced analytical applications. Eventually, they present the highly processed and manageable volume of data for effective and insightful decision-making. This is a data-driven approach. So, as we can see, human judgment is still the central processor in this approach. Even though it is undoubtedly better than our previous practice of relying solely on human intuition, the data-driven approach still has many limitations. For example, even if we incorporate a certain volume of data, we may not leverage all the data available. It should also be noted that it is not easy to insulate the decision-makers from all cognitive biases. When it comes to data-centered decision-making, consultants like RemoteDBA can also help remote database administration for deriving real-time data and info.

Integrating Artificial Intelligence in Decision Making

To deal with the shortcomings of the data-driven approach, we have to evolve further and bring artificial intelligence into the workflow as the primary data processor. For the routine decisions that rely only on structured data, we may better delegate these decisions to artificial intelligence. As far as we know, it is less prone to any human cognitive bias. On the other hand, there is a large risk of using biased data, which may cause AI also to find unfair relationships.

Harnessing Artificial Intelligence for Enhanced Decision-Making

Understanding how the data is generated, where it comes from, and how it is used is essential. Artificial intelligence can effectively learn over time to identify the segments in the population that best explain the variants at the grassroots level. This capability can reveal insights that may be intuitive to human perception. AI also has no shortfalls while dealing with millions of such groupings. So, we can see that AI is more than comfortable while working with nonlinear relationships, geometrical series, exponential data, binomial distributions, and so on.

The Role of Artificial Intelligence in Transforming Decision Processes

Let us take the example of Volvo using standard artificial intelligence applications to generate more and more data from their vehicles fitted with many sensors for security purposes. Volvo effectively uses AI and IoT to uphold its reputation in terms of safety. Back in 2015, Volvo fitted their first 1,000 cars with sensors to capture and analyze driving conditions. This setup allows them to monitor their vehicles’ performance in hazardous situations. The data is sent to the cloud, where Volvo collaborates with Teradata to run machine learning analyses. The early warning system developed by Volvo now analyzes about a million events per week to predict any failures or breakdowns in their cars.

Artificial Intelligence-based workflows source data from the big data pools and process uses AI applications and passes on for data-driven insights to the business decision-makers. AI apps can better leverage the information in the data and deliver it consistently for making decisions. It can better determine which ads are more creative and effective in marketing settings. Additionally, it can identify optimal inventory levels for retail. Furthermore, AI can help investors understand which financial investments are the right choices in real-time.

While we remove humans only from this workflow, it is also important to know that complete automation is not the ideal AI-driven workflow. The actual value of artificial intelligence is to do better decision-making than what humans alone can do on their own. This creates a step-change improvement in efficiency and can enable new capabilities.

Combining AI and human processes in the workflow

Removing humans from the workflows and making these only involve processing structured data does not mean that humans are obsolete. On the contrary, there are many business decisions that depend on things more than structured data. For example, company strategies, vision statements, and corporate values are all forms of data and information. We can access these elements through our cognitive sense, and they transmit through the specific culture of an organization. Additionally, they communicate through non-digital means. All this information remains accessible to any application and is also crucial in terms of insightful business decision-making.

Artificial Intelligence in Optimizing Inventory and Marketing

In another instance, artificial intelligence can objectively determine the right inventory levels to optimize profits. In a competitive environment, an organization may sometimes choose to maintain higher inventory levels to enhance customer experience. This decision can be made even at the expense of profits. In other instances, AI may determine that investing more money in marketing could yield a higher return compared to various other options.

This approach allows companies to strategically balance customer satisfaction and profitability. On the other hand, a company may choose to tamper with the growth to uphold the quality standards. AI may suggest that organizations reduce human workloads in some cases. Alternatively, decision-makers might use human judgment as input for AI processing in other instances. Many ideal cases demonstrate an effective iteration between human processing and AI.

To conclude the key to success is that humans are not interfacing directly with any data but rather with the possibilities produced by artificial intelligence data processing methodologies. Strategies, values, and culture are the way to reconcile the decisions by putting in objective rationality. This can be done at best by leveraging both AI and human processes for better business decision-making. For more insights on this topic, you can read about the implications of artificial intelligence in business or explore further with this external resource on AI in decision-making.

Author’s Bio:

Walter Moore is a writer and notable management and digital marketing expert at RemoteDBA. He is an experienced digital marketer who has helped e-commerce businesses in all niches gain with his effective marketing strategies and guidance

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