An Overview of Artificial Intelligence-Driven Business Decision Making

An Overview of Artificial Intelligence-Driven Business Decision Making

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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 even imply that the data is curated by or summarized by people to the process.

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 AI-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 understand how to leverage each of these at best, we need to review the evolution of decision-making in business, which we will discuss in detail in another article in this series.

Data-driven decision making

Thanks to the goodness of the data management tools available now, the connected devices can capture unimaginably huge volumes of data, including each transaction, all microeconomic indicators, and even 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.

These summaries are further processed by humans using tools like spreadsheets and dashboards for advanced analytical applications. Eventually, the highly processed and manageable volume of data is presented 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.

Bringing artificial intelligence into 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.

So, you need to be sure in terms of understanding how the data is generated and from where it comes, and how it is being used. AI can be effectively trained over time to find the segments in the population which best explain the variants at the grassroots level, even if they are 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.

Let us take the example of Volvo using standard AI 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 itself, Volvo fitted their first 1000 cars with sensors to capture and analyze the driving conditions to monitor their vehicles' performance in hazardous situations. These data get on to the cloud, and Volvo further works on it with Teradata to run analysis driven by machine learning across the data. The early warning system developed by Volvo analyzes about a million events per week now for predicting any failure or breakdowns in their cars.

AI-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 also better determine which ads are more creative and more effective in marketing settings, identifying the optimal inventory levels for retail, understanding financial investment is the right choice for investors, etc., 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 AI 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, you can find the company strategies; vision statements, corporate values, market dynamics, etc. are examples of data and information available in our cognitive sense and transmitted through a specific culture of an organization or non-digital communication. All this information remains accessible to any application and is also crucial in terms of insightful business decision-making.

In another instance, AI can objectively determine the right inventory levels to optimize profits. Still, in a competitive environment, the organization may sometimes go for higher inventory levels to provide a better customer experience, even at the expense of their profits. In some other cases, AI may determine that investing more money into marketing may offer a higher return among various other options available to a company. On the other hand, a company may choose to tamper with the growth to uphold the quality standards. In some cases, AI may suggest reducing human workloads, and in some other cases, human judgment may be used as inputs for AI processing. In many ideal cases, there may be an ideal 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 AI 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.

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

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.

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