In just seven decades, we’ve evolved from solely relying on our intellect to make business decisions to a more effective and efficient means – intelligent decisioning. Businesses require data for operational decision-making; that’s why we collect as much of it as we can. However, despite having such an abundance of data at our fingertips, making the right decisions based on the data can be pretty hard because, as humans, we are inherently biased, even when we don’t mean to be.

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Artificial intelligence removes the element of bias in decision-making. It can also help businesses make better decisions by leveraging datasets to make fast, accurate, and consistent decisions. Intelligent decisioning is catching on so fast that we’ve seen a 270% growth in just four years. In 2015, only 10% of businesses leveraged AI in decision-making. Fast forward to 2019, and the number has risen to 37% [1].

This article will break down the role of artificial intelligence in decision-making, from what it is to how you can apply it to make better, more informed business decisions. Read on to learn more.

What is AI decision-making?

AI decision-making is the process in which data processing roles like trend analysis and data crunching are done by an AI platform rather than a human entity. By handing over complex datasets to an AI platform, businesses can ‘outsource’ complex tasks like data crunching, anomaly detection, trend analysis, and complex analysis. This way, human resource teams have more time on their hands to focus on other business-related matters.

AI decision making processSource: Eric Colson

Although using AI in intelligent decision-making makes the process much easier and faster, it completely cuts out the human element – which is not advisable. Therefore, to circumvent this drawback, many businesses are now combining humans with intelligent decisioning, whereby AI does all the work, but a human makes the final decision.

Experts predict that by 2030, roughly 70% of businesses will be using AI technologies, and around 50% of all large enterprises will have a full range of AI technologies embedded in their operations [2].

Soon, the continued survival and subsequent success of any business will depend almost entirely on its ability to implement an agile, data-centric infrastructure to make business decisions [3].

How does AI help in decision-making?

One of the most notable aspects of AI is machine learning. It gives AI a unique ability to constantly teach itself. Basically, the more data-driven decisions it makes, the more it learns. As it continuously trains itself, it can use collected data to build efficient models that can then be used to make categorizations, predictions, and recommendations that ultimately enable businesses to make informed commercial decisions.

Here are a few other ways intelligent decisioning can help in business:

MARKETING DECISIONS

Many businesses are often overwhelmed by the numerous complexities involved in making marketing decisions. For starters, a business must have a thorough understanding of its customers’ needs and expectations. It should then offer products and services aligned with the relevant needs and expectations.

the way of how intelligent decisioning can help your business

A business should also have a firm grasp of consumer behavior before making any marketing decisions. Artificial intelligence technologies can give better insights into the customers’ viewpoints through models and simulations.

Additionally, intelligent decisioning aspects like decision support systems are better at predicting consumer behavior through rational decision-making techniques. With these systems in place, a business is better able to make real-time decisions based on data forecasting and market trend analysis.

It might be interesting for you: Problems of artificial intelligence implementation: How to overcome them

MANAGEMENT DECISIONS

Before AI, business executives used to rely solely on their intellect derived from inconsistent and incomplete data. The advent of intelligent decisioning brought data-based models and simulations that managers could now look up to.

This is especially true for businesses that have applied augmented intelligence to their operations. Artificial intelligence in decision-making offers executives comprehensive models and simulations that act as a basis for their decision-making process.

intelligent decisioning

AI also enhances automation, which in turn, reduces human-intensive labor and tedious tasks. Several sectors like weather forecasting and disaster management are already using intelligent decisioning to make more effective decisions on the go.

The continuous utilization of AI in making management decisions also improves productivity tremendously. It is estimated that AI will contribute at least $13 trillion by 2030[4]. To put it in perspective, this represents a 1.2% increase in the global GDP every year.

CUSTOMER RELATIONSHIP MANAGEMENT

23 percent of all customer service organizations currently use AI[5] to drive deeper connections with their customers. The added flexibility provided by AI enables businesses to track consumer behavior, which they then use for micro-targeting and lifecycle analysis.

AI has also enabled the automation of several other functions like contact management, data recording, data analysis, and lead ranking. Synergistically, these functions can help your marketing teams work more effectively and efficiently.

AUTOMATION EFFICIENCY

Traditionally, AI-powered operation efficiency was primarily centered around assembly line processes. But now, the use of AI-generated operational efficiency has transcended into the business processes space as well, with business functions such as marketing and distribution adopting automation as well.

AI also provides businesses with reliable insights into customers. With these insights, businesses can enhance their customer interactions and have a seamless decision-making process, especially when it comes to marketing.

AI implementation statistics

With AI, retailers are also able to monitor and control the market. They can as well predict and respond to product demands more accurately through:

• Opinion mining: This is an AI-assisted data mining process that focuses on finding and understanding the general sentiment about a product or service through a web search. It gives marketers a better understanding of how customers view their products.

• Social computing: AI can simulate, predict, and analyze human behavior. These capabilities give marketing professionals a better understanding of the social behaviors and dynamics of a target market.

RECOMMENDATION SYSTEMS

This form of AI capability was first deployed on music content sites. Since then, it has been adopted across numerous industries. The system works by first learning about a user’s preferences, then subsequently pushing forward content that fits the preferences.

The recommendation system increases the number of purchases on eCommerce sites and significantly reduces the bounce rate. Additionally, the data obtained through the process can help the AI system target more relevant content.

Read more about Product Recommendation System: Algorithms, Challenges, Benefits

Can AI make better decisions than humans?

Business decisions are made based on insights derived from collected data. Traditionally, decision-makers used basic processes like spreadsheets and databases to categorize and analyze data. However, due to the vast amount of data being collected by modern businesses, these processes no longer cut it; and humans, on their own, just can’t keep up.

That’s where artificial intelligence in decision-making comes in. AI drives intelligent decisioning by leveraging available data to come up with data-driven insights. However, this form of decision-making removes humans from the equation, either wholly or partially. Thus, begging the question, should we keep humans in charge, or leave decision-making to the machines?

Intelligent decisioning

Current AI decision-making applications have adopted a hybrid structure in which artificial intelligence systems process the data and present it for decision-making (which is done by a human entity).

Although this is better than solely relying on intuition or giving all the decision-making power to a machine, the mere fact that humans still play the role of the central processor presents a few limitations [6], including:

WE DON’T LEVERAGE ALL THE DATA

Artificial intelligence systems summarize data so we can gain insights from it. Unfortunately, summarized data can obscure many of the patterns, insights, and relationships contained in the original dataset.

Despite the limitations it presents, this form of data reduction is necessary since humans cannot process vast amounts of structured data. The human mind can handle grouped data but starts to shut down when you’re dealing with millions or billions of independent data elements.

Summaries work well in providing basic visibility but offer little value when it comes to intelligent decisioning. Some important data is lost during the preparation process, and sometimes the summarized data can be outright misleading.

Think of it this way; confounding factors can give the allure of a positive relationship when it is actually negative. Moreover, once the data is aggregated, it is nearly impossible to recover contributing factors, so you can create a control for them using randomized control trials like A/B testing. Therefore, by retaining humans as the central processor, businesses trade-off accuracy.

WE LET BIAS TAKE OVER

The amount of data we have on our hands isn’t enough to insulate us from cognitive bias. When data summaries are directed by humans, they direct the summarization in a way that is intuitive to them, thus adding an element of bias.

AI technology statistics

Humans also tend to classify datasets into broad stereotypes that, in most cases, can’t sufficiently explain their differences. We also prefer simple relationships between elements. That’s why we think of most relationships as linear because it’s easier to process (even when the data says otherwise).

Apart from processing power and storage, the major difference between humans and AI lies in the fact that AI doesn’t have feelings. Unlike humans, AI technologies can collect data and derive meaningful conclusions without letting bias, emotion, or human error get in the way. That said, AI is developing at such a tremendous rate that some experts predict that it may surpass humans in every aspect by 2060[7].

Will AI eliminate the need for humans in decision-making?

Although artificial intelligence can collect and analyze data quicker and come up with helpful insights, that doesn’t mean you should leave all your business decisions in the hands of a machine – a successful business needs a human touch.

Because, despite our limitations, there are numerous ways in which humans can influence decision-making for the better. Whether it’s by making compassionate customer decisions, creating a bold and adventurous advertisement strategy, or making a killer marketing copy, the human mind is still vastly superior to the most advanced AI technologies.

The bottom line: Artificial intelligence in decision-making

The sheer amount of data collected by businesses is nothing short of overwhelming for the human mind, particularly one looking to make informed decisions based on the data. Thanks to intelligent decisioning business executives can take advantage of available data to make meaningful decisions that ultimately make them more profitable and competitive.

 

[1] Gartner.com. Gartner Survey Shows 37 percent of Organizations have AI implemented in Some Form. URL: https://www.gartner.com/en/newsroom/press-releases/2019-01-21-gartner-survey-shows-37-percent-of-organizations-have. Accessed May 13, 2022
[2] Mckinsey.com. Impact of AI on the World Economy. URL: https://mck.co/38vku17. Accessed May 13, 2022
[3]Gartner.com. Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019. URL: https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo. Accessed May 13, 2022
[4] Mckinsey.com. The Impact of AI on the World Economy. URL: https://mck.co/38vku17. Accessed May 13, 2022
[5]Sfdcstatic. com. State of the Connected Customer Report. URL: https://c1.sfdcstatic.com/content/dam/web/en_us/www/assets/pdf/salesforce-state-of-the-connected-customer-report-2019.pdf. Accessed May 13, 2022
[6] Hbr.org. What AI-driven Decision-making Looks Like. URL: https://hbr.org/2019/07/what-ai-driven-decision-making-looks-like. Accessed May 13, 2022
[7] Newscientist.com. AI Will be Able to Beat Us at Everything by 2060. URL: https://www.newscientist.com/article/2133188-ai-will-be-able-to-beat-us-at-everything-by-2060-say-experts/. Accessed May 13, 2022

 

 

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