How AI Supports Effective Decision-Making in Organizations
AI adoption is 2.5 times higher today than it was in 2017, according to McKinsey’s Global Survey on AI.
Image via McKinsey & Company
And one of the key areas that organizations are using AI is effective decision-making.
Through analyses of available datasets, AI is helping leaders make more accurate and consistent decisions. Fast.
Unlike humans, AI can process millions to billions of records for an organization and link relations between data elements, leading to more efficient decision-making.
Besides, it’s common for emotions, biases, and decision fatigue to cloud the judgment of humans, leading to errors.
But on the other hand, AI lacks empathy or the ethical and moral considerations that guide the course of business and society at large. That said, AI does have some inherent biases based on the human biases of its developers and the data it has been fed.
So, although AI can make unsupervised decisions based on the available data, parameters, and variables, organizations shouldn’t leave all the decision-making to machines.
It’s best to create a decision-making model that’s powered by both human intelligence and AI.
How is AI Involved in Effective Decision-Making?
When it comes to the use of AI in decision-making, we could classify it into two categories:
- Assisted intelligence
- Augmented intelligence
Assisted intelligence is where people get insights from data and then make a decision based on it. It’s purely data-driven decision-making.
In augmented intelligence, machine learning and predictive analysis of massive data sets are enhanced by human intelligence, helping organizations make more efficient decisions.
In this case, the decisions are not purely data-driven. They’re based on a partnership model of human intelligence and artificial intelligence that enhances outcomes.
So, by combining cloud-computing technology and data processing tools that help with data segmentation, validation, and processing, organizations can make speedy and consistent decisions.
The best part about AI is that it is constantly learning.
As it makes more data-driven decisions, AI algorithms build models that become more accurate at making predictions.
Organizations can then use these models to make decisions using AI-generated predictions made on live data and in real-time either by:
- Following the system’s suggestions
- Using the system’s suggestions within the organization’s decision-making framework
3 Ways AI Is Influencing Decision-Making in Organizations
AI encompasses any hardware or software component that can support:
- Natural language understanding (NLU), as used in AI chatbots
- Machine learning
- Computer vision
- Natural language processing (NLP), as used in tools such as AI text-video generators
Here are the ways these components can help in decision-making within organizations.
1. Making More Efficient and Effective Decisions In Business Operations
With a constant flow of large heterogeneous datasets, AI algorithms can help executives make more relevant decisions that are critical to business operations.
While the manual analysis of this data would be a time-consuming task for the decision-makers, AI completes it swiftly.
To address the challenges of customer relationship management, for instance, AI can help organizations with accurate decision-making in the following ways:
- Analyze contextual data and decide the best content to create for their target audience. Or find out the best adjustments to make to a failing marketing campaign
- Natural language processing or AI writing tools can help organizations understand how customers are interacting with their brand. This can help marketers use the words and tone that would elevate their content marketing game to make it more appealing to the target audience
- AI tools like chatbots can engage with customers and collect data about their expectations, pain points, level of interest in your products, and more. This can help organizations make the right decisions that increase their satisfaction levels.
- An analysis of existing customer datasets can help organizations identify patterns and predict future consumer behavior. It makes it easier to provide the right product or service recommendations to customers.
- Analyzing and filtering the active email IDs of the customers to build an email list for your business to use in generating good leads.
Not just sales and marketing, but business operations in all departments can benefit from AI.
In human resources, employees’ and applicants’ data can be massive and monotonous. Yet, accurate decision-making is essential in building the right workforce.
Organizations can come up with AI-powered solutions that can help with the categorization and evaluation of this data to source the right candidates, analyze the interviews, and hire the right candidate.
2. Solving Complex Problems
AI can also come in handy in helping organizations solve multilayer and complex problems.
One way is through AI-enriched simulation.
A simulation is a computer-based imitation of a real-world process or system.
The simulation uses a model that represents the key characteristics of the process or system in question. This helps identify how it would behave under different conditions.
AI-enriched simulations aid in decision-making by helping leaders test out different decision scenarios and show what the potential consequences could be. The best part is that AI enables quick simulations, which would, otherwise, take a lot of time.
By incorporating AI in the simulations, organizations are able to identify the most effective simulations to try out. It reduces the time taken to reach a decision and solve complex problems.
3. Simplifying Customer-Related Decisions
Organizations face customer-driven complexities in their everyday decision-making.
They must understand the needs and desires of their customers and align their decisions with those needs and desires.
This is one of the places where AI is helping in complex decision-making for organizations—providing customer-related insights from data.
Here are some areas AI can help organizations in customer-related decision-making.
Venturing Into New Markets
Think about launching a new product that targets a new market. There are decisions to be made about the first marketing campaign, product development, product promotion, and more.
A machine learning algorithm can use the buying behavior of current customers to predict whether the new market will be interested in the product and how likely they would be to purchase.
AI can also help organizations in the decision-making process of a pricing strategy for their products or services.
It makes it easier to predict how customers will react to different price points through the analysis of data from:
- Competitor analysis
- Surveys and customer reviews
- Historical prices
- Operational cost analysis
Identifying Potential Customers
AI simulation and modeling techniques can help organizations have a more accurate profile of their ideal customer.
Besides, applying artificial intelligence algorithms to the customer data your lead generation software collects can help you predict customer lifetime value (CLV).
By pursuing and monitoring customers with a high CLV, organizations can achieve steady growth.
AI plays an important role in eCommerce business by automating the customer management process. It sends personalized messages, reminders, and greetings that help in boosting customer experience.
Predicting customer lifetime value more accurately also helps organizations update their business plans and future budgets better.
Over to You
There’s no doubt that implementing technologies based on AI in your organization can make your decision-making process more effective. It’s a great way to give you more time to focus on creative tasks.
AI can make decision-making in your organization more data-driven.
When implemented correctly, it can provide unparalleled insights into every aspect of your organization and facilitate its growth.
So, go ahead and start using AI for your organization now.
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- How AI Supports Effective Decision-Making in Organizations - March 21, 2023