What is AI decision making?

A single person spends 150 minutes a day making decisions. Imagine removing the burden of constant decision making and the transformative impact it could have on your life. It’s the same concept for businesses. Employees are making hundreds of decisions a week affecting millions of customers. AI-driven decision making is beginning to transform businesses by automating manual and repetitive tasks, harnessing advanced insights, and experimenting to achieve optimal outcomes. AI isn’t here to replace people; it’s designed to free up valuable time by handling the tasks that AI is good at, enabling you to focus on tasks that require creativity, critical thinking, and human expertise.

To help you understand more about what AI decision making is, this article will explore:

• What is AI decision making?
• Why should you let AI make your decision?
• How does AI decision making work?
• How AI decision making impacts teams
• Real-world examples of AI decision making
• How to implement AI decision making

What is AI decision making?

AI decision making combines artificial intelligence with your proprietary data, such as product information, customer interactions, and historical trends, to determine what creates the best outcome and automate manual tasks.

AI decision making is crucial because it saves you time you would otherwise spend on often time-consuming decisions. It leverages existing data to identify patterns you might have overlooked, driving toward optimal outcomes. AI continuously refines its results through experimentation to achieve the best possible outcome.

Why should you let AI make your decisions?

One of the most critical tasks in any business is making decisions. As your business grows, the number of decisions you face also increases, requiring more time and resources. Before you know it, you’re bogged down in decision-making, leaving no room for anything else. That’s where AI becomes invaluable—streamlining decisions so you can focus on what truly matters. Here are some benefits of using AI to make decisions:

• Scale decisions without adding headcount: AI can help you manage large volumes of decisions and scale at a rate far beyond human capability, such as personalizing marketing campaigns for millions of customers. This enables your business to grow without the need to increase staff.
• Free up your team to perform high-value tasks: By automating repetitive tasks like data entry, A/B tests, or customer support inquiries, AI frees you to focus on strategic projects—like launching new campaigns, building client relationships, or refining market strategies—that directly contribute to business growth.
• Improve outcome from continuous learning: AI can help you analyze every decision, learning from outcomes to refine future strategies. For example, a recommendation engine on an e-commerce site can adapt based on customer behavior, leading to more accurate product suggestions, increased conversions, and higher customer retention over time.
• Enhances decision-making: AI helps you minimize emotional or biased decision-making by using data to guide actions. For instance, instead of relying on intuition to target a market segment, AI can analyze customer demographics and purchasing patterns to recommend the most profitable audience, resulting in smarter marketing investments and reduced risk.

How does AI decision making work?

AI decisioning uses machine learning and reinforcement learning to help you make decisions on a scale beyond human capabilities, effectively driving business outcomes. AI Decisioning harnesses customer data to determine the best decisions, whether it’s the ideal tone of an email, the most appealing product, or the most effective channel and timing, and continuously experiments to optimize results for each customer.

Consider the Amazon shopping experience. For instance, a user who frequently shops for sportswear and spends most of their time on a smartphone might receive notifications about new running shorts as soon as they become available. Another user, who prefers sportswear but is more responsive to email, would receive an email promotion highlighting the latest running shorts. In this case, AI decisioning is optimizing the experience for both users based on their preference, making it more likely that they perform the outcome you’re driving to.

Effective AI Decisioning relies on three key components: AI agents, reinforcement learning (RL), and large language models (LLMs).

AI agents

AI agents can help you to automate tasks that drive your desired outcomes. They gather data, make decisions, execute actions, and learn from feedback. Think of AI agents as your own "digital marketers" assigned to each customer, working to deliver the most personalized experience possible while aligning with your specific goals.

An AI agent can help you to recommend the best products or offers for your customers faster and more accurately than a human. For instance, an AI agent might display a tailored product suggestion in a pop-up for a returning website visitor while showing a welcome banner to a first-time visitor offering free shipping on their first order.

Reinforcement learning

Reinforcement learning (RL) can help you improve every decision by analyzing its outcomes and identifying patterns that lead to success or failure.

For example, imagine a product recommender for an online retailer. Initially, the AI suggests popular items to new customers. It refines its recommendations by observing which products customers click on or purchase. Unlike static "top sellers" lists or one-time A/B tests, reinforcement learning continuously adapts its suggestions based on real-time results. Each positive action, such as adding an item to the cart, signals that the AI is on the right track, allowing it to deliver increasingly accurate and relevant recommendations with every interaction.

Large language models

Large language models (LLMs) can help you analyze your content to assess its tone, structure, and style, adding a layer of depth by tagging each component. These tags enable the AI to better understand your customer's preferences, personalizing their experience with content that resonates with them and is more likely to drive your desired outcome.

For instance, an LLM might tag a piece of content so that AI agents know to adopt a friendly and approachable tone for loyalty program updates for a certain customer while using a sense of urgency for another.

Bring it together: real-world example

With all three components working alongside your customer data, you have the foundation for effective AI decision making. Here’s an example of how an online grocery store might use AI decisioning to drive add-on purchases:

• The marketing team defines its goal (increase order size) and provides treatment components such as email templates, product recommendations, and promotional offers to increase revenue.
• AI agents analyze purchase patterns. For example, if a shopper recently purchased pasta, the system might identify a discount on marinara sauce as a highly relevant suggestion to encourage an upsell opportunity.
• Reinforcement learning optimizes campaign performance by learning from past data and the outcome of the experiments to identity future opportunities. If historical trends show that bundle offers outperform standalone discounts, the system adjusts its strategy to include complementary products in its recommendations.
• Large language models enhance this process by tagging content for the tone, structure, or style, so the right preference can be delivered to the customer. For instance, a push notification promoting dinner ideas might adopt a cheerful and helpful style to align with a customer's preferences.

How AI decision making impacts teams

AI is versatile and can benefit any team if you provide a set of inputs for experimentation, relevant data, and a clear outcome to aim for. Here are some AI decision making use cases that companies are taking advantage of:

Sales

Every sales team is focused on hitting their sales targets each quarter. They do this by finding potential buyers, researching their wants and needs to better position what they sell, and continuing to nurture existing accounts through the pipeline. AI can help sales with:

• Prioritizing leads and accounts to focus on: When dealing with a large pool of potential customers, it's challenging to determine which ones deserve the most attention. AI Decisioning helps you predict which customers are the best fit and most likely to convert, prioritizing them based on past interactions, demographic data, and purchasing intent. This helps the sales team by increasing the number of closed deals, accelerating deal closure as they focus on highly interested prospects, and boosting overall productivity by minimizing time spent on low-potential opportunities.
• Deciding the next best action for each account: In sales, every action brings you closer to closing the deal, but determining the best next step often involves guesswork. AI Decisioning eliminates this uncertainty by identifying the most effective next action. This could be sending a follow-up email, scheduling a demo, or offering a discount. The AI analyzes factors such as the current pipeline stage, customer engagement level, and historical preferences to tailor the next step for each account. By leveraging AI for decision making, you can achieve higher engagement rates, as customers receive communication that resonates with them, and improved win rates, as sales efforts become more targeted and effective.

Marketing

Your marketing team wants to drive revenue growth by optimizing each step of the customer lifecycle funnel across multiple channels. AI can assist in this effort by:

• On-site and in-app personalization: Because of many elements, like resource and data constraints and technology, customers are often grouped into broad segments and given the same generic experience. While this approach may work somewhat, it often results in a lack of true personalization. AI Decisioning lets you show each customer the most relevant content or products. It achieves this by analyzing browsing and purchase history and comparing behavior with similar customers. This level of personalization leads to improved click-through and conversion rates, as customers are presented with content and products that genuinely match their interests.
• Optimizing cross-sell journeys: When you offer multiple products, determining the next best product to recommend to a customer can be challenging, let alone deciding the most effective message, offer, or timing. Often, businesses default to showcasing the most popular or closely related products, which may not always be the best choice. AI Decisioning overcomes these challenges by analyzing user preferences, purchase history, and behavior to recommend the most relevant product. It also identifies the most effective channels and strategies to maximize conversions. By leveraging AI to optimize your cross-sell journeys, you can achieve higher average order values, increased customer lifetime value, and improved conversion rates.
• Finding disengaged users and winning them back: Every customer has unique reasons for disengaging, and addressing each concern can feel overwhelming—especially when identifying which users are likely to churn. This is where AI steps in. AI Decisioning helps you to predict which users risk churning by analyzing their engagement patterns. It then determines the most effective offers, reminders, content, and communication channels to re-engage these users and win them back. Enhancing your win-back strategy with AI can significantly reduce churn and improve customer retention.

Product

Product team concentrates on driving feature adoption and reducing churn. AI can support the product team by:

• Streamlining onboarding: Every customer has unique reasons for choosing your product, making it challenging to design an onboarding experience that meets the needs of each individual and leads them to adoption. Typically, a few generic onboarding flows are created for all customers, often resulting in a disjointed and less effective experience. AI Decisioning can help you transform this process by analyzing data collected during sign-up, feature usage, and drop-off points. This enables AI to identify the most effective onboarding steps tailored to each customer, ensuring they fully adopt the product and achieve their desired outcomes.
• Boosting feature adoption: When your product offers numerous features, it’s easy to focus solely on promoting the most profitable or popular ones. However, each customer has specific needs and goals, making them more likely to benefit from a particular feature that addresses their unique challenges. Identifying and promoting these features without significant manual effort requires AI. With AI Decisioning, you can predict the most relevant features for each customer by analyzing feature engagement and usage patterns. It can also determine the best channels and messaging strategies to promote those features effectively. Increasing feature adoption boosts product engagement and user satisfaction, increasing customer lifetime value.
• Reducing churn: Keeping track of customers losing interest in your product can be a significant challenge. While it might be relatively easy to identify those at risk of churning based on their interactions with your business, developing and managing a win-back strategy—especially at scale—is much harder. Each customer may have a unique reason for leaving, requiring a tailored approach. This is precisely where AI excels. AI Decisioning determines the best actions to re-engage customers and reduce churn by analyzing past feature usage, user feedback, and incomplete tasks. The more customers you retain, the higher your customer lifetime value becomes.

Support

Your support team wants to minimize resolution times and prioritize customers at risk of churning or who are of high value to the business. AI can assist the support team by:

• Routing tickets: Managing a large volume of customer support tickets can make it challenging to identify which issues are most urgent, especially when vital information is scattered across multiple tools. Manually prioritizing tickets risks overlooking critical customers or urgent cases. AI Decisioning streamlines this process by evaluating the customer’s support level, churn score, issue type, and agent expertise. It then allocates tickets to the most suitable support member or team. By routing tickets to the right person and team, resolution times are reduced, and customer retention improves.

Finance

Every finance team focuses on managing expenses and optimizing budgets to ensure money is used efficiently and does not hinder progress. AI can assist the finance team by:

• Managing expenses: Tracking and analyzing expenses is a labor-intensive and error-prone task that involves accurately categorizing transactions, identifying policy violations, and detecting anomalies. When handled manually, these processes are susceptible to oversight, potentially leading to the approval of inappropriate expenses or incorrect allocations. AI Decisioning simplifies and streamlines this work by automating decisions, ensuring accuracy, and preventing costly mistakes. By leveraging AI for expense management, businesses can save time, reduce errors, and optimize costs.
• Optimizing budgets: Optimizing budgets involves extensive work, including analyzing historical data, forecasting trends, and accounting for market fluctuations and operational demands. When decisions are made incorrectly, the result can be inefficient allocations, underutilized resources, or missed opportunities for growth. AI Decisioning enhances budget optimization by analyzing past performance, seasonality, and operational priorities to make data-driven decisions that avoid suboptimal outcomes. The result is improved budget utilization, greater cost efficiency, and higher return on investment (ROI).

Real-world examples of AI decision making

Because of the spotlight on AI technology brought by OpenAI, it’s easy to assume that generative AI is the only type of AI available for businesses to leverage. This misconception can make AI decision making seem like a far-off fantasy. For years, major companies have used AI-driven decision making to power critical aspects of their operations.

Here are some examples:

• Netflix built a custom recommendation engine powered by AI to determine what you will most likely watch next. By analyzing your viewing habits, it suggests movies and shows that match your tastes, sometimes before you even realize what you want. This personalized approach has helped Netflix keep viewers engaged and loyal.
• Uber uses AI to determine which offers or updates you’ll care about most, and sends them when you’re most likely to take action. Whether it’s a discount on your next trip or a timely reminder to grab a ride to the airport, these intelligent push notifications help draw users back to the app and boost overall engagement.
• Starbucks uses AI to power many aspects of their business, from tailoring the promos and rewards you see in the app to customizing menu suggestions based on your go-to drinks. Behind the scenes, AI also helps Starbucks schedule staff at busy times and manage inventory so stores never run out of popular items. The result? Better customer experiences, higher revenue, and a more active loyalty program.

How to implement AI decision making

If you want to use AI for decision making in your business, the first step is establishing a strong data infrastructure. Ideally, all your data should be consolidated in a single location, such as a data warehouse. This creates a single source of truth, enabling AI to access all previous decisions to determine what works best for each customer.

However, the maturity of your data doesn’t determine whether AI decision making can work—it absolutely can. Less mature data simply means it may take longer for the AI to learn and optimize personalization for each customer.

From there, you have two options. One option is to build your solution using an AI agent. This approach is ideal if you require control over customization and integrations; however, it involves needing technical expertise, can be rather expensive, and is frankly hard to do.

The other option is to choose a platform that offers a ready-made solution for AI decision making. These solutions typically require minimal setup, are user-friendly, and are designed to address specific use cases. Examples include:

• Hightouch AI Decisioning leverages machine learning and your customer data to continuously experiment with producing the best outcomes for each of your customer, at a beyond human scale by understanding the best messaging, offer, channel, and timing for each customer.
• Unifygtm streamlines sales outreach by automating repetitive tasks, pinpointing ideal prospects, managing workflows autonomously, and delivering tailored messages to maximize impact.
• Cognigy enhances customer support with automated, lifelike interactions across chat and voice platforms. Its integration of generative AI enables empathetic conversations and ultra-realistic voice responses.

Closing Thoughts

AI Decisioning gives your business the edge to maximize performance. By harnessing customer data, you can make smarter decisions at superhuman scale and automate manual tasks—freeing you to focus on what truly matters.

Your competitors are already exploring AI Decisioning technology. Are you ready to let them gain the advantage? If you want to learn how to harness your data to make better decisions and drive more value, book a demo with one of our solutions engineers today.