The Best AI Decisioning Use Cases

AI Decisioning is a category of software that applies advanced machine learning—specifically reinforcement learning and AI agents—to automate previously manual decision-making on a massive scale.

From approving credit card applications to optimizing customer service call routing, AI Decisioning already powers critical operations across industries. But its impact is expanding rapidly, particularly in marketing, where it shifts the focus from operational efficiency to delivering deeply personalized customer experiences.

In marketing, AI Decisioning addresses one of the toughest challenges: scaling personalization. Marketers historically relied on broad user segments to deliver targeted experiences across channels such as email, websites, and mobile apps. While effective to some degree, this approach falls short as businesses grow, resulting in generic experiences that fail to resonate with individual customers.

With AI Decisioning, marketers can use AI to automate the personalization process. By setting clear goals—such as increasing repeat purchases or enhancing customer loyalty—and providing the necessary content and creative assets, AI Decisioning selects the optimal combination of message, timing, channel, and cadence for each customer. This ensures every interaction is tailored, creating stronger brand connections and driving more measurable results.

In this blog, we’ll explore the best use cases for AI Decisioning, showcasing how the technology transforms key business outcomes such as increasing customer lifetime value, boosting engagement, and optimizing retention. Whether you’re a marketer looking to improve campaign performance or a business leader exploring AI’s potential, these examples will hopefully inspire your first steps toward using AI to automate personalization at scale.

Maximizing cross-sell and upsell opportunities

Cross-selling and upselling campaigns are goldmines for marketing teams—they boost lifetime value, strengthen customer loyalty, and reduce the reliance on new customer acquisition. But let’s face it, figuring out the next best product to recommend to each customer is a challenge. Most marketing teams resort to broad segments, promoting the same products to large groups of people, even though we all know no two customers are exactly alike.

With AI Decisioning, cross-selling and upselling become smarter and more personalized. Instead of guessing what a customer might want, AI analyzes their past purchases, browsing habits, and what’s worked for similar customers. It continuously tests and learns to identify the product most likely to spark interest for each customer—and delivers it within the experience most likely to drive the user to take action.

Here’s the difference it makes:

Before AI Decisioning:

• A customer buys a product.
• The marketing team enrolls them in a workflow with static recommendations based on their first purchase.
• Everyone who bought Product A gets a promo for Product C, and everyone who bought Product B gets a promo for Product D—regardless of individual preferences.

With AI Decisioning:

• A customer buys a product.
• The marketing team sets a goal for a second purchase and connects their product catalog, messaging templates, and creative assets.
• The AI tailors personalized recommendations based on the customer’s unique profile and engagement history, sending them a message or offer that feels like it was made just for them.

The result? Customers feel understood, engage more, and are far more likely to make another purchase. It’s personalization at scale, and it’s a win for both your customers and your bottom line.

Enhancing customer renewal and retention

Renewing a subscription or retaining a customer is always cheaper—and smarter—than finding a new one. Long-term customers bring more value, spend more money, and help your business grow sustainably. But keeping customers engaged can feel like an uphill battle, especially when you’re relying on one-size-fits-all approaches like generic renewal emails or blanket discounts.

The reality is that every customer is different. Some may need a gentle nudge with a loyalty perk, while others might need reassurance about how your product meets their specific needs. This is where AI Decisioning shines. It helps you understand your customers on an individual level and delivers the right messages and offers to keep them coming back.

Here’s what that looks like in action:

Before AI Decisioning:

• A customer’s subscription is about to expire.
• The marketing team sends a generic renewal email to everyone, with a small discount to sweeten the deal.
• Many customers churn because the message doesn’t address their unique concerns or needs.

With AI Decisioning:

• A customer’s subscription is nearing expiration.
• The marketing team sets a goal to maximize renewals.
• The AI tailors a personalized message highlighting specific product features the customer loves or shares updates on new improvements relevant to their needs. For example, a power user might get a message about advanced features they haven’t tried yet, while a more casual user gets a simplified renewal offer.

The result? Renewals feel effortless and personal. Customers are more likely to stay because the outreach resonates with them and shows that you understand what they value most.

Reviving dormant users with win-back campaigns

Winning back inactive customers is one of the smartest moves a business can make. After all, these are people who already know your brand and have engaged with you before—you don’t need to start from scratch. But generic “We miss you!” emails or blanket discounts rarely work. They feel impersonal and often fail to address why the customer disengaged in the first place.

That’s where AI Decisioning can make a big difference. Instead of using the same experiences for everyone, it analyzes each customer’s past behavior, preferences, and interactions. Then it creates personalized campaigns that feel relevant and timely, giving dormant users the most impactful reason to re-engage.

Here’s how it works:

Before AI Decisioning:

• A customer hasn’t interacted with your brand in months.
• The marketing team sends out a standard “We miss you!” email to all inactive users.
• A small percentage of users return, but most ignore the outreach because it feels irrelevant or arrives at the wrong time.

With AI Decisioning:

• A customer stops engaging with your brand.
• The marketing team sets a goal to re-engage lapsed users.
• The AI identifies which users are most likely to return and crafts tailored messages for each. For example, a customer who used to buy frequently might get a personalized discount on their favorite product, while another who hasn’t engaged in a while might see a limited-time offer paired with a compelling reminder of their previous experiences.

The result? Customers feel valued and understood, making them more likely to return. With AI Decisioning, win-back campaigns go from generic to highly effective, helping you turn dormant users into loyal customers once again.

Boosting referrals through tailored incentives

Referrals are one of the most cost-effective ways to acquire new customers. There’s nothing quite like a personal recommendation to build trust and loyalty right from the start. However, finding the right incentives to motivate customers to refer others is very difficult. Too often, companies use a one-size-fits-all approach, resulting in generic messages and a reward system that doesn't resonate with individuals.

AI Decisioning takes a customer-centric approach to referrals, tailoring the messaging and incentive structure to suit each customer. Some may respond best to discounts, others to merchandise, or even just recognition. AI Decisioning determines the ideal approach for each customer, ensuring that referral campaigns are both motivating and effective.

Here's an example:

Before AI Decisioning:

• Marketing team launches a generic referral campaign.
• All customers receive the same referral offer: "Give $10, Get $10."
• Some customers participate, but many do not find the offer compelling.

With AI Decisioning:

• Marketing team sets a goal to maximize referral signups.
• Team uploads referral program parameters (rewards, creative assets, and messaging options) into the platform.
• AI Decisioning identifies which customers are most likely to refer and personalizes the offer for each:
• Loyal customers receive a message focused on recognition: “Share your love for our product!”
• Reward-driven customers are shown an exclusive bonus: “Earn up to $50 by referring friends!”
• Socially active customers are engaged with a gamified leaderboard.
• Referrals increase as customers receive tailored marketing campaigns designed to appeal to their specific motivations

The result? Customers are more excited and motivated to refer others because the incentives feel personal and meaningful to them. With AI Decisioning, referral campaigns become personalized and far more effective—helping you turn your customers into your biggest advocates.

Converting leads with personalized nurture journeys

Nurturing leads is essential for turning prospects into customers. Without a targeted, adaptive approach, your pipeline can stagnate, leaving valuable opportunities on the table. However, most lead nurturing strategies rely on generic, persona-based campaigns. Leads often receive irrelevant or poorly timed content that doesn’t resonate, resulting in low engagement and limited conversion rates.

AI Decisioning delivers the right message at the right time, through the right channel. By analyzing past interactions, current funnel stage, and channel preferences, it crafts a custom experience for each lead—maximizing relevance and impact.

Here's how it works:

Before AI Decisioning:

• A customer downloads a resource (e.g., eBook, demo request).
• The marketing team places the lead into a pre-set nurture workflow.
• Only a small percentage of leads engage or convert because the content doesn’t align with individual interests or timing.

With AI Decisioning:

• A customer downloads a resource (e.g., eBook, demo request).
• The marketing team sets conversion goals (e.g., scheduling a demo or completing a purchase).
• The AI dynamically tracks the lead's interactions, adjusting timing, channels, and content, and creates a personalized outreach based on their unique journey and preferences, such as product recommendations, time-sensitive offers, or helpful educational content.

The result? Customers receive tailored content that meets their unique needs, driving higher engagement and conversion rates. In turn, your marketing team benefits from increased efficiency, improved ROI, and a more predictable pipeline—ultimately contributing to sustainable business growth.

Driving loyalty and engagement for super users

Focusing on your most engaged, high-value customers—your “super users”—can dramatically boost long-term growth. By nurturing loyalty and incentivizing ongoing engagement, you increase customer lifetime value and reduce reliance on costly acquisition efforts. But most loyalty and engagement strategies use broad, one-size-fits-all campaigns. This lack of personalization can leave customers feeling undervalued and unappreciated, making it less likely they’ll remain loyal or refer new business.

AI Decisioning takes a customer-first approach, tailoring rewards, messaging, and offers based on each user’s preferences, behaviors, and history. It learns what each super user values most—be it exclusive experiences, special discounts, early product access, or recognition of their achievements—and delivers precisely that.

Before AI Decisioning:

• A customer joins a loyalty program.
• The marketing team assigns points for purchases and sends the same reward offers to all members.
• The customer receives generic “earn more points” messages, ultimately feeling like just another face in the crowd.

With AI Decisioning:

• A customer joins a loyalty program.
• The marketing team sets a goal to deepen engagement and retention among high-value customers.
• AI analyzes the user’s purchase history, preferred channels, and past redemption patterns to create personalized rewards and experiences—such as VIP events, tailored discounts on favorite items, or gamified challenges that align with their interests.

The result? Customers feel genuinely recognized and appreciated. They’re rewarded with relevant, meaningful perks that speak directly to their interests, making them more likely to stay engaged, continue spending, and spread the word. In turn, your brand enjoys greater customer loyalty, higher lifetime value, and a more sustainable path to growth.

Increasing conversions through on-site personalization

Optimizing your website’s experience can significantly boost conversions, revenue, and long-term customer satisfaction. On-site personalization ensures that each visitor encounters the most relevant content, offers, and products—dramatically increasing the likelihood they’ll complete a purchase or other desired action. However, many companies rely on static web pages or basic segmentation strategies, resulting in generic and often irrelevant content. This leads to frustrated users, missed revenue opportunities, and lower overall conversion rates.

AI Decisioning curates the on-site experience based on each visitor’s unique behavior, purchase history, and browsing patterns. It learns in real-time, adjusting what’s displayed so every visitor sees content that aligns with their interests and intent, leading to a more engaging and successful user journey.

Before AI Decisioning:

• A new customer lands on your website.
• The site displays default product categories and static content based on broad industry insights.
• Returning visitors see only slightly adjusted recommendations rooted in broad segmentation, not individual preferences.
• The customer struggles to find what they need, resulting in low conversion rates and abandoned sessions.

With AI Decisioning:

• A new customer lands on your website.
• The marketing team sets the goal of increasing conversion rates.
• AI analyzes the visitor’s behavior, combining past browsing history, similar customer data, and intent signals, and dynamically adjusts to show relevant product recommendations, personalized promotions, and targeted content—right from the start.

The result? Customers find what they’re looking for quickly and effortlessly, enjoying a truly personalized shopping journey. This relevance-driven approach boosts engagement, conversion rates, and overall customer satisfaction.

Industry Use Case Examples

Below are examples from various industries showing how AI Decisioning outperforms traditional methods. Each case demonstrates how personalized, data-driven tactics increase engagement, boost conversions, and improve overall business performance.

Closing thoughts

The overall theme in this article is that generic personalization will only produce generic results. It can be frustrating to know you have all the customer data needed to create the best experiences yet struggle to translate it into increased revenue. The real challenge comes when your customer base grows to a scale where manual personalization is no longer feasible without substantial resources.

Fortunately, AI Decisioning allows your business to break free from generic marketing strategies, delivering hyper-personalized experiences that truly resonate with each customer. By automating complex decisions and continuously learning from customer behavior, AI Decisioning ensures you're always optimizing for the best outcome.

Interested in seeing the power of AI Decisioning firsthand? Book a demo with one of our solutions engineers today.