Rules-based decisioning engines (also known as enterprise decisioning applications) have long been a mainstay for marketers. These applications automate marketing actions using predefined business rules.
When the decision is executed as part of a marketing campaign, specific conditions defined in the rules will trigger actions without requiring manual segmentation or IT intervention.
There’s a big upside to traditional decisioning models
Many decision engines also allow machine learning model scores to be incorporated as static variables after the model is trained on customer data, further improving decision outcomes.
As a result, personalized responses and campaigns can be executed in batch or real time. The benefits are twofold: personalized messaging that meets specific marketing objectives plus scalable automation of complex, multichannel customer journeys for large audiences. And all of this is accomplished without manual labor or spreadsheets.
There’s also a real downside to traditional models
There is no question that decisioning yields benefits. A recent study of personalization trends highlighted that personally relevant recommendations influence 73% of consumers to purchase.
However, that same study also illustrated the downside of generic or irrelevant messages, finding that 80% are likely to ignore companies that push them.
AI can significantly improve relevance. But in traditional decisioning engines that use static AI-generated model scores, great results can quickly degrade into irrelevance.
For example, let’s show what can happen when using static model scores. Say a marketer wants to judge a customer’s likelihood to purchase a certain product (a propensity use case) and develop AI models to do that.
In traditional decisioning engines, marketers score customers using an at-rest snapshot of data captured when the model runs. They use those scores to drive decisions, often with strong initial results. However, if they continue using the same model throughout a campaign or customer journey, performance will likely decline over time. Customer behavior changes, and the model must adapt through retraining.
Since model scores are static, decisions that were once relevant can quickly become irrelevant – causing negative effects on both conversions and customer satisfaction.
Given the significant upside of decisioning models, how can marketers gain substantial benefits while minimizing the downsides?
Enter decision intelligence.
Decision intelligence changes the game – in a good way
AI is changing the game when it comes to decisioning. It’s both raising the stakes and skyrocketing the results.
Decision intelligence is the real-time execution of AI models seamlessly combined with decisioning systems – extending the capabilities of both. It transitions decisioning from manual rules and static model scores to a system that automatically figures out the right message, offer, channel and moment for each individual customer. Plus, it gets smarter with every decision it makes.
Traditionally, marketers build segments (customers who bought X get email Y) and set rules that apply to thousands of people the same way. Decision intelligence flips that model. Instead of marketers deciding what happens to groups of customers, the system evaluates every individual customer in real time – behaviors, history, context and predicted intent – then makes a unique decision for each one.
In the 2026 State of Martech Report, Jonathan Moran, Senior Marketing Manager at SAS, describes how decision intelligence works in real-life scenarios. According to Moran, “Real orchestration requires a decisioning layer that can evaluate, for any given customer at any given moment: What’s this person eligible for? What’s the best action, offer or experience across all competing actions? What channel and timing maximize the likelihood of the right outcome?” He adds, “ This is where logic, business rules, suppression rules, fatigue controls and AI-driven propensity models all have to coexist.”
This is decision intelligence to a T.
What are the benefits of decision intelligence?
Decision intelligence brings some significant advantages to marketers, including:
Truly individualized personalization. Static campaigns treat customer segments the same way. AI-powered decisioning changes that fundamentally. Decision intelligence gives brands a way to use their customer data to make one-to-one decisions for each customer across messages, channels, creative, product offer, incentive, time, day and frequency.
Real-time responses to customer behavior. Timing is everything in marketing. Real-time decisioning enables platforms to respond instantly to customer behaviors, delivering the right offer or action at the exact moment it matters. This drives engagement, improves loyalty and enhances measurable business outcomes. The ideal platform will process real-time, data-in-motion customer signals and apply AI models, business rules and scoring logic to determine the next-best action on the fly.
Continuous learning and optimization. Unlike rules-based systems that stay static until someone manually updates them, AI learns as it goes. Platforms that use AI-powered decisioning optimize ROI through analytical targeting and continuous learning that automatically improve recommendation performance over time.
Movement from manual testing to automated experimentation. Marketers historically relied on A/B testing, which can be slow, limited and often inconclusive. Decision intelligence replaces static rules and manual testing with a system that learns and adapts automatically. This means teams can run far more experiments at scale without the operational overhead.
Insight to action connected in one platform. Historically, insights lived in analytics tools and actions lived in execution tools with a painful gap in between. Decision intelligence connects data, decisioning and activation across the customer lifecycle. Marketers can move from insight to action in one platform – executing faster, adapting in real time and measuring impact with confidence.
SAS stands out because it gives marketers flexibility without adding complexity. Its marketing AI, decisioning and journey orchestration capabilities are built to work together, so teams can seamlessly connect customer experiences across channels and data environments.Doug Mbaya, Senior Partner Solutions Architect, AWS
Analyst perspective on decision intelligence
Forrester analyst Zeid Khater strongly endorses AI decisioning in The Forrester Wave™: Customer Analytics Technologies, Q2, 2026, where he positions analytics technologies closer to customer interaction points and recommends driving actions to the point of decision.
Khater has a recommendation for marketers considering purchasing or upgrading their analytics technology:
“Buyers should prioritize vendors that pair analytics with native decisioning and next-best-action capabilities, make updates to profiles and segments as new signals arrive, and extend decisions into downstream systems where interactions occur. Seek vendors that can demonstrate speed from insight to executable decisions, evaluate which learning paradigms underpin decisioning (rules, bandits or reinforcement learning models), and whether orchestration and activation are first‑class capabilities or separately licensed add‑ons that could potentially slow time to value.”
What’s the bottom line? Khater makes a strong case for the superior benefits of decision intelligence over static decisioning in this research.
The decision(ing) is clear
What should marketers do when it comes to choosing static or AI-powered decisioning? The direction is clear and the business case for decision intelligence is straightforward when you think of it this way: More relevant experiences drive higher engagement, lower churn and deliver better ROI. And all of this can be accomplished without proportionally increasing your marketing team's size.
