AI Decision Intelligence in Marketing: G2’s 2026 Industry Report

It’s not just automation; it’s orchestration. Marketers are increasingly relying on AI to reduce guesswork and guide their next move.

Vendors like MoEngage, Blueshift, and Iterable describe customers using AI to automate increasingly complex decision flows, from adaptive cross-channel journeys to real-time optimization. Customer.io also points to growing interest in AI-driven performance interpretation, where decision intelligence helps surface insights and recommendations that might otherwise be overlooked.

The direction is clear: teams are shifting from manually coordinating campaigns to supervising systems that make and justify intelligent decisions in real time.

 “We’re seeing AI decisioning deliver transformative outcomes. Global brands expect adaptive journeys that reason in order to consistently drive higher conversions, increase retention, and accelerate time to value.”

How mature are brands in AI decision intelligence in marketing?

One of the most revealing findings is how differently brands approach AI maturity depending on the platform they use and the data foundations they’ve built.

MoEngage, Blueshift, Bloomreach, and Iterable describe operating at an advanced stage, supported by years of investment in predictive modeling, autonomous decision engines, and real-time optimization frameworks. Their customers are applying AI across multiple decision layers, from audience selection and channel choice to experimentation and journey orchestration; embedding decision intelligence directly into execution workflows.

Customer.io reflects a phased adoption model that mirrors how many organizations evolve. Their users are beginning with predictive signals and lightweight automation, then expanding into more connected decision workflows as data readiness and internal confidence grow. Rather than a single maturity curve, this highlights how decision intelligence adoption progresses incrementally, shaped by organizational context and priorities.

How many customers are using AI decisioning in marketing today?

When vendors were asked what percentage of their customers actively use AI decisioning features, the responses revealed an encouraging trend: adoption is solid and rising quickly.

• MoEngage and Customer.io estimated adoption between 26% and 50%, reflecting the early to mid-stage journey many of their customers are on. 
• Blueshift, Bloomreach, and Iterable reported significantly higher adoption, between 51% and 75%, highlighting stronger readiness and deeper reliance on decision intelligence.

What results AI decisioning actually delivers in marketing

Across vendor responses, measurable impact surfaced most clearly in execution speed, performance lift, and efficiency gains.

Across the five participating vendors, the following performance outcomes were consistently validated as the areas where AI decisioning drives the most noticeable improvements:

• Campaigns go live faster
• Conversions increase
• Retention improves
• Budgets stretch further
• Targeting becomes more accurate
• Time-to-value decreases

MoEngage, Bloomreach, Blueshift, and Iterable all pointed to meaningful gains in execution speed. By letting AI handle decisions that once consumed hours of manual setup, marketers reclaim time for strategy and experimentation.

Conversion improvements were also common. When audience selection, send-time optimization, and channel choice shift from guesswork to machine-driven precision, performance naturally follows. Blueshift emphasized this specifically in the context of real-time decision loops that eliminate delays between insight and action.

Retention gains emerged from predictive identification of at-risk users and automated re-engagement flows, areas where MoEngage, Iterable, and Bloomreach have seen strong impact.

Once decisions become automated and self-optimizing, performance improvements begin reinforcing each other across campaigns, channels, and journeys. That compounding effect ultimately shows up as revenue growth, but only when teams move beyond productivity gains and start automating with goal-based AI agents, a point MoEngage emphasized strongly.

 “Marketers don’t need AI that acts for them — they need AI that thinks with them. Decision intelligence closes the gap between ‘here’s your data’ and ‘here’s what it means,’ giving teams the insight to move faster without losing control of the strategy.”

Why decision intelligence in marketing sometimes fails

This section examines where decision intelligence breaks down in real-world marketing operations

For all the upside AI decisioning delivers, every vendor also agreed on a hard truth, even the most sophisticated AI systems falter when foundational elements aren’t in place. The vendors were unanimous on this point: data quality is the single greatest barrier to effective decision intelligence.

AI-driven systems require clean, unified, and timely data. Without it, decisions either stall or misfire. Blueshift emphasized this challenge directly, pointing to the need for richer datasets and deeper integrations. 

Iterable mentioned that skill gaps within customer teams often restrict adoption. Even when the tech is in place, teams may not know how to design decision strategies, interpret outputs, or integrate AI into their processes. Bloomreach highlighted that even advanced systems struggle when organizations lack internal alignment or clarity around goals for what decision intelligence should achieve. In other words, you need both the capability and the clarity to make AI decisioning work.

Customer.io raised another critical point: explainability. Teams hesitate to adopt AI-driven decisions when they can’t understand why the system recommended a particular action. That gap erodes trust and slows down adoption.

Vendor responses consistently pointed to operational readiness as the limiting factor, not model capability. Without robust processes, clear strategies, and educated teams, even the most powerful decision engine cannot deliver transformative value.

Where will teams invest marketing AI decision-making in 2026?

If data, skills, and trust are where decision intelligence in marketing breaks, they’re also where teams are now focusing their energy. From vendor responses, it’s clear that companies are preparing for deeper, more integrated AI decisioning by strengthening three core areas.

Real-time data infrastructure

Across responses, platforms emphasized the need for faster, more reliable data pipelines. Teams are investing in systems that surface customer signals instantly, so AI-driven decisions are grounded in the freshest possible context. As decisioning moves closer to real time, outdated or delayed data quickly becomes a bottleneck.

Predictive and autonomous decision engines

Vendors consistently pointed to advances in predictive modeling and autonomous execution as a major investment priority. Blueshift and Bloomreach highlighted the importance of systems that learn continuously and adjust decision logic in real time. MoEngage and Iterable echoed this direction, emphasizing goal-based agents and adaptive workflows that reduce the need for constant manual reconfiguration as campaigns scale.

Customer enablement and trust-building

Several platforms underscored that technology alone isn’t enough. For decision intelligence to succeed, teams need training, clearer ROI frameworks, and systems that feel collaborative rather than opaque. Investment is increasingly flowing into enablement, helping marketers understand, trust, and confidently guide AI-driven decisions instead of working around them.

What I found especially telling is that vendors consistently described investment priorities that span data, people, and process. The evolution of decision intelligence is as much about people, skill development, and trust as it is about models and algorithms.

Where is decision intelligence in marketing heading next?

Across every vendor response, one message was clear: 2026 marks the shift from AI-assisted decision-making to autonomous execution.

MoEngage’s goal-based agents, Bloomreach’s real-time memory framework, Blueshift’s self-refining intelligence, Iterable’s adaptive journeys, and Customer.io’s expanding decision layers all point toward the same future; AI systems that don’t just inform decisions, but actively carry them out.

As autonomy increases, four changes will define the next phase of decision intelligence.

From recommendations to autonomous action

Decision intelligence is moving beyond surfacing insights or “next best actions.” Autonomous systems will continuously evaluate options, select the optimal path, and execute decisions across targeting, timing, channel selection, and creative delivery. Bloomreach envisions this extending to fully autonomous campaign execution, where AI generates content, chooses distribution paths, and optimizes outcomes without manual orchestration.

From static testing to continuous experimentation

Experimentation will no longer be a discrete workflow. Instead of planning individual A/B tests, teams will rely on AI systems that generate hypotheses, allocate traffic dynamically, measure outcomes, and roll forward winning variations automatically. Iterable and MoEngage both point toward experimentation becoming an always-on capability embedded directly into decision engines.

From scheduled optimization to real-time recalibration

As decision systems ingest live behavioral signals, optimization will happen moment by moment rather than in fixed cycles. Blueshift describes this as the continuous transformation of unified customer data into high-impact decisions, where every interaction refines the next one in real time.

From external automation to in-product intelligence

Decision intelligence is also moving closer to the product experience itself. Customer.io highlights the growing role of in-product guidance, where AI supports users directly within the application, adapting onboarding, feature discovery, and engagement based on live usage patterns.

Taken together, these shifts signal a fundamental change in how marketing teams operate. As decision intelligence becomes autonomous, marketers move away from configuring workflows and toward directing strategy, setting goals, defining guardrails, and overseeing intelligent systems that learn, act, and optimize continuously.

 “The future of AI decisioning is autonomous, where marketers and lifecycle experts act less like important cogs in the campaign machine and more like air traffic controllers who oversee AI agents executing and optimizing campaigns.”

What this means for leaders in 2026 and beyond

One theme stands out above all: AI decision intelligence is becoming the foundation of how modern marketing teams operate.

Leaders who want to stay ahead should begin laying the groundwork now.

This starts with improving data readiness, unifying sources, cleaning structures, and ensuring signals flow where they need to go. It also means building internal AI literacy so teams understand not just how to use decision intelligence, but how to trust it.

Most importantly, leaders must rethink how marketing gets done. Instead of manually orchestrating campaigns, teams will increasingly design systems that think, adapt, and optimize autonomously.

 “Over the past decade, marketers have increasingly adopted AI, with Generative AI driving significant productivity gains in the recent past. However, this increased productivity does not guarantee higher revenue. To achieve real growth, brands must implement AI Decisioning.
 By utilizing goal-based AI Agents, companies can finally automate the massive number of micro-decisions needed to personalize every customer interaction”