AI has become impossible to ignore in modern marketing, with expectations ranging from effortless personalization to fully automated execution. But for many teams, those expectations haven’t translated into consistent, reliable performance. The challenge isn’t access to AI—it’s knowing when and how to trust it to make the right decisions.
This is where AI decision intelligence comes in. Rather than focusing solely on automation, decision intelligence applies AI to guide and execute marketing decisions at scale. But, as G2’s 2026 AI Decision Intelligence in Marketing report makes clear, success depends on thoughtful system design, reliable data, and alignment with how marketers actually operate.
Iterable was included in this independent G2 research as one of the platforms shaping how AI decision intelligence is being built and used today. Below, we break down what the report reveals about where the industry stands—and how lifecycle teams can translate these insights into action.
AI Decision Intelligence Adoption Is Accelerating Across Marketing Teams
G2’s research shows that AI decision intelligence has moved beyond isolated testing and into day-to-day marketing execution, with maturity shaped largely by data readiness and how deeply decisioning is embedded into workflows.
According to the report, between 51% and 75% of customers on more advanced platforms, such as Iterable, actively use AI decisioning features today, signaling a clear shift from experimentation to operational use. Rather than applying AI in narrow scenarios, mature teams are using it across multiple decision layers, including:
- Audience selection
- Channel routing
- Send-time optimization
- Journey progression
- Automated experimentation
This represents a meaningful change from earlier approaches, where AI was often limited to lead scoring, churn prediction, or content recommendations. Today, decision intelligence is woven directly into how campaigns and journeys are planned, executed, and optimized.
The Real Business Results of AI Decision Intelligence in Marketing
G2’s research shows that AI decision intelligence has reached a tipping point at the enterprise level. Nearly 60% of enterprises now have AI agents in production, and the report predicts that aggressive adopters of AI-powered automation will reduce marketing operational costs by 30%. These signals point to more than experimentation—they reflect widespread operational adoption with tangible business impact in day-to-day marketing execution.
Across the platforms surveyed, G2 identified consistent areas where AI decision intelligence is delivering measurable value:
- Faster campaign execution
- Higher conversion rates
- Improved retention
- More efficient use of budget
- More accurate targeting
- Reduced time to value
As decision systems learn and adapt over time, their impact extends beyond individual campaigns. Brands now expect decision intelligence to support adaptive journeys that consistently improve conversion, retention, and time to value.
What Prevents AI Decision Intelligence From Delivering Full Value
Despite its promise, G2’s research makes it clear that even advanced AI systems can fail when foundational elements aren’t in place.
- Data quality remains the biggest barrier: All participating platforms pointed to data readiness as the primary obstacle. AI decision intelligence depends on clean, unified, and timely data. Without it, decisions may stall, misfire, or fail to earn trust.
- Trust and explainability influence adoption: Teams are hesitant to rely on AI-driven decisions if they don’t understand why a system recommended a particular action. Lack of transparency slows adoption and limits impact.
- Skills and alignment matter: Even with capable technology, teams may struggle if they lack clarity on goals, confidence in outputs, or the skills to design effective decision strategies. G2’s findings consistently point to operational readiness—not model sophistication—as the limiting factor.
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How prepared is your team to operationalize AI? This Checklist for Unlocking the Power of AI breaks down how to embed agentic intelligence across every layer of marketing. |
The Future of AI Decision Intelligence in Marketing
Across vendor responses, G2 identified a clear directional shift: from AI-assisted decisions to autonomous orchestration.
Rather than simply recommending actions, decision intelligence is evolving toward systems that can continuously evaluate options, select optimal paths, and execute across targeting, timing, channel selection, and experimentation.
Key changes highlighted in the report include:
- A move from static recommendations to autonomous action
- Continuous experimentation replacing discrete A/B tests
- Real-time recalibration instead of scheduled optimization cycles
- Decision intelligence extending closer to in-product experiences
Together, these shifts point to a future where marketers focus less on manual orchestration and more on setting strategy, goals, and guardrails for intelligent systems that adapt continuously.
Putting AI Decision Intelligence Into Action With Iterable
G2’s report shows that as AI decision intelligence becomes more autonomous, teams are focusing investment on three core areas: real-time data, adaptive decision engines, and marketer trust. Iterable is designed to support each of these foundations directly within lifecycle workflows through:
- Real-time data that powers timely decisions: As decisioning shifts toward continuous execution, data freshness becomes critical. Iterable helps teams act on real-time customer signals by unifying behavioral and event data within journeys, so decisions—such as when to engage or how to progress a customer—are informed by current context rather than delayed insights. This allows AI-driven decisions to adjust as customer behavior changes, instead of relying solely on scheduled updates or static assumptions.
- Predictive and adaptive decisioning within journeys: G2 highlights growing investment in predictive and autonomous decision engines that can learn and adjust over time. Iterable’s predictive modeling, embedded experimentation, and adaptive journey logic help inform who to engage, when to reach out, and how journeys evolve as customers interact across channels. As a result, teams can move away from one-off testing and toward continuous learning without the need for constant manual reconfiguration.
- Enablement, transparency, and marketer control: As autonomy increases, trust becomes essential. G2 emphasizes that teams adopt AI decisioning more confidently when systems are understandable and collaborative. Iterable’s explainable AI is designed to keep marketers in control by providing visibility into decision logic, performance signals, and journey behavior. This focus on enablement helps teams build confidence in AI-driven decisions and scale decision intelligence responsibly over time.
Key Takeaways From G2’s 2026 AI Decision Intelligence Report
G2’s 2026 AI Decision Intelligence in Marketing report highlights a defining moment for the industry. Decision intelligence is no longer an abstract concept—it’s becoming the backbone of modern marketing operations.
For teams ready to move from experimentation to execution, the next phase involves embedding intelligence where decisions are made and designing journeys that can learn, adapt, and improve over time.
