A bold new wave of AI-powered capabilities is reshaping how brands cut through the noise and engage customers across channels. At Iterable’s annual Activate Summit, the company unveiled a slate of AI-driven features designed to help marketers move beyond generic messages and craft more purposeful, efficient, and measurable journeys. The announcements emphasize turning a flood of data into timely actions, enabling marketers to be more agile, strategic, and ROI-focused as they leverage generative AI insights. This expansion comes amid growing scrutiny of AI’s scalability and cost, with brands seeking practical, enterprise-grade solutions that deliver tangible throughput and sustained value. The event also highlighted the broader market opportunity for AI-powered customer engagement platforms, situating Iterable’s innovations within a fast-evolving landscape where marketers increasingly expect AI to drive outcomes rather than simply automate tasks.
AI-powered capabilities unveiled at Activate Summit
Journey Assist: accelerating customer journeys with AI
Customer journeys sit at the heart of modern marketing, mapping how individuals interact with a brand from awareness through advocacy. In this section, Iterable introduces Journey Assist, a groundbreaking feature designed to dramatically simplify the creation and enhancement of customer journeys using AI. Rather than manually drafting every step of a journey, marketers can initiate a prompt that instructs the system to generate a complete journey flow, drawing on commonly used templates and best practices. The platform then surfaces ready-to-use templates tailored to the specified use case, reducing planning time from hours to minutes and enabling rapid experimentation with new paths.
The concept hinges on translating complex journey logic into intuitive, linear sequences. The marketing team can describe the desired outcome in plain language or use technical specifications, and Journey Assist will translate that input into a structured flow that aligns with stages such as awareness, consideration, purchase, and advocacy. This approach mirrors the way a flow chart visualizes processes but replaces manual drafting with AI-assisted drafting that can be refined iteratively. In practical terms, a brand might outline a scenario where a user signs up for a Priceline deal, which would trigger a welcome message and a multi-step engagement plan. A typical enterprise setup could entail hundreds of potential steps; Journey Assist compresses this complexity, offering an initial, coherent path that marketers can tweak as needed.
The value proposition goes beyond speed. Journey Assist addresses the common challenge of managing complexity in customer journeys, where even well-designed paths can become unwieldy as new data points, channels, and messages accrue. By generating a baseline journey rapidly, marketers gain a solid foundation from which to optimize, test, and scale. The first month of the customer experience is particularly critical, and Journey Assist aims to ensure that this early window is optimized for conversion and long-term engagement. In short, Journey Assist makes it feasible to design sophisticated journeys in a fraction of the time, enabling teams to try more variations, learn faster, and iterate toward higher performance.
From the perspective of users, Journey Assist offers a flexible toolset that accommodates a wide range of use cases, from onboarding sequences to post-purchase nurture and re-engagement campaigns. It supports both simple and technical language descriptions, so teams with different levels of data literacy can participate in journey design. The AI will automatically generate the journey flow based on the described parameters, presenting a coherent sequence that can be deployed across channels. This capability is particularly valuable for marketing organizations that must coordinate cross-channel communications while maintaining a consistent voice and cadence.
In practice, Journey Assist can empower marketers to articulate their goals, translate them into concrete actions, and see an end-to-end flow emerge in minutes rather than days. This not only accelerates the time-to-value for new campaigns but also reduces the cognitive load on teams, freeing up time for strategic thinking and creative experimentation. The result is a more agile marketing operation that can respond to shifting customer behavior and market conditions with greater speed and precision.
Smart Segmentation: building richer audiences with AI augmentation
Segmentation remains a cornerstone of effective marketing, yet the underlying data landscape is often fragmented, making it difficult to assemble precise, actionable audience segments. Iterable’s Smart Segmentation addresses this pain point by enabling marketers to construct richer segments with a broader set of attributes and event signals, all while maintaining visibility into how data is used and where it originates. The feature provides contextual information about data usage and behavior, along with AI-driven recommendations that guide segment construction.
The impetus for Smart Segmentation is straightforward: marketers are inundated with data points but still struggle to turn that data into timely, relevant audience segments. By automating the synthesis of disparate signals—behavioral events, demographic attributes, engagement history, and contextual cues—the platform accelerates the path from raw data to usable audiences. The AI suggests segmentation criteria and prioritizes attributes that are most likely to correlate with desired outcomes, helping marketers avoid dead-end segments and focus on high-potential targets.
Industry professionals who have used Smart Segmentation report that the feature enables faster decision-making and more precise targeting. The system delivers a clear map of data usage and how signals contribute to segment definitions, which helps teams understand the rationale behind audience choices. Moreover, the platform offers smart recommendations that surface potential new audience angles based on observed patterns, enabling marketers to explore opportunities they might not have considered otherwise.
In real-world terms, Smart Segmentation translates into more efficient workflows and better alignment between data science and marketing teams. For example, a food delivery platform could use AI-generated segments to identify users most likely to respond to promotional offers, based on a combination of past orders, order frequency, time-of-day activity, and engagement with prior campaigns. Marketers can then tailor creative, messaging, and timing to maximize conversion rates and lifetime value. The aim is not just to slice audiences more finely, but to do so in a way that is grounded in data-driven insights and delivered at speed.
Customer voices highlight the practical impact of Smart Segmentation. Selen Kucukarslan, a senior CRM and marketing automation manager at a leading delivery service, notes that prior approaches often required data scientists to implement audience models. With Iterable’s Smart Segmentation, the organization can craft intelligent audience segments in a scalable manner, centering efforts on users most likely to achieve key business goals. Redbubble’s CRM leadership echoes this sentiment, emphasizing that the capability frees up time and mental bandwidth to focus on high-impact ideas rather than routine data wrangling. In such cases, the ability to quickly assemble precise segments translates to more efficient campaigns, improved targeting accuracy, and a better chance of achieving specific outcomes such as increased engagement or higher conversion rates.
The practical takeaway is that Smart Segmentation reduces the friction between data complexity and operational marketing tasks. Marketers gain faster access to actionable audience insights while still benefiting from the depth and breadth of data available within their ecosystems. This capability aligns with broader industry trends toward data-driven marketing that combines intensively analyzed signals with real-time activation, enabling campaigns to adapt to evolving user behaviors and preferences.
Brand Affinity and WhatsApp integration: deepening cross-channel insights and reach
Brand Affinity represents another pillar of Iterable’s AI-enhanced platform, focusing on how brands gauge and interpret customer sentiment across channels. The updated Brand Affinity tool translates cross-channel engagement into user labels and provides historical trend analysis supported by explainable AI. Marketers can now view scoring and insights at the campaign-aggregate level, gaining visibility into how customer affinity evolves over time as a result of interactions—content choices, messaging strategies, and audience mix. In practice, brands often juggle hundreds of concurrent campaigns; this feature helps them maintain a coherent view of overall sentiment and momentum across the portfolio of efforts. By centering data analysis on sentiment over time, Brand Affinity supports more informed decision-making and a clearer sense of how creative, channel, and message combinations influence customer perceptions.
Stepanova highlights the practical reality that brands run dozens or even hundreds of campaigns simultaneously. In such scenarios, obtaining a consolidated view of customer sentiment can be challenging, and disparate campaigns may yield conflicting signals. Brand Affinity is designed to synthesize cross-campaign data into a unified perspective, enabling marketers to understand whether customers feel positively, neutrally, or negatively about a brand as a whole and how that sentiment shifts in response to campaigns, content, and audience segmentation. The approach emphasizes data and context as core differentiators of AI-powered marketing, reinforcing the idea that AI’s value emerges when it translates raw data into meaningful, actionable insights.
In a further expansion of cross-channel capabilities, Iterable announced the integration of WhatsApp, a platform owned by Meta and the world’s most widely used messaging app with more than two billion monthly active users. This integration empowers Iterable users to send personalized messages aligned with customer preferences through one of the most universal and widely adopted channels in the world. The addition of WhatsApp facilitates interactive communication with customers through quick-reply messaging and the automation of campaigns across the entire customer lifecycle. The potential impact for marketers is substantial: global reach on a platform with vast engagement potential, combined with AI-driven personalization that tailors interactions to individual customer profiles.
The broader takeaway is that WhatsApp integration serves as a powerful extension of cross-channel marketing strategy, enabling brands to meet customers where they are with timely, relevant, and interactive messages. This capabilities set enhances customer experience, supports better lifecycle management, and provides marketers with a streamlined way to orchestrate communications across a platform that customers frequently prefer for real-time, conversational interactions. The overarching theme across Brand Affinity and WhatsApp is the pursuit of deeper consumer understanding, more nuanced sentiment measurement, and more effective activation across channels through AI-augmented insights.
Industry-wide context: aligning marketing AI with real-world practices
The Activate Summit announcements come at a time when marketers are intensely focused on practical AI adoption—balancing ambition with operational realities like cost, latency, and ROI. The emphasis on Journey Assist, Smart Segmentation, Brand Affinity, and WhatsApp integration reflects a deliberate strategy to translate AI capabilities into tangible improvements in speed, relevance, and scale. By enabling the rapid creation of journeys, the rapid assembly of robust audience segments, and the ability to measure and act on sentiment across campaigns and channels, Iterable positions itself as a platform that not only automates tasks but also informs strategic decisions with data-driven insights.
The overarching narrative is that AI is not merely a tool for incremental optimization but a catalyst for rethinking how marketing operations are designed and executed. The emphasis on enabling marketers to be more agile and strategic aligns with the broader industry trend of leveraging AI to augment human expertise rather than replace it. The summit’s framing suggests a path toward more efficient, evidence-based marketing that can respond quickly to changing customer behavior, market dynamics, and competitive pressures.
Market context: the growth of AI-driven customer engagement platforms
The AI-powered customer engagement sector has been expanding rapidly as brands seek scalable ways to connect with customers across channels. Market research firms project substantial growth in this space, underscoring the potential for AI-enabled engagement to reshape marketing outcomes. Global customer engagement solutions are anticipated to reach tens of billions of dollars in value in the coming years, driven by increasing demand for personalized, cross-channel experiences and the need to manage increasingly complex data ecosystems. Analysts highlight a compound annual growth rate in the double-digit range as brands invest in platforms capable of handling real-time data, predictive insights, and automated orchestration across channels.
Within this broader market, notable players compete to offer end-to-end solutions that cover data collection, audience segmentation, campaign orchestration, cross-channel messaging, analytics, and optimization. The competitive landscape includes enterprise-grade marketing automation and customer relationship management (CRM) platforms, as well as specialized vendors focusing on specific channels such as email, social, mobile messaging, and web experiences. The convergence of marketing data management, AI-driven analytics, and automation capabilities is accelerating as brands seek to unify data governance with real-time activation.
Iterable, founded in 2013, has achieved notable growth milestones, including reaching significant annual recurring revenue (ARR). The company counts an impressive customer roster that includes Priceline, DoorDash, Box, Redfin, Calm, Zillow, and Volvo. This client mix illustrates the platform’s relevance across industries, from travel and e-commerce to real estate and consumer goods, and highlights how AI-enabled engagement tools can be applied to diverse business models and customer journeys. The market data and competitive dynamics suggest that the AI-powered engagement category will continue to evolve as vendors add more advanced capabilities to address evolving marketing needs, including privacy-compliant data usage, explainable AI, and scalable orchestration across global operations.
In parallel, research reports on omnichannel commerce platforms project strong growth, underscoring the importance of a cohesive, cross-channel strategy for retailers and service providers. These platforms aim to unify customer experiences across physical and digital touchpoints, leveraging AI to optimize paths to purchase, customer retention, and lifetime value. The confluence of AI, data-driven decision making, and cross-channel orchestration is expected to drive meaningful improvements in customer satisfaction, revenue, and brand loyalty. The overall market trend supports the strategic direction of Iterable’s product announcements, which seek to bring advanced AI capabilities to marketers in a manner that is both scalable and easy to adopt.
How brands are embracing AI today: adoption, sentiment, and ROI considerations
A recent global survey of marketers reveals a strong embrace of AI across marketing teams, with a majority already integrating AI into their workflows. The survey indicates that a large share of respondents—well beyond a minority—believe that generative AI will simplify their jobs and enhance efficiency. This sentiment reflects a readiness to adopt AI-powered capabilities that can automate routine tasks, accelerate decision-making, and unlock new levels of creativity and strategic thinking. The data also shows that marketers are increasingly considering how to balance the benefits of AI with potential risks and costs, recognizing the importance of governance, data quality, and responsible AI usage in achieving long-term ROI.
Analysts emphasize that the pace of AI adoption in marketing is accelerating, driven by the demand for more personalized experiences and the need to manage vast data sets across multiple channels. The industry is witnessing a shift from experimentation to scale, with organizations investing in platforms that offer end-to-end capabilities—from data ingestion and audience modeling to multi-channel orchestration and measurement. In this environment, features like Journey Assist, Smart Segmentation, Brand Affinity, and channel integrations become critical, as they enable marketers to operationalize AI in ways that deliver measurable results.
From the perspective of practitioners, the move toward AI-enhanced marketing requires careful planning around integration with existing systems, data governance, and the establishment of clear success metrics. Teams are looking for AI tools that not only generate outputs but also provide explainable reasoning and actionable guidance. The emphasis on transparency and control aligns with broader industry expectations for AI to augment human decision-making rather than replace it. The narrative emerging from industry observers is that AI will continue to reshape marketing practices, with platforms that offer robust automation, data-driven insights, and cross-channel orchestration at the forefront of the next wave of growth.
Real-world implementation and customer perspectives
The journey from concept to value with AI-powered marketing tools is grounded in concrete use cases and measurable outcomes. Clients report faster time-to-value when adopting AI-enabled journey design, as teams can prototype, test, and optimize sequences more rapidly. The ability to generate journey flows from simple prompts reduces the cognitive load associated with planning complex campaigns, enabling marketers to experiment with different messaging cadences, channel sequences, and timing windows. This speeds up the iteration cycle and shortens time to revenue while maintaining a disciplined approach to customer experience design.
Smart Segmentation has shown tangible improvements in targeting accuracy and campaign performance. By leveraging AI-driven attributes and signals, teams can construct more precise audiences and tailor messages to the right segments at the right times. The added visibility into data usage and provenance helps marketers understand the foundations of the segments they deploy, improving trust and collaboration between marketing and data teams. The resulting campaigns tend to exhibit higher engagement and conversion rates, as audiences receive communications that align more closely with their interests and behaviors.
Brand Affinity and cross-channel sentiment analysis offer marketers deeper insights into how customers perceive a brand over time. By aggregating signals from multiple campaigns and channels, marketers can identify trends in sentiment and adjust creative, messaging, and channel strategy accordingly. The ability to view aggregate campaign sentiment helps teams make portfolio-level decisions, prioritizing campaigns that drive positive brand perception and adjusting those that do not. The WhatsApp integration extends reach into a channel that is deeply embedded in daily life for billions of users, enabling real-time, interactive conversations that feel personal and timely. The combination of AI-powered sentiment analysis and direct messaging on a globally popular platform can produce meaningful improvements in customer relationships and lifecycle outcomes.
For practitioners, the practical implications include better resource allocation, more efficient workflows, and the ability to scale personalized experiences without proportionally increasing headcount. AI-driven recommendations and automation help reduce manual data wrangling, enabling teams to focus on strategy, creativity, and optimization. The market-ready nature of these capabilities—designed to operate within enterprise-grade environments—addresses a common concern among marketing leaders: how to implement AI responsibly and effectively within existing platforms and processes.
The implications for enterprise AI strategy and governance
As marketers increasingly embrace AI-powered engagements, it becomes essential to align AI capabilities with organizational governance and compliance requirements. The emphasis on explainable AI within Brand Affinity signals a growing demand for transparency in how AI derives insights and makes recommendations. For enterprises, this translates into a need for robust data lineage, auditing capabilities, and governance frameworks that ensure AI outputs are interpretable, auditable, and aligned with business objectives.
The integration of WhatsApp as a native channel also raises important considerations around data privacy, consent, and messaging standards. Enterprises must design communication strategies that respect user preferences, comply with platform policies, and maintain a consistent brand voice across channels. The ability to automate campaigns across lifecycle stages must be balanced with safeguards to prevent fatigue, ensure opt-out options, and maintain a respectful cadence of interactions.
In a broader sense, the convergence of AI, data, and orchestration platforms signals a shift toward unified marketing ecosystems. The goal is to reduce friction between data collection, audience construction, creative execution, and measurement, creating a closed-loop system in which insights quickly translate into action and then back into deeper understanding. For marketers, this means adopting AI-enabled tools that prioritize reliability, scalability, and clear ROI while maintaining control over data usage, governance, and strategic direction.
VB Daily and industry-forward insights
Industry coverage and practical case studies play a critical role in helping marketing teams understand how generative AI is deployed in real-world settings. Industry newsletters and daily briefs offer a window into regulatory shifts, deployment strategies, and practical deployments that translate into value for organizations. For marketers, staying informed about evolving AI practices, success stories, and cautionary notes can help shape internal roadmaps, governance policies, and investment decisions. The coverage emphasizes understanding when and how to apply AI techniques effectively, as well as how to measure and communicate ROI to stakeholders.
In this context, the wider industry conversation includes how AI-driven marketing intersects with data privacy, customer trust, and responsible AI usage. Teams consider the balance between experimentation and governance, ensuring that AI enhancements deliver meaningful outcomes without compromising compliance or customer confidence. The ongoing discourse also highlights the need for continuous learning and adaptation as AI capabilities evolve, enabling marketing leaders to calibrate strategies in line with technological advances, market dynamics, and consumer expectations.
Practical takeaways for marketers and organizations
- AI can accelerate the design and deployment of sophisticated customer journeys, reducing time-to-value and enabling rapid experimentation with multiple paths.
- Richer audience segmentation is achievable through AI-driven aggregation of attributes and signals, empowering marketers to target the right people with the right messages at the right time.
- Understanding customer sentiment across campaigns and channels helps optimize portfolio strategy, ensuring messages resonate and brand perception improves over time.
- Expanding reach through high-engagement channels like WhatsApp allows for real-time, personalized, and interactive customer interactions at scale.
- A disciplined approach to governance and explainability ensures AI outputs are trusted, auditable, and aligned with business objectives.
Conclusion
The Activate Summit announcements reaffirm a clear trend: AI-powered capabilities are moving from experimental add-ons to foundational components of enterprise marketing success. Journey Assist, Smart Segmentation, Brand Affinity, and WhatsApp integration collectively enable marketers to design faster, smarter, and more effective campaigns, grounded in data-driven insights and amplified by AI’s capabilities. As brands confront the realities of AI scaling—the need for efficiency, cost control, and measurable ROI—these tools offer a practical path to turning data into meaningful customer experiences at scale. The market context confirms strong growth in AI-enabled customer engagement platforms, with major players across the ecosystem expanding capabilities to manage cross-channel experiences, deliver personalized interactions, and measure impact with clarity. For marketing teams aiming to stay ahead in a dynamic, AI-driven landscape, embracing these capabilities can translate into tangible improvements in engagement, conversion, and long-term value, while maintaining governance, privacy, and strategic alignment across the organization.