If you’ve scrolled LinkedIn lately, you may think every company has cracked the code on using AI in customer engagement. Bots, automation, and personalization — the buzzwords stack up fast, but how many are seeing tangible results?
Anyone who’s spent even a day managing customer journeys knows reality is far more nuanced. Yes, smart automation can reduce repetitive tasks, predictive scoring can identify opportunities, and personalized messaging can grab customer attention (sometimes spectacularly). But the flip side is also true.
I’ve seen AI-generated content that reads awkwardly and robotically, predictive models that frequently miss key context, and, without solid data, even the best algorithm is just guessing.
That’s why I went straight to the platforms powering customer engagement at scale. Over the past month, I’ve gathered candid input from five companies: MoEngage, Insider, Customer.io, Netcore Cloud, and HasData. Collectively, they serve industries from SaaS and fintech to e-commerce and media. I asked them what’s working, where AI still underdelivers, and which new features they believe will matter most in the coming year.
This isn’t about fancy marketing claims or futuristic predictions. It’s about practical truths you can act on today.
TL;DR: AI in customer engagement at a glance
- Adoption drives measurable impact: All five vendors use predictive segmentation and automated messaging, and it’s paying off. Predictive segmentation speeds campaign launches for 3/5 vendors and reduces churn for 2/5, while automation also contributes to churn reduction for 2/5. Four vendors additionally use real-time personalization to boost retention.
- Innovation is targeted, not hype: Every vendor in the list has AI features launching in the next 12 months, with 3/5 adding autonomous action capabilities and others focusing on journey orchestration, in-product assistants, and clearer analytics.
- AI maturity is uneven: 2/5 vendors say most customers are in the experimental stage, 2/5 in evaluation but not scaling, and 1/5 report wide variance from advanced to beginner adopters.
- ROI tracking is inconsistent: MoEngage leads with >75% of customers measuring ROI, while Netcore Cloud and HasData are under 25%.
- Shift to first-party data is accelerating: All vendors report the move. Three call it “significant,” while two label it “moderate.”
- Quantified vendor results: MoEngage delivers campaigns 50% faster; Insider improves CTR via send-time optimization; Netcore boosts conversions with predictive targeting; HasData increases upsells; Customer.io reduces onboarding drop-off.
- Key barriers remain: Data quality issues (3/5), incomplete journeys (2/5), and missing feedback loops limit AI success.
- Budgets are rising strategically: 4/5 vendors report 10–25% YoY increases, with spending aimed at cleaner data flows, adaptive segmentation, and self-refining models.
- SaaS and e-commerce lead industry spend: 4/5 vendors see them as top sectors, followed by fintech (3/5), with healthcare and media/entertainment emerging.
These five companies were refreshingly open about their wins and challenges, helping me see what’s happening and what still needs work.
Who are the five innovators shaping AI in customer engagement right now?
- MoEngage is known for making multi-channel customer journeys simpler and quicker with AI-assisted messaging. If you’ve launched a campaign lately, you’ve probably seen its push notifications or emails in action.
- Insider is all about making personalization truly personal, using predictive tools to match messages with moments that matter. It makes marketing feel less automated and more human.
- Customer.io is best for tailored experiences, especially for SaaS and subscription businesses, and helps marketers create meaningful touchpoints.
- Netcore Cloud is widely regarded for using AI to help companies anticipate when someone’s about to churn or ready to buy. No crystal ball, just data.
- HasData, stands true to its name by living in the data. It makes customer engagement smarter by providing tools that interpret complex analytics, automate timely interactions, and genuinely cut down churn rates.
Whether you lead a customer-facing team, make strategic decisions about tech investments, or just want the inside scoop on AI-driven engagement, this AI in customer engagement report is built for you.
Methodology: How I gathered these insights
Over the course of July 2025, I sent a structured questionnaire to the five participating vendors. The survey asked them to share:
- The AI capabilities most widely adopted by their B2B customers today.
- Features customers are requesting or planning to add in the next 12 months.
- Concrete metrics showing positive impact from AI-driven engagement.
- Pain points and familiar sources of disappointment.
- How customers are measuring ROI.
- Industry and behavioral signal trends.
- Personalization strategies and scaling challenges.
I’ve included exact data points and concrete examples from vendor responses where possible. For qualitative responses, I’ve distilled them into themes and paired them with actionable insights.
Which AI capabilities are B2B teams using?
Customer-facing teams have more channels, data, and complexity to handle than ever before. It’s easy to see why automation and smart personalization are appealing. But there’s a big gap between what’s trendy and what’s genuinely helpful regarding AI in customer engagement.
Looking at how vendors are using AI, a clear pattern emerges: despite the flood of new features in the market, B2B teams are doubling down on two capabilities they already trust: predictive segmentation and AI-powered personalization. They’re not chasing novelty here; they’re sticking with tools that cut work, reduce guesswork, and show results they can measure.
Together, they form a core capability driving measurable impact in AI-powered engagement for B2B companies.
1. Predictive scoring and segmentation: Knowing your customer better, faster
I’ve seen marketers spend a lot of time manually building audience lists, only to find half the people in the segment would never act. Predictive models are changing that. Every vendor in this survey said their customers use AI to automatically group audiences and score their likelihood to buy, churn, or re-engage.
For some platforms, the standout benefit is speed. MoEngage, which holds a 4.5 rating on G2 and scores especially high for ease of use, notes that customers experience shorter prep cycles and launch more campaigns per quarter once AI manages audience setup. Insider, with a 4.8 G2 rating and recognition for its robust personalization tools, highlights improved engagement rates when predictive segmentation is paired with automated decisions on channel and timing, ensuring messages reach the right audience at the right time.
On the personalization front, Customer.io, which carries a 4.4 G2 rating, stands out for its segment builder — a go-to feature for marketers without dedicated engineering support. It enables them to build complex audience groups in real time and act instantly on insights. Meanwhile, Netcore Cloud, with a 4.5 G2 rating and high marks for predictive analytics accuracy, empowers customers to leverage behavior-driven affinity and propensity models, delivering measurable lifts in conversion rates.
HasData customers have reduced churn by reaching at-risk accounts earlier, thanks to predictive models that flag potential leavers and trigger automated outreach before disengagement becomes permanent.
Data at a glance:
- All vendors named predictive segmentation as a top current capability.
- MoEngage, Insider, and Netcore Cloud linked it directly to faster campaign launches.
- HasData and Customer.io reported measurable churn reduction from predictive triggers. Others cited incomplete journeys, early adoption, and data quality issues as barriers.
2. AI-powered personalization: Automation and real-time relevance
Scaling one-to-one outreach manually is no longer realistic; teams that try often end up with inconsistent timing, missed signals, and content that feels stale. AI solves this by keeping personalized outreach consistent, timely, and scalable, without overwhelming marketing teams.
Every vendor in the survey confirmed that automated, personalized messaging is now a standard capability. These systems handle repetitive execution so marketers can focus on creative strategy and higher-value work. Customers see the strongest retention gains when automated campaigns are triggered by key behavioral signals, such as product inactivity, onboarding drop-off, or feature adoption milestones. Acting on these signals early helps prevent churn and fosters stronger engagement over time.
MoEngage and Insider customers report higher conversion and click-through rates from campaigns that launch quickly and adapt dynamically to user behavior. Customer.io clients highlight smoother onboarding and adoption journeys, with real-time personalization helping users reach value milestones faster. Netcore Cloud customers also see improved outcomes with targeted offers that adapt in real time, delivering contextually relevant promotions or messages at critical engagement points.
This isn’t about using AI for novelty; it’s about timeliness and precision. When outreach is delivered in the moment, customers are more likely to engage and take meaningful action.
Data at a glance:
- All five vendors report widespread adoption of automated, personalized messaging.
- MoEngage, Insider, Customer.io, and Netcore Cloud identify real-time personalization as a key driver of retention and satisfaction.
- Common triggers include onboarding completion, product inactivity, and key feature adoption milestones.
Which AI innovations will redefine customer engagement in the next 12 months?
Looking at how vendors are approaching the future of AI in customer engagement, the focus isn’t on speculation but on practical features already in development or actively being tested with customers.
Every response carried the same message: there’s no time to wait. Whether it’s compressing the time it takes to build a multi-step journey, moving from prediction to real-time action, or making analytics easier to interpret, these innovations share a single aim: to shorten the distance between seeing an opportunity and acting on it.
Across the five platforms, four innovation themes emerged.
1. AI-powered journey orchestration
Building complex customer journeys manually can take hours, with marketers mapping every interaction, trigger, and channel decision step by step. AI-powered journey orchestration uses data to suggest the best next steps automatically, helping teams create accurate, optimized journeys faster.
Who’s investing here:
MoEngage is preparing to launch journey orchestration tools powered by AI prompts. These tools are designed to guide teams through building complex journeys efficiently by recommending steps across content, timing, and channels.
2. AI agents: From prediction to proactive action
One of the biggest gaps in current AI adoption is what happens after a model makes a prediction. Marketers want AI to do more than just predict; they want it to take meaningful actions proactively. Think of it as moving from “Here’s what your customer might do next” to “We’ve already handled this for you.”
Who’s investing here:
Insider’s upcoming Sirius AI aims to take marketers from prediction to fully automated action, selecting the optimal message, channel, and timing for each customer. Building on its predictive tools, this step is intended to reduce decision bottlenecks and speed up campaign execution without adding manual steps.
Similarly, Netcore Cloud is rolling out AI agents that can proactively engage with customers and adapt tactics on the fly using live behavioral signals. The intent is to handle more of the engagement process end-to-end, freeing teams to focus on strategy and creative planning.
3. Real-time, in-product assistants
Not every meaningful interaction happens through an email, push notification, or SMS. In many cases, the most influential engagement moments happen inside the product, while the customer is actively using it. That’s why some vendors are turning their attention to AI-driven in-product assistants.
Who’s investing here:
Customer.io is developing an in-product assistant that will deliver personalized nudges and recommendations directly within the app experience. By embedding support directly into the user workflow, the goal is to guide customers toward key actions without relying solely on email or external channels.
4. Clearer predictive analytics and insights
Predictive analytics can be powerful only if the teams using it understand and trust the outputs. I’ve seen engagement teams stall on a decision simply because they weren’t confident in interpreting a model’s result correctly. In those moments, AI’s speed advantage is lost.
Who’s investing here:
HasData’s upcoming enhanced data analytics and predictive insights are designed to make predictive outputs easier to interpret. By clarifying what the data means and the likely impact of each action, these tools aim to help marketers respond faster and with greater confidence.
Data at a glance
- All five vendors have at least one new AI capability planned for release within the next 12 months.
- Three vendors are building autonomous action features that will remove manual intervention.
- Two vendors are prioritizing clearer, more explainable predictive analytics.
- Journey orchestration and in-product assistants are each being developed by at least one vendor in the survey group.
Innovative teams aren’t chasing AI for its own sake. They’re investing in features that tangibly reduce manual effort, sharpen predictions, and improve customer experiences right now, and these vendors are aligned with that priority.
“B2B teams crave unified views across channels. Automated content tagging and intent detection are on their wishlist, but few platforms nail this at scale. The biggest wins come from combining first-party usage data with real search behavior — tracking not just clicks, but what users *try* to find. That’s where insights surface.”
Borets Stamenov
Co-Founder and CEO, SeekFast
How far along are B2B companies in AI adoption, and what’s holding them back?
When you peel back the shiny marketing layer of AI-powered engagement, a more grounded reality emerges: not every company is equally comfortable or confident with AI. Vendors offered candid assessments of where their customers really stand, and the truth is, most are still figuring things out.
AI maturity isn’t linear. Some companies are running small pilot programs, others are cautiously evaluating their options, and a few have advanced to more sophisticated implementations. Progress depends as much on industry dynamics and available resources as it does on leadership priorities and technical expertise.
Most companies are still testing the waters
According to Customer.io and HasData, many of their clients remain firmly in the experimental stage, with roadmaps that are still evolving. This doesn’t mean they’re hesitant; they’re learning by running pilots, measuring initial outcomes, and gradually expanding AI capabilities as they find what delivers real-world value.
Insider and Netcore Cloud described a slightly different scenario. Their customers are past the pilot stage and actively evaluating AI, but many haven’t fully committed to scaling. Insider characterizes this group as “largely undecided,” while Netcore Cloud notes that teams often pause until they see consistent performance gains, especially in conversion rates, before rolling AI features out more broadly.
MoEngage, meanwhile, sees considerable variance. Their customers range widely, from sophisticated AI adopters who track every metric, to those still asking, “So how exactly do we use this effectively?” This variation highlights that AI maturity doesn’t follow a neat progression; it has an uneven pace of adoption across industries and organizations.
Tracking ROI remains a challenge
Measuring the impact of AI-driven engagement remains inconsistent across vendors. Some platforms are seeing high levels of ROI tracking, while others note that many customers are still in early stages of building measurement frameworks.
- MoEngage: Over 75% of customers measure ROI, reflecting maturity and a culture of accountability.
- Insider: 25 to 50% consistently track ROI, grappling with the complexities of attribution and measurement.
- Netcore Cloud and HasData: Fewer than 25% measure ROI.
- Customer.io: Limited visibility, suggesting their customers might struggle with clear metrics or handling measurement outside their platform’s view.
This gap underscores a critical action item for any decision-maker reading this report: if you’re adopting AI-driven tools, clearly define and measure your engagement success criteria. Without this, even sophisticated technology can’t fully prove its value.
First-party data is becoming the standard
All five vendors confirmed a decisive shift toward first-party data strategies in the past year, driven by tightening privacy regulations and growing consumer expectations for transparency. MoEngage, Netcore Cloud, and HasData described this shift as “significant,” emphasizing it as fundamental to effective AI engagement. Customer.io and Insider agreed, though labeling the change as “moderate,” acknowledging that hybrid strategies still dominate many marketing stacks.
In practical terms, this shift is an immediate call-to-action for customer-facing leaders: investing now in quality first-party data isn’t just smart; it’s increasingly mandatory for successful AI-powered personalization.
“First-party data is quickly becoming the gold standard, especially in high-trust sectors like financial services and healthcare. Clients want more control over their data and are getting more intentional about how it’s collected and used. Third-party data still plays a role in enrichment, but there’s a shift toward cleaner CRM practices and deeper internal insights.”
Matt Erhard
Managing Partner, Summit Search Group
Data at a glance
- Customer.io and HasData described their customers as primarily in the experimental stage of AI adoption.
- Insider and Netcore Cloud said most customers are still evaluating AI without committing to scale.
- MoEngage reported a wide variance, with customers ranging from advanced adopters to teams just starting out.
- Depending on the vendor, ROI tracking rates ranged from under 25% to over 75%.
- All vendors observed a shift toward first-party data strategies, with three calling it “significant.”
Ultimately, AI maturity isn’t about racing ahead but clarity, intentionality, and disciplined measurement. Recognizing where your company sits is the first critical step toward making smarter, more practical AI investments — without falling for hype.
What measurable results are companies seeing from AI-powered engagement?
It’s easy to promise better customer engagement with AI. But promises don’t keep marketing budgets funded; results do. The results shared by vendors go beyond vague claims like “improved efficiency” or “better targeting,” offering concrete metrics and real-world examples of how AI is transforming customer engagement.
Vendors also reported clear retention gains from automated triggers that engage users showing early signs of churn. By acting on behavioral indicators such as product inactivity or onboarding drop-off, these workflows re-engage customers at the right time, improving retention without adding extra manual work.
The responses include hard metrics, specific examples, and success stories that illustrate how AI pays off for B2B engagement teams today.
“The biggest ROI usually comes from better retention and expansion. If engagement helps a customer get value faster, they’re more likely to stick around and grow with your product. The faster a customer sees a clear win, the more invested they become. Time-to-value is one of the strongest signals we track.”
Faster campaign delivery
Several respondents pointed to speed as a tangible win. For MoEngage customers, predictive segmentation and AI-assisted content creation have freed up team hours for creative testing and optimization by launching campaigns up to 50% faster. With audiences defined and message drafts generated quickly, teams can run more experiments in less time.
Vendors also noted that proactive AI capabilities, such as automated channel selection and real-time adjustments, are reducing manual effort even further. Insider and Netcore Cloud, for example, are evolving beyond prediction to systems that take direct, automated actions, accelerating campaign execution without sacrificing relevance or precision.
Smarter channel selection and higher engagement
Insider reports higher click-through rates when campaigns adjust send times to match each user’s activity patterns. This timing optimization helps avoid wasted sends and ensures messages reach customers when they’re most receptive.
Higher conversions through predictive targeting
Netcore Cloud customers use affinity and propensity scoring models to focus outreach on the most promising segments, allowing teams to reduce campaign volume without sacrificing impact. This targeted approach frees budget and resources for high-priority initiatives. Vendors reported this shift away from broad, undifferentiated outreach as a direct driver of improved ROI.
Targeted growth opportunities
For HasData’s customers, predictive capabilities help identify clients most likely to upgrade or purchase additional services. These models trigger targeted offers that have increased upsell success rates and boosted overall customer lifetime value.
Shorter time-to-value for new customers
Customer.io highlighted the impact of real-time personalization during onboarding and early product use. By inserting customized content at precisely the right moment, rather than in delayed follow-up sequences, customers reach meaningful engagement milestones faster. The effect is twofold: better initial experiences and a reduced likelihood of early-stage drop-off.
At the same time, AI-powered journey orchestration is helping teams reclaim hours spent on manual campaign mapping. By automating setup and suggesting next steps, marketers can focus more on strategy, testing, and creative planning, rather than repetitive execution tasks.
Data at a glance
- MoEngage, Insider, Customer.io, and Netcore Cloud shared quantified customer outcomes tied to AI adoption.
- Reported improvements include: up to 50% faster campaign launches, measurable conversion lifts, significant churn reduction, and shorter onboarding timelines.
- Common factor in all examples: AI was applied to a specific process with clear success metrics.
These examples make one thing clear: AI’s value in customer engagement is most visible when it is tied to a specific outcome and measured rigorously. The companies seeing the strongest results align each capability to a defined goal and track the impact from start to finish. The same discipline that proves success also exposes the gaps, which is exactly where we turn next.
Why these success stories matter (and how to replicate them)
While each vendor shared distinct experiences, a common thread emerged: companies achieving standout results clearly defined their success criteria upfront. If you want similar outcomes, here’s what to do right now:
- Automate thoughtfully. Choose specific marketing bottlenecks (like channel selection or churn triggers) and start small. Successful companies see the biggest returns when they automate clearly defined, measurable tasks.
- Be proactive, not reactive. Use AI to get ahead of customer behavior — predict churn or interest instead of waiting for signals. Predictive tools consistently outperform reactive ones in driving meaningful customer outcomes.
- Personalize early. Real-time personalization is most effective when introduced at critical engagement stages, such as onboarding or early product interactions. Prioritize AI investments that make your first customer interactions count.
- Let AI handle routine content creation. Leverage AI content generation for routine, repetitive messaging tasks. This frees human resources for strategy and creativity, improving overall campaign quality and team morale.
Why does AI in customer engagement sometimes fail to deliver?
AI may promise hyper-efficiency and personalization, but ask the teams deploying it, and a different reality surfaces. According to the vendors in this report, the gap between expectation and outcome often stems from one thing: underestimating the work required to make AI work well.
“Most of the AI in B2B engagement right now is flashy but shallow. Auto-emails, chat summaries, “smart” sequences. Helpful, sure, but not game-changing. What’s still missing is real memory across the stack. A buyer talks to sales, clicks a few support articles, then goes quiet. AI should stitch that together and tell you what they’re thinking. Right now it doesn’t.”
Santiago Nestares
CoFounder, DualEntry
Strategy can’t be skipped
Several vendors flagged a consistent issue: AI is often deployed without a clear engagement strategy in place. MoEngage, for example, cited “lack of context due to incomplete or hurriedly set up journeys” as a core reason why AI underperforms. When brands try to shortcut strategic planning, AI models are left guessing, and customers notice.
The takeaway here is simple but easy to overlook: AI still needs human input. It isn’t going to build lifecycle stages for you, define success metrics, or clarify who your ideal customer is. That still starts with your team.
Poor data = poor outcomes
HasData underscored a significant limitation in the field: AI tools can’t compensate for low-quality or incomplete data. They pointed to “the challenge of poor data quality” and segmentation issues as key reasons AI fails to deliver. This was echoed by Customer.io, which shared that clients often struggle when plugging AI tools into data ecosystems that weren’t built with AI in mind.
Put simply: AI magnifies the quality of your data infrastructure. If it’s fragmented, misaligned, or outdated, even the most advanced engagement tool will struggle to drive results.
Feedback loops are missing or too manual
A recurring pain point is the lack of real-time feedback systems that allow AI to improve continuously. While platforms offer robust analytics dashboards, that data isn’t continuously fed back into the AI layer to adjust content, timing, or channel selection dynamically.
Customer.io described this as a gap between signal collection and decision-making, where teams may review performance, but don’t consistently retrain models or update targeting logic based on what works.
Data at a glance
- Insider, Customer.io, Netcore Cloud, and HasData cited data quality or availability issues as a primary barrier.
- MoEngage flagged issues with a lack of context due to incomplete or hurriedly written prompts during setup.
- Feedback loop gaps were mentioned by multiple respondents as a cause of stagnant performance.
AI is still early-stage for many B2B companies, which means the most successful use cases aren’t necessarily the flashiest, but the best aligned with a thoughtful strategy and clean inputs. That’s what separates hype from durable results.
What does this mean for customer-facing teams?
For leaders rolling out AI engagement strategies, these real-world insights offer a few crucial takeaways:
- Slow down to set context. Rushed setups are one of the biggest killers of value. Don’t skip the foundational steps like journey mapping, signal selection, and segment clarity.
- Audit your data quality. Before investing in AI capabilities, take a hard look at your engagement data. Are signals relevant? Are labels clean? Are customer records unified? AI can’t clean this up for you.
- Build feedback loops into your engagement models. AI tools won’t automatically adapt if you don’t connect campaign results directly to your next round of targeting and creative decisions.
- Clarify roles between platform and strategy. Engagement platforms offer tools. It’s your team’s job to define the strategic direction, measure outcomes, and hold those tools accountable.
“From the AI perspective, clean, centralized data systems help deliver account-based personalization at scale. With their implementations of dynamic content changes, firmer marketing and sales alignment, and dynamic workflows, they seem to be unlocking value to a level that can even be measured.”
Yaniv Masjedi
Chief Marketing Officer, Nextiva
AI in engagement doesn’t fail because the technology isn’t powerful — it fails when it’s misapplied, fed poor data, or deployed without a strategic framework. Once those foundations are solid, the real question becomes: which signals should AI act on? After all, engagement AI delivers value only when it aligns clean inputs and a strong strategy with the behaviors that reliably predict long-term retention.
Which customer behaviors most reliably predict long-term retention?
The way customers interact with a product or service generates an ongoing stream of behavioral data. Vendors in the survey were clear: certain signals consistently prompt the most effective automated engagement, and a few stand out as robust indicators of long-term retention.
The most tracked signals
When asked which behavioral triggers their customers monitor most frequently, vendors pointed to a blend of adoption milestones and risk indicators.
- Onboarding completion or drop-off was cited multiple times, reflecting its importance as an early-stage health metric. Completing onboarding usually correlates with higher product adoption, while dropping off signals a need for immediate intervention.
- Feature adoption milestones are another common trigger, particularly for SaaS products. Hitting these milestones often marks a deepening relationship with the product, and vendors noted that customers use these moments to prompt relevant tips or upsell offers.
- Product inactivity or churn risk remains a staple signal across industries. Even without explicit churn predictions, a period of inactivity often initiates a retention workflow.
The strongest retention correlations
The survey data also included vendor views on the single behavioral touchpoint most closely linked to improved retention. Responses varied, but some patterns emerged:
- MoEngage pointed to the number of campaigns launched and the volume of campaigns a user engages with as a clear indicator that a customer is active and seeing value.
- Insider identified feature usage and demonstrated value as their strongest retention drivers, aligning with the idea that ongoing engagement comes from perceived usefulness.
- Netcore Cloud noted an improvement in engagement and conversion metrics over time as its most reliable retention signal.
- HasData reinforced the importance of tracking inactivity patterns and designing re-engagement campaigns that restore active usage before accounts go completely dormant.
How can B2B teams scale personalization?
Scaling personalization has long been a goal for engagement teams, but the survey responses show that execution is still uneven. Vendors described a mix of practical approaches their customers are using to tailor engagement, along with persistent challenges that slow progress or reduce impact.
How personalization is being executed today
Vendors repeatedly mentioned lifecycle-stage communications, real-time behavioral triggers, and role- or persona-based messaging as the primary methods customers use to deliver personalized engagement.
- Lifecycle-stage and onboarding communications ensure that customers receive information and prompts that match their current relationship with the product or service. This could mean a guided introduction for new users or targeted reactivation for long-term customers who have gone inactive.
- Real-time behavioral triggers allow engagement to respond to what the customer is doing in the moment, for example, sending a helpful prompt when they start using a new feature or offering assistance if they appear stuck in a workflow.
- Role or persona-based messaging adjusts content and offers based on a customer’s profile or function within an organization, ensuring that each recipient sees the most relevant material.
Examples of impact at scale
When personalization strategies are executed well, vendors reported meaningful outcomes. Customer.io described dynamic content insertion during onboarding by helping customers realize benefits sooner, increasing customer satisfaction early in the relationship.
MoEngage cited a retail client whose insights-led personalization increased average order value by promoting complementary products tailored to each shopper’s purchase history. HasData highlighted targeted re-engagement campaigns that revived dormant accounts, increasing active user counts by tailoring offers and messaging to specific inactivity patterns
Obstacles to scaling personalization
Despite progress, vendors identified several recurring issues that prevent companies from scaling personalization effectively:
- Gaps in data completeness and accessibility remain the most common barriers. If customer records are incomplete or inconsistent, the system has less context for delivering relevant messages.
- Limited engineering or IT resources can slow the implementation of advanced personalization workflows, especially when integration with multiple systems is required.
- Content production bottlenecks occur when marketing teams cannot create or adapt enough high-quality variations to match the complexity of their targeting logic.
Practical takeaways for your team
- Start personalization efforts with the lifecycle stages where timing and relevance have the most tremendous revenue impact, such as onboarding or renewal.
- Invest in data hygiene before expanding personalization complexity; clean, complete records multiply the value of targeting efforts.
- Map content requirements alongside targeting logic to avoid creative bottlenecks that slow delivery.
- Focus on automating a smaller set of high-impact personalization flows before attempting full-scale implementation, especially where technical resources are limited.
Which industries are leading in AI-driven engagement?
The vendors’ responses reveal a concentrated pattern in where B2B companies are putting their engagement budgets. While industries vary in maturity and pace of adoption, specific sectors are clearly leading the way in AI-enhanced engagement initiatives. These are high-growth markets and industries where customer retention, tailored experiences, and real-time responsiveness are direct drivers of revenue.
SaaS and e-commerce lead the pack
Four of the five vendors identified SaaS and e-commerce among their top three industries for engagement investment. In SaaS, the push is driven by subscription-based models where every stage of the customer lifecycle — from onboarding to renewal — offers opportunities to reinforce value and reduce churn. AI’s role here is often about predicting risk, streamlining adoption, and tailoring communication for distinct user roles within the same account.
E-commerce, on the other hand, uses AI engagement to compete in an environment where customer attention is fleeting and switching costs are low. Vendors noted that predictive product recommendations, personalized offers, and timely re-engagement campaigns are becoming standard, not differentiators, for companies that want to hold market share.
Fintech remains a consistent growth area
Fintech appeared frequently in vendor responses, reflecting the industry’s need for highly relevant, trust-building communication. Engagement platforms are being used to anticipate customer needs based on transaction behavior, deliver security-related updates with precision, and create onboarding experiences that balance compliance with usability. The sensitivity of customer data and regulatory oversight means AI is often applied in measured, highly targeted ways.
Other high-engagement verticals
Several vendors also pointed to healthcare and media/entertainment as emerging engagement hotspots. In healthcare, AI tools are helping organizations segment patients or members for custom health reminders, benefit updates, and education programs. In media and entertainment, AI-driven engagement supports subscription retention, content curation, and reactivation of dormant users.
Where will engagement teams invest in 2025 and why?
When vendors described their customers’ engagement priorities for the year ahead, the patterns pointed to a dual focus: strengthening the systems that support engagement and expanding the capabilities that can act on that foundation. The survey responses showed precise alignment between where budgets are growing and the operational areas most in need of improvement.
Building faster, cleaner data flows
Several vendors reported that customers are putting effort into improving the speed and reliability of customer data movement through their systems. The goal is to have engagement-ready data available in real time, with fewer delays caused by manual updates or incomplete records. For AI-powered engagement features, this means models work with up-to-date, consistent inputs, reducing the lag between an action a customer takes and the platform’s ability to respond.
Advancing segmentation and predictive capabilities
Advanced segmentation and predictive analytics appeared repeatedly as top 2025 investment areas. Here, the emphasis is on making these tools more adaptive. Respondents described customers seeking segmentation logic that can adjust automatically as new behavioral signals are received, and predictive models that refine themselves continuously rather than in periodic batches. These upgrades are intended to support more fluid, accurate targeting without adding operational burden.
Focused budget growth
Four of the five vendors observed customer engagement budgets increasing by 10–25% over the past year. The increases are directed toward well-defined improvements, such as reducing manual setup in campaign workflows or enhancing analytics to shorten the time from insight to decision. The priority is on funding capabilities that directly influence efficiency or measurable performance outcomes.
Data at a glance
- Multiple vendors reported investments in faster, more reliable customer data pipelines.
- Advanced segmentation and predictive analytics ranked among the most common 2025 priorities, with a focus on adaptability to live data.
- MoEngage, Insider, Netcore Cloud, and HasData reported budgets rise by 10–25%, with spending directed at targeted performance improvements.
Key takeaways and what’s next for AI in customer engagement
After reviewing insights across five leading platforms, one theme stands out: AI in customer engagement is delivering results, but only when organizations approach it with focus, data discipline, and a clear strategy. The findings point to both what’s working well now and where teams should invest next to turn experimentation into repeatable success.
Here’s what the data revealed:
- Predictive segmentation and personalization are no longer optional. These capabilities are the backbone of successful AI-driven engagement, helping teams launch campaigns faster, reduce churn, and focus on high-value customer interactions.
- AI maturity is all over the map. While some companies are running advanced programs with ROI tracking in place, many are still testing features in pilot phases or experimenting without fully scaling their efforts.
- Data quality and strategic clarity are the real differentiators. Vendors repeatedly emphasized that fragmented or outdated data prevents AI from reaching its full potential. The most effective teams start by unifying customer records, mapping clean engagement signals, and tying every workflow to measurable goals.
- The shift to first-party data is accelerating. With privacy regulations tightening and customers expecting greater transparency, organizations are moving away from third-party reliance and investing heavily in first-party data systems that allow for compliant, personalized engagement at scale.
- Innovation is practical, not flashy. Instead of chasing headline-grabbing features, platforms are focusing on tools that shorten the gap between insight and action — things like real-time journey orchestration, autonomous decision-making, in-product assistants, and clearer predictive analytics.
What this means for you
Companies looking to mature their AI engagement strategies should prioritize three things above all else.
- Invest in real-time, reliable data pipelines that give AI systems accurate, up-to-the-minute information to work with.
- Embed feedback loops into every campaign so results directly inform and refine future targeting, timing, and messaging decisions.
- Treat AI as a core layer of the engagement strategy, not a side project or experimental add-on. The most successful teams approach AI adoption with the same rigor they apply to budgeting, segmentation, and customer experience design.
Companies that combine strategic clarity, disciplined measurement, and agile implementation will set the benchmark for what AI-powered engagement can achieve in 2025 and beyond.
See where your AI engagement stands. G2’s AI Marketing Mind Report explores how teams use intelligent tools to personalize at scale, improve analytics, and strengthen customer connections.