In the event you’ve scrolled LinkedIn currently, chances are you’ll assume each firm has cracked the code on utilizing AI in buyer engagement. Bots, automation, and personalization — the buzzwords stack up quick, however what number of are seeing tangible outcomes?

Anybody who’s spent even a day managing buyer journeys is aware of actuality is much extra nuanced. Sure, sensible automation can scale back repetitive duties, predictive scoring can establish alternatives, and customized messaging can seize buyer consideration (typically spectacularly). However the flip facet can be true. 

I’ve seen AI-generated content material that reads awkwardly and robotically, predictive fashions that steadily miss key context, and, with out strong knowledge, even the most effective algorithm is simply guessing.

That’s why I went straight to the platforms powering buyer engagement at scale. Over the previous month, I’ve gathered candid enter from 5 corporations: MoEngage, Insider, Buyer.io, Netcore Cloud, and HasData. Collectively, they serve industries from SaaS and fintech to e-commerce and media. I requested them what’s working, the place AI nonetheless underdelivers, and which new options they imagine will matter most within the coming 12 months.

This isn’t about fancy advertising and marketing claims or futuristic predictions. It’s about sensible truths you may act on at present.

These 5 corporations have been refreshingly open about their wins and challenges, serving to me see what’s occurring and what nonetheless wants work.

Whether or not you lead a customer-facing group, make strategic choices about tech investments, or simply need the within scoop on AI-driven engagement, this AI in buyer engagement report is constructed for you. 

Buyer-facing groups have extra channels, knowledge, and complexity to deal with than ever earlier than. It is easy to see why automation and sensible personalization are interesting. However there’s an enormous hole between what’s stylish and what’s genuinely useful concerning AI in buyer engagement.

Taking a look at how distributors are utilizing AI, a transparent sample emerges: regardless of the flood of recent options available in the market, B2B groups are doubling down on two capabilities they already belief: predictive segmentation and AI-powered personalization. They’re not chasing novelty right here; they’re sticking with instruments that reduce work, scale back guesswork, and present outcomes they will measure.

Collectively, they kind a core functionality driving measurable affect in AI-powered engagement for B2B corporations.

1. Predictive scoring and segmentation: Understanding your buyer higher, quicker

I’ve seen entrepreneurs spend lots of time manually constructing viewers lists, solely to seek out half the individuals within the phase would by no means act. Predictive fashions are altering that. Each vendor on this survey mentioned their clients use AI to robotically group audiences and rating their chance to purchase, churn, or re-engage.

For some platforms, the standout profit is pace. MoEngage, which holds a 4.5 score on G2 and scores particularly excessive for ease of use, notes that clients expertise shorter prep cycles and launch extra campaigns per quarter as soon as AI manages viewers setup. Insider, with a 4.8 G2 score and recognition for its strong personalization instruments, highlights improved engagement charges when predictive segmentation is paired with automated choices on channel and timing, making certain messages attain the suitable viewers on the proper time.

On the personalization entrance, Buyer.io, which carries a 4.4 G2 score, stands out for its phase builder — a go-to characteristic for entrepreneurs with out devoted engineering assist. It permits them to construct complicated viewers teams in actual time and act immediately on insights. In the meantime, Netcore Cloud, with a 4.5 G2 score and excessive marks for predictive analytics accuracy, empowers clients to leverage behavior-driven affinity and propensity fashions, delivering measurable lifts in conversion charges.

HasData clients have decreased churn by reaching at-risk accounts earlier, due to predictive fashions that flag potential leavers and set off automated outreach earlier than disengagement turns into everlasting.

2. AI-powered personalization: Automation and real-time relevance

Scaling one-to-one outreach manually is not reasonable; groups that attempt usually find yourself with inconsistent timing, missed alerts, and content material that feels stale. AI solves this by protecting customized outreach constant, well timed, and scalable, with out overwhelming advertising and marketing groups.

Each vendor within the survey confirmed that automated, customized messaging is now a regular functionality. These techniques deal with repetitive execution so entrepreneurs can concentrate on inventive technique and higher-value work. Clients see the strongest retention beneficial properties when automated campaigns are triggered by key behavioral alerts, equivalent to product inactivity, onboarding drop-off, or characteristic adoption milestones. Appearing on these alerts early helps forestall churn and fosters stronger engagement over time.

MoEngage and Insider clients report greater conversion and click-through charges from campaigns that launch rapidly and adapt dynamically to person habits. Buyer.io shoppers spotlight smoother onboarding and adoption journeys, with real-time personalization serving to customers attain worth milestones quicker. Netcore Cloud clients additionally see improved outcomes with focused presents that adapt in actual time, delivering contextually related promotions or messages at essential engagement factors.

This isn’t about utilizing AI for novelty; it’s about timeliness and precision. When outreach is delivered within the second, clients usually tend to have interaction and take significant motion.

Insider’s upcoming Sirius AI goals to take entrepreneurs from prediction to completely automated motion, deciding on the optimum message, channel, and timing for every buyer. Constructing on its predictive instruments, this step is meant to scale back resolution bottlenecks and pace up marketing campaign execution with out including handbook steps.

Equally, Netcore Cloud is rolling out AI brokers that may proactively have interaction with clients and adapt techniques on the fly utilizing reside behavioral alerts. The intent is to deal with extra of the engagement course of end-to-end, releasing groups to concentrate on technique and inventive planning.

3. Actual-time, in-product assistants

Not each significant interplay occurs by means of an electronic mail, push notification, or SMS. In lots of circumstances, probably the most influential engagement moments occur contained in the product, whereas the shopper is actively utilizing it. That’s why some distributors are turning their consideration to AI-driven in-product assistants.

Who’s investing right here:

Buyer.io is creating an in-product assistant that may ship customized nudges and suggestions immediately throughout the app expertise. By embedding assist immediately into the person workflow, the objective is to information clients towards key actions with out relying solely on electronic mail or exterior channels.

4. Clearer predictive analytics and insights

Predictive analytics will be highly effective provided that the groups utilizing it perceive and belief the outputs. I’ve seen engagement groups stall on a choice just because they weren’t assured in decoding a mannequin’s end result accurately. In these moments, AI’s pace benefit is misplaced.

Who’s investing right here:

HasData’s upcoming enhanced knowledge analytics and predictive insights are designed to make predictive outputs simpler to interpret. By clarifying what the info means and the possible affect of every motion, these instruments purpose to assist entrepreneurs reply quicker and with better confidence.

Revolutionary groups aren’t chasing AI for its personal sake. They’re investing in options that tangibly scale back handbook effort, sharpen predictions, and enhance buyer experiences proper now, and these distributors are aligned with that precedence.

“B2B groups crave unified views throughout channels. Automated content material tagging and intent detection are on their wishlist, however few platforms nail this at scale. The largest wins come from combining first-party utilization knowledge with actual search habits — monitoring not simply clicks, however what customers *attempt* to seek out. That is the place insights floor.”

Borets Stamenov
Co-Founder and CEO, SeekFast

MoEngage, in the meantime, sees appreciable variance. Their clients vary broadly, from refined AI adopters who observe each metric, to these nonetheless asking, “So how precisely can we use this successfully?” This variation highlights that AI maturity would not observe a neat development; it has an uneven tempo of adoption throughout industries and organizations.

Monitoring ROI stays a problem

Measuring the affect of AI-driven engagement stays inconsistent throughout distributors. Some platforms are seeing excessive ranges of ROI monitoring, whereas others notice that many shoppers are nonetheless in early levels of constructing measurement frameworks.

• MoEngage: Over 75% of consumers measure ROI, reflecting maturity and a tradition of accountability.
• Insider: 25 to 50% constantly observe ROI, grappling with the complexities of attribution and measurement.
• Netcore Cloud and HasData: Fewer than 25% measure ROI.
• Buyer.io: Restricted visibility, suggesting their clients may battle with clear metrics or dealing with measurement outdoors their platform’s view.

This hole underscores a essential motion merchandise for any decision-maker studying this report: when you’re adopting AI-driven instruments, clearly outline and measure your engagement success standards. With out this, even refined know-how cannot absolutely show its worth.

First-party knowledge is turning into the usual

All 5 distributors confirmed a decisive shift towards first-party knowledge methods up to now 12 months, pushed by tightening privateness rules and rising shopper expectations for transparency. MoEngage, Netcore Cloud, and HasData described this shift as “important,” emphasizing it as elementary to efficient AI engagement. Buyer.io and Insider agreed, although labeling the change as “reasonable,” acknowledging that hybrid methods nonetheless dominate many advertising and marketing stacks.

In sensible phrases, this shift is a direct call-to-action for customer-facing leaders: investing now in high quality first-party knowledge is not simply sensible; it is more and more obligatory for profitable AI-powered personalization.

“First-party knowledge is rapidly turning into the gold customary, particularly in high-trust sectors like monetary providers and healthcare. Shoppers need extra management over their knowledge and are getting extra intentional about the way it’s collected and used. Third-party knowledge nonetheless performs a task in enrichment, however there is a shift towards cleaner CRM practices and deeper inside insights.”

Matt Erhard
Managing Accomplice, Summit Search Group

Finally, AI maturity is not about racing forward however readability, intentionality, and disciplined measurement. Recognizing the place your organization sits is the primary essential step towards making smarter, extra sensible AI investments — with out falling for hype.

“The largest ROI often comes from higher retention and enlargement. If engagement helps a buyer get worth quicker, they’re extra more likely to stick round and develop along with your product. The quicker a buyer sees a transparent win, the extra invested they turn into. Time-to-value is among the strongest alerts we observe.”

Jamie Frew
CEO,Carepatron

Sooner marketing campaign supply

A number of respondents pointed to hurry as a tangible win. For MoEngage clients, predictive segmentation and AI-assisted content material creation have freed up group hours for inventive testing and optimization by launching campaigns as much as 50% quicker. With audiences outlined and message drafts generated rapidly, groups can run extra experiments in much less time.

Distributors additionally famous that proactive AI capabilities, equivalent to automated channel choice and real-time changes, are lowering handbook effort even additional. Insider and Netcore Cloud, for instance, are evolving past prediction to techniques that take direct, automated actions, accelerating marketing campaign execution with out sacrificing relevance or precision.

Smarter channel choice and better engagement

Insider studies greater click-through charges when campaigns regulate ship occasions to match every person’s exercise patterns. This timing optimization helps keep away from wasted sends and ensures messages attain clients once they’re most receptive.

Larger conversions by means of predictive focusing on

Netcore Cloud clients use affinity and propensity scoring fashions to focus outreach on probably the most promising segments, permitting groups to scale back marketing campaign quantity with out sacrificing affect. This focused method frees finances and assets for high-priority initiatives. Distributors reported this shift away from broad, undifferentiated outreach as a direct driver of improved ROI.

Focused progress alternatives

For HasData’s clients, predictive capabilities assist establish shoppers most probably to improve or buy extra providers. These fashions set off focused presents which have elevated upsell success charges and boosted general buyer lifetime worth.

Shorter time-to-value for brand spanking new clients

Buyer.io highlighted the affect of real-time personalization throughout onboarding and early product use. By inserting custom-made content material at exactly the suitable second, slightly than in delayed follow-up sequences, clients attain significant engagement milestones quicker. The impact is twofold: higher preliminary experiences and a decreased chance of early-stage drop-off.

On the identical time, AI-powered journey orchestration helps groups reclaim hours spent on handbook marketing campaign mapping. By automating setup and suggesting subsequent steps, entrepreneurs can focus extra on technique, testing, and inventive planning, slightly than repetitive execution duties.

These examples make one factor clear: AI’s worth in buyer engagement is most seen when it’s tied to a particular end result and measured rigorously. The businesses seeing the strongest outcomes align every functionality to an outlined objective and observe the affect from begin to end. The identical self-discipline that proves success additionally exposes the gaps, which is precisely the place we flip subsequent.

“Many of the AI in B2B engagement proper now could be flashy however shallow. Auto-emails, chat summaries, “sensible” sequences. Useful, positive, however not game-changing. What’s nonetheless lacking is actual reminiscence throughout the stack. A purchaser talks to gross sales, clicks just a few assist articles, then goes quiet. AI ought to sew that collectively and inform you what they’re considering. Proper now it would not.”

Santiago Nestares
CoFounder, DualEntry

Technique can’t be skipped

A number of distributors flagged a constant situation: AI is usually deployed with no clear engagement technique in place. MoEngage, for instance, cited “lack of context as a result of incomplete or hurriedly arrange journeys” as a core purpose why AI underperforms. When manufacturers attempt to shortcut strategic planning, AI fashions are left guessing, and clients discover.

The takeaway right here is easy however straightforward to miss: AI nonetheless wants human enter. It isn’t going to construct lifecycle levels for you, outline success metrics, or make clear who your ultimate buyer is. That also begins along with your group.

Poor knowledge = poor outcomes

HasData underscored a major limitation within the subject: AI instruments can’t compensate for low-quality or incomplete knowledge. They pointed to “the problem of poor knowledge high quality” and segmentation points as key causes AI fails to ship. This was echoed by Buyer.io, which shared that shoppers usually battle when plugging AI instruments into knowledge ecosystems that weren’t constructed with AI in thoughts.

Put merely: AI magnifies the standard of your knowledge infrastructure. If it’s fragmented, misaligned, or outdated, even probably the most superior engagement software will battle to drive outcomes.

Suggestions loops are lacking or too handbook

A recurring ache level is the dearth of real-time suggestions techniques that enable AI to enhance repeatedly. Whereas platforms provide strong analytics dashboards, that knowledge isn’t repeatedly fed again into the AI layer to regulate content material, timing, or channel choice dynamically.

Buyer.io described this as a spot between sign assortment and decision-making, the place groups could assessment efficiency, however don’t constantly retrain fashions or replace focusing on logic primarily based on what works.

AI remains to be early-stage for a lot of B2B corporations, which suggests probably the most profitable use circumstances aren’t essentially the flashiest, however the most effective aligned with a considerate technique and clear inputs. That’s what separates hype from sturdy outcomes.

“From the AI perspective, clear, centralized knowledge techniques assist ship account-based personalization at scale. With their implementations of dynamic content material modifications, firmer advertising and marketing and gross sales alignment, and dynamic workflows, they appear to be unlocking worth to a stage that may even be measured.”

Yaniv Masjedi
Chief Advertising and marketing Officer, Nextiva

AI in engagement doesn’t fail as a result of the know-how isn’t highly effective — it fails when it’s misapplied, fed poor knowledge, or deployed with no strategic framework. As soon as these foundations are strong, the actual query turns into: which alerts ought to AI act on? In any case, engagement AI delivers worth solely when it aligns clear inputs and a powerful technique with the behaviors that reliably predict long-term retention.

• Lifecycle-stage and onboarding communications be sure that clients obtain data and prompts that match their present relationship with the services or products. This might imply a guided introduction for brand spanking new customers or focused reactivation for long-term clients who’ve gone inactive.
• Actual-time behavioral triggers enable engagement to answer what the shopper is doing within the second, for instance, sending a useful immediate once they begin utilizing a brand new characteristic or providing help if they seem caught in a workflow.
• Position or persona-based messaging adjusts content material and presents primarily based on a buyer’s profile or operate inside a company, making certain that every recipient sees probably the most related materials.

Examples of affect at scale

When personalization methods are executed effectively, distributors reported significant outcomes. Buyer.io described dynamic content material insertion throughout onboarding by serving to clients notice advantages sooner, rising buyer satisfaction early within the relationship. 

MoEngage cited a retail shopper whose insights-led personalization elevated common order worth by selling complementary merchandise tailor-made to every shopper’s buy historical past. HasData highlighted focused re-engagement campaigns that revived dormant accounts, rising energetic person counts by tailoring presents and messaging to particular inactivity patterns

Obstacles to scaling personalization

Regardless of progress, distributors recognized a number of recurring points that forestall corporations from scaling personalization successfully:

• Gaps in knowledge completeness and accessibility stay the most typical limitations. If buyer information are incomplete or inconsistent, the system has much less context for delivering related messages.
• Restricted engineering or IT assets can sluggish the implementation of superior personalization workflows, particularly when integration with a number of techniques is required.
• Content material manufacturing bottlenecks happen when advertising and marketing groups can not create or adapt sufficient high-quality variations to match the complexity of their focusing on logic.

See the place your AI engagement stands. G2’s AI Advertising and marketing Thoughts Report explores how groups use clever instruments to personalize at scale, enhance analytics, and strengthen buyer connections.