December 27, 2025
Across industries, boardrooms are making a blunt calculation: if AI can handle a growing share of customer interactions, why keep running sprawling, expensive support operations the old way?
HP plans to cut up to 6,000 roles to fund AI-driven “operational efficiency.” Allianz Partners is reportedly preparing to replace call-center jobs with an AI voice assistant that can handle hundreds of calls in 20+ languages. New research from MIT’s Iceberg Index suggests current AI could already perform the work of over 11% of the US workforce, with routine service and back-office roles among the most exposed.
But beneath the layoff headlines, something more structural is happening in customer experience (CX): enterprises are moving away from fragmented bots and ticketing queues toward AI-first, “agentic” customer journeys — where autonomous agents observe behavior across channels, decide when to intervene, and coordinate seamlessly with humans.
For SaaS leaders, this shift is both an opportunity and a trap. Done well, agentic journeys can lift activation, retention and CSAT at the same time as they reduce cost-to-serve. Done badly, they become another wave of brittle bots that quietly erode trust and brand equity.
This article unpacks what “agentic journeys” actually mean in 2025, what current research and market moves are signaling, and how to design AI agents that improve omnichannel CX rather than just headcount metrics — with a practical blueprint you can execute on top of a unified support and feedback OS like Gleap.
The last decade of “omnichannel” mostly meant adding more entry points — live chat, WhatsApp, in-app chat, social DMs — all feeding into ticket-based systems and scattered automations. The result in many SaaS organizations today:
In parallel, customer expectations have accelerated. According to recent CX trend research:
Traditional automation can’t keep up with that complexity. This is where agentic AI enters the picture.
Agentic AI differs from simple chatbots in three ways:
An agentic journey applies this model to the full lifecycle. Instead of designing 100 separate flows — a churn-prevention email here, a chat macro there — you define outcomes and guardrails, then let agents orchestrate across channels:
In other words, the journey becomes agent-led, not campaign-led.
Looking across the latest research and news, a few themes stand out.
Moves like HP’s and Allianz’s are rightly raising concern: AI is explicitly cited as a driver for large-scale reductions in support and operations headcount. MIT’s Iceberg Index backs up that the automation potential is real, especially in administrative, financial, and service roles.
But the same research also stresses that implementing AI as a direct human replacement is “enormously complicated and time-consuming”. In practice, enterprises that are seeing real results aren’t just swapping staff for bots; they are re-architecting how customer work gets done around AI agents and human specialists.
Consultancies and vendors tracking agentic AI adoption point to a different medium-term picture:
For SaaS leaders, the key is to avoid anchoring your AI strategy on near-term labor arbitrage only. That’s where trust, quality, and institutional knowledge are most likely to be sacrificed.
Recent CX trend reports are converging on the same directional signal:
In short: the agentic model is on track to become the default pattern for high-volume, digital-first customer operations, including B2B SaaS.
One major pain point exposed in recent SaaS and CX research is orchestration complexity. Enterprises are wrestling with:
At the same time, low-code/no-code has gone from novelty to norm. Gartner estimates the majority of new enterprise apps are now built or configured with low-code platforms, driven by the need to move faster without more engineering headcount.
For agentic journeys, this translates into an emerging requirement: non-technical CX and product teams need to be able to design and govern journey logic — which events matter, when agents are allowed to intervene, and how escalations work — without writing code.
That’s where a unified layer like Gleap becomes strategically important: a single place where signals, channels, and automations are wired together, with AI assisting but humans still owning the design.
To move beyond incremental bot deployments, CX leaders need to deliberately redesign their operating model around three shifts.
Many SaaS teams still think in terms of a bot per channel:
In an agentic model, you instead design one AI support layer that can express itself wherever the customer is:
This is exactly the problem space a customer support OS like Gleap is built for: one multichannel inbox, one knowledge base, one set of automations, with AI augmenting both customers and agents.
Most support dashboards are still tuned to operational metrics:
Agentic journeys force a different question set:
To answer these, you need journey-level analytics that unify product data, support interactions, feedback, and messaging campaigns — not just a log of resolved tickets. When all of that runs through a single OS, you can analyze the complete picture instead of cobbling insights together from five tools.
Recent customer support trend reports keep landing on the same theme: CX leaders who are seeing real upside think in terms of AI-augmented teams, not AI-versus-humans.
In practice, that looks like:
This is less about “replacing” support than resegmenting work around the respective strengths of machines and people.
How do you go from today’s omnichannel sprawl to purposeful agentic journeys without breaking trust or burning out your teams?
Below is a pragmatic rollout sequence that reflects current CX trends, AI maturity, and the realities of SaaS organizations.
Before you switch on agents, simplify the playground they operate in.
Concretely:
Gleap’s positioning as a Customer Support & Feedback OS — combining bug reports, in-app chat, surveys, feature requests, and a public roadmap — is intentionally aligned with this first step: one backbone rather than a patchwork of point tools.
Rather than aiming for a single omni-competent “super agent” on day one, start with tightly scoped, outcome-oriented journeys where the risk is low and the value is clear. For example:
Each of these can be configured largely via no-code workflows and targeting rules inside a platform like Gleap — defining triggers (events, segments, behaviors), channels (in-app, email, chat), and handoff conditions.
Guardrails are where many enterprises stumble. Without them, an enthusiastic AI rollout can create more brand risk than value.
Design guardrails along four dimensions:
Using a unified platform helps here: you can manage agent permissions and flows centrally instead of hard-coding them separately in every channel tool.
Agentic journeys live or die based on what you measure.
In addition to traditional support metrics, set up a measurement framework that spans:
Because Gleap combines multichannel messaging, surveys, bug reports, and feature requests, you can gather this feedback in the same place where agents operate — sending a quick CSAT after an AI-only interaction, logging frustration in bug reports, and tying all of it back to specific journeys.
One of the least-discussed benefits of agentic journeys is that they generate extraordinarily rich product insight — if you capture it.
Examples of what an integrated OS can surface:
With Gleap, support tickets, bug reports, and feature requests can all be linked and surfaced in a public or internal roadmap. That creates a direct line from what agents are seeing to how product decisions are made, turning agentic journeys into a continuous improvement engine rather than a static automation layer.
Beyond architecture and tooling, the quality of agentic journeys depends on a set of design choices that directly affect customer trust.
Current CX research emphasizes that loyalty is increasingly tied to feeling understood, not to discounts or clever tech. Agentic experiences should therefore be designed backwards from the human reality of your users:
Gleap’s survey and feedback capabilities are crucial here: you can continuously gather micro-feedback on specific flows and let that shape how agents behave, rather than guessing from afar.
Proactive outreach is powerful, but over-automation quickly becomes noise or even feels invasive. Anchor proactive agent behaviors on clear customer value signals:
With Gleap’s workflow automation, you can define these triggers as events and let agents step in via the most appropriate channel — in-app nudge first, email follow-up later, and only then a WhatsApp or SMS for critical issues.
Trust erodes fast when customers feel “tricked” into talking to a bot.
Practical guidelines:
Because Gleap’s multichannel inbox and workflow engine are unified, you can consistently enforce these preferences across all touchpoints rather than trying to sync them across separate tools.
Journey-level analytics shouldn’t just celebrate conversion lifts; they should also be used as an early warning system.
Examples:
Here, Gleap’s combination of conversation data, survey results, and product feedback in one system makes it feasible to detect these patterns without a data-science team.
The strategic challenge of the next few years isn’t “should we adopt AI agents?” The market is already answering that question. The real challenge is: can we deploy agentic support in a way that connects channels, data, and teams — without losing control of the experience?
Gleap’s architecture is intentionally aligned with that need. It brings together:
On top of that foundation, SaaS teams can progressively roll out agentic journeys that are:
Looking ahead, the most sophisticated SaaS organizations won’t think of agentic AI as a layer on top of support at all. They’ll think of it as a horizontal capability that threads through product, CX, and operations:
In that world, your choice of platform matters. A fragmented stack will lock agentic journeys into channel silos and shallow metrics. A unified OS for support and feedback — like Gleap — gives you the leverage to make AI structural to the way you run customer experience, rather than just another bot on the website.
For SaaS founders, product leaders, and heads of CX under pressure to “do more with less,” the imperative is clear: don’t wait for agentic journeys to arrive fully formed from vendors or consultants. Start designing them yourself — with clear guardrails, rich analytics, and a backbone that connects support, feedback, and product decisions into a single, intelligent system.
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