8 min read | New research on AI readiness and customer journey maturity
AI Doesn’t Fix Broken Operations
Where does AI belong in the customer journey?
It’s a fair question. Customer Success, Professional Services, Support, and Account Management teams are all under pressure to deliver more value after the sale, create a more consistent customer experience, and scale without increasing headcount at the same rate.
But I’ve found a more useful question sits underneath the AI conversation:
What needs to be true before AI becomes an accelerator?
To explore that question, I went back to the data. More than 200 organizations have completed ESG’s Customer Success Operations Maturity Assessment, giving us a view into how operational practices, measurement capabilities, and AI adoption show up together.
A clear pattern emerged.
The organizations getting the most value from AI had already done the foundational work. They understood their customer journey, documented it, aligned teams around it, and built mechanisms to learn from the people closest to the customer.
In these organizations, technology wasn’t the starting point.
Technology was the accelerator.
Across the organizations in this assessment, we kept seeing variations of the same pattern. In our work with Customer Success, Professional Services, Onboarding, and Support teams, we often describe it as:
Process → Methodology → Asset → Technology
First, define how work should happen. Then, establish a repeatable methodology. Next, codify that methodology into assets that can be reused, measured, refined, and scaled.
Only then does technology become a meaningful accelerator.
Organizations often want to start with technology because it feels like the fastest path to improvement. What we see in practice is that technology creates the most value when the underlying operating model is already defined.
The findings that follow don’t prove that sequence, but they are remarkably consistent with it.
Start With the Customer Journey
A customer journey map creates alignment around how customers move from purchase to value, where they struggle, and which moments matter most.
That foundation appears to matter when AI enters the picture.
Organizations that documented customer journeys for two or more customer segments were nearly three times as likely (2.9x) to use advanced automation and AI-driven workflows to scale engagement.¹
The relationship makes intuitive sense. Automation requires clarity. A single customer journey rarely reflects how different customers actually realize value. Teams that have taken the time to define distinct paths for different customer segments create the specificity that automation depends on.
How the journey is built matters just as much as whether it exists.
Organizations that created journey maps collaboratively with other business units and customer-facing teams were twice as likely (2.0x) to consistently connect leading indicators, such as adoption and usage, with lagging indicators, such as renewal and retention.²
When teams build a journey together, they often create shared definitions along the way. Sales, services, product, customer success, and finance begin working from a common understanding of what customer progress looks like and which signals matter most. That alignment makes it easier to connect activities to outcomes across the customer lifecycle.
That tracks in the real world. One ESG client, a digital health software company serving thousands of small practices, brought its sales, onboarding, and training teams together to map the customer journey step by step. The exercise revealed that customers were disengaging much earlier than leadership realized. Once that leading indicator became visible, the team redesigned onboarding and significantly reduced the time it took customers to reach value.ᵃ
The People Closest to the Work Usually Know First
One of the biggest mistakes organizations make is treating customer journey mapping as a workshop rather than an operating discipline.
The journey should be tested with the employees responsible for delivering it and the customers experiencing it.
When organizations validate their assumptions, they uncover operational friction, resource constraints, and opportunities for improvement that leadership teams often can’t see on their own.
The data support that view.
Organizations that validated journey maps with employees and customers were more than twice as likely (2.2x) to have formal processes for capturing frontline gaps and needs, including opportunities for AI enablement.³
That finding resonates with what I’ve seen in practice. Once organizations establish a habit of listening to the people doing the work, they gain a steady source of insight into where processes break down, where customers struggle, and where technology could create leverage.
The same digital health software company used interviews, pilot programs, and structured feedback loops to redesign its onboarding experience. The effort created significant productivity improvements while reducing the staffing requirements needed to support onboarding.ᵃ
The customer journey also becomes a tool for designing work.
Organizations that use customer journey insights to inform engagement models and role ownership are twice as likely (2.1x) to accurately determine team capacity.⁴
This matters because productivity conversations often begin with technology when they should begin with visibility.
Before we can claim that AI saves time, we need to understand how time is currently being spent. Clear ownership and defined customer moments make that work visible enough to measure.
This Is Where AI Starts to Earn Its Place
Organizations often approach AI as a starting point.
The data suggest it’s more often the result of operational maturity than the cause of it.
Once teams understand their customer journey, align stakeholders, clarify ownership, and measure work consistently, AI has something valuable to act on. Without those foundations, AI tends to accelerate existing inconsistencies rather than solve them.
That pattern appears throughout the assessment results.
Organizations using advanced automation and AI-driven workflows were three times more likely (3.1x) to measure the impact of their operations function on Net Revenue Retention (NRR).⁵
That’s an important distinction.
Many teams believe they influence retention but struggle to demonstrate how. Automated and AI-enabled workflows create data trails that make activities, interventions, and outcomes easier to measure and connect.
HPE Networking, formerly Aruba Networks, provides a good example. By creating a digital-first engagement model supported by automation, health scores, and defined operational metrics, the team could connect it to results: NRR increased by 40 percent and churn improved by 20 points demonstrating the real and tangible financial outcomes available for organizations that connect customer success activities to measurable business outcomes.ᵇ
The strongest relationship in the assessment involved signal detection.
Organizations whose digital and AI-enabled engagement metrics actively informed engagement strategy were seven times more likely (7.0x) to correlate leading and lagging indicators with a high degree of predictability.⁶
Put simply, the teams that use operational data to change decisions become much better at identifying outcomes before those outcomes occur.
That value became tangible for a private equity-backed identity security company that implemented a structured engagement and risk-identification program. Together, we identified that 42 percent of annual contract value was at risk and surfaced meaningful revenue exposure much earlier than the company’s existing renewal-focused processes.ᶜ
All of this reinforces an important point.
AI is exceptionally useful for surfacing patterns, analyzing large volumes of information, and reducing administrative work.
It is not a substitute for judgment because customers build relationships with people, not software. The responsibility for interpreting signals, making decisions, and creating value still belongs to the humans using the system.
Readiness Is a Discipline
The finding I found most interesting had less to do with AI itself.
Only about one in eight organizations reported having a documented process for determining readiness for new technology. Yet, organizations with a defined readiness process were nearly five times more likely (4.9x) to measure the impact of enablement efforts, including AI adoption and usage.⁷
That isn’t surprising.
Organizations that define success before implementation are usually better positioned to determine whether a technology actually delivered value afterward.
In other words, readiness isn’t a checkpoint.
It’s a discipline.
The organizations that approach technology intentionally tend to approach measurement the same way. That creates a feedback loop that improves decision-making long after the implementation is complete.
Taken together, these findings point to a broader lesson. The organizations getting the most value from AI aren’t necessarily the ones adopting technology fastest. They’re the ones building the operational foundations that allow technology to be effective.
The assessment data don’t prove causation, but they do reveal a consistent pattern. Organizations that appear most successful with technology and AI are also the organizations that invest in understanding their customer journey, aligning teams around shared definitions, measuring work consistently, and approaching technology adoption deliberately.
That raises a few important questions for any organization evaluating its own readiness.
Questions Worth Asking
If you’re evaluating your own AI readiness, a few questions are worth discussing:
- Have we documented the customer journey for our meaningful customer segments, or are we still managing to an average customer that doesn’t really exist?
- Did we build that journey with the teams that influence the customer experience?
- Do we agree on which leading indicators predict desired outcomes?
- Have the people doing the work validated our assumptions?
- Can we accurately explain how time and capacity are being spent today?
- Before adopting a new AI capability, have we defined what success looks like and how we’ll measure it?
The answers to those questions may tell you more about your operational approach and AI strategy than the technology itself.
Where Does Your Organization Stand?
Every finding in this analysis comes from organizations willing to evaluate their operations honestly.
If you’re curious how your organization compares, the Customer Success Operations Maturity Assessment evaluates fifteen operational capabilities across three stages: Build, Operationalize, and Transform.
The assessment helps teams establish a baseline, identify strengths and gaps, and prioritize the investments that are most likely to improve performance over time.
Many ESG clients have used the assessment as a starting point for operational transformation. Not to justify technology purchases, but to understand where foundational improvements should come first.
Take the Customer Success Operations Maturity Assessment
Notes and sources
Source: ESG Customer Success Operations Maturity Assessment, responses from over 200 organizations. Each comparison includes only organizations that answered both questions. “More likely” shows how strongly the presence of one capability was associated with the presence of another. All seven relationships are statistically significant (Fisher’s exact test, p < .05) and remain significant after correcting for multiple comparisons (Benjamini-Hochberg, q < .05). Findings describe association, not causation.
- Documented customer journey for two or more segments, and use of advanced automation and AI-driven workflows to scale CS motions: 52% (35 of 67) vs. 18% (16 of 89).
- Journey maps created collaboratively with other business units or customer-facing teams, and consistent correlation of leading and lagging indicators: 31% (25 of 80) vs. 15% (13 of 85).
- Journey maps tested and validated with the CS organization or customers, and a formal process for capturing front-line gaps and needs, including AI enablement: 50% (29 of 58) vs. 22% (22 of 98).
- Customer journey informs the engagement model or RACI for critical moments, and the ability to accurately determine CSM and CS staff capacity: 64% (37 of 58) vs. 31% (30 of 98).
- Use of advanced automation and AI-driven workflows, and the ability to measure the impact of CS Operations on net revenue retention: 35% (18 of 51) vs. 11% (12 of 105).
- Digital CS and AI-enabled operational metrics inform engagement strategy, and the ability to correlate leading and lagging indicators with a high degree of predictability using analytics or AI models: 48% (26 of 54) vs. 7% (7 of 102).
- Documented process for determining readiness for new technology, including AI, and measuring the impact of CS enablement, including AI adoption and usage: 68% (13 of 19) vs. 14% (19 of 137). Overall, 19 of 156 organizations (12%) reported a documented readiness process.
Client examples come from ESG’s published case studies. They are reported by ESG and its clients and are separate from the assessment analysis above.
- Digital health software company: Transforming Onboarding for Enhanced Efficiency, Improved Adoption, and Increased Retention and Driving Exit Readiness for a PE-Owned Tech Company. Onboarding time and productivity figures are from a four-month pilot; the productivity gain is an estimate.
- Aruba, a Hewlett Packard Enterprise company: 10X CS Maturity Improvement Leads to 40% Increase in Net Retention for Aruba.
- Identity security company: Improving Retention and Enterprise Value Through a Scalable Customer Success System.