In brief
This article solves the problem of poor-quality CX data that blocks investment firms from capitalizing on AI-driven client experience (CX). It’s for senior leaders who need to modernize CX under competitive pressure. It offers a free asset management CX data audit tool to assess and improve readiness. This matters because clean data is essential to staying efficient, relevant, and client-focused in the era of AI. Readers should get the free CX Data Maturity Audit tool and start now.
AI-driven CX is no longer optional
From differentiation strategy to table stake – in 2025, AI-driven CX is no longer optional. As we examine in our recent white paper (AI-driven CX is set to become the next ‘table stake’), the arrival of widely deployable artificial intelligence (AI) is translating high-touch CX from a discretionary differentiation strategy into a mandatory requirement for operating in the market. Leading firms are already using it to personalize journeys, predict client needs, and cut servicing costs. For everyone else, it’s catch-up time.
Key differences between traditional CX and AI-driven CX – what was previously a costly and manual service reserved for top-tier clients is now affordable, automated, and scalable for every client. Static experiences have become dynamic and personalised, and reactive experiences become predictive.
Benefits of AI-driven CX for asset managers – as client expectations continue to rise, firms are competing to show clients how they are mastering AI. At the same time, they are boosting client engagement and increasing behavioral conversion rates across the pre- and post-sale experience – from digital engagement to in-person engagement and from buying to staying and buying more.
We expect the bar to get higher – as firms compete with these new capabilities, we expect to see the levels of engagement and conversion needed to outperform in the CX Benchmark rise. Firms using the CX Benchmark have already benefited from doubling their RFP success rates, cutting millions per year off their hidden costs of slow onboarding, and being able to act fast to stem client losses in response to behavioral signals in their data.
Risks of being late to the party – those who resist this development will place themselves at an information disadvantage as others develop data management techniques that fit the AI era and use them to unearth commercially actionable insights. The risks include operational inefficiencies, declining engagement, and client churn as investors seek more seamless, data-driven experiences. Laggards will risk drawing unfavourable attention to themselves, which is not where you want to be in a consolidating market.
Garbage in, garbage out
A firm’s ability to capitalize on this development rests on the quality of its input data: if it is poor quality, AI won’t help – it’ll hurt.
Need proof? In an unrelated industry, one firm – Unity Technologies – rushed at AI with poor-quality data and created a $110 million loss. So while that seems unthinkable in our regulated industry, this isn’t a corner to cut – or a risk to ignore.
Step 1: asset management CX data maturity audit
Early in your AI projects, make a clear-eyed evaluation of the quality of the raw materials you will need. Across your pre- and post-sale CX, evaluate the data’s quality, find the gaps, and develop crisp action plans.
As we have proposed separately, Step 1 to CX data maturity in the era of AI is a CX data maturity audit.
Do this to discover where you stand and where to invest, as well as to avoid two things:
- Overlooking the importance CX will play in the era of AI.
- Burning your AI budget on bad data and making decisions that alienate high-value clients.
Accomplish has already done the heavy lifting: solutions for overcoming your obstacles
“We don’t know what data we will need.” To overcome this hurdle, the tool is a structured worksheet with predefined data standards – no guesswork, no consultants needed. The standards are driven by the data dictionary that drives the CX Benchmark, as used by some of the largest and most-respected asset management brands. Developed by asset managers for asset managers – and trusted by firms like BlackRock, JPMAM, and Fidelity – it includes only the essential metrics needed to monitor the end-to-end client experience. It has been tested, proven, and strengthened with live data every quarter for the last 4 years.
A proven foundation, already in use
“We don’t know where our CX data lives or how to evaluate it.” The audit will take you through the process of finding out and help teams coordinate cross-functionally, a common blocker in CX data projects. Specifically, it defines the quality needed for each data input, so you can:
- Define your scope – pre and/or post-sale experience.
- Understand your data requirements.
- Engage stakeholders.
- Assess the availability and quality of the data you need.
- Develop action plans to resolve any issues.
“We don’t know what ‘good’ looks like.” We do, and we have a rich dataset of CX data against which you can compare your performance. Develop your CX data in this tried and tested way, and you will be able to find out too.
“The logic of auditing CX data is compelling, but how should we do it?” Download the asset management CX data template audit for free now and find out whether your CX data fits the AI era. Most teams complete the audit in under a week, and it typically highlights about 5 high-impact fixes.
