Table of Contents
In brief
As we have outlined in previous white papers, AI-driven client experience (CX) is set to become the next ‘table stake’ for asset managers. This white paper highlights the importance of clean, reliable CX data as the foundation for effective AI applications. The authors introduce a five-step CX Data Maturity Framework, guiding firms through auditing their data, defining AI strategies, implementing AI processes, securing governance, and empowering staff. Real-world tools and case studies from Artefact and Accomplish demonstrate how firms can take actionable steps toward AI readiness. Ultimately, the piece encourages firms to choose whether they want to keep up with this industry shift – or lead the industry – with their CX capabilities.
If you’re ready to get started, conduct your CX Data Maturity Audit here. Together, we can turn AI-driven CX into your competitive edge.
AI-driven CX is set to become the next ‘table stake’
AI-driven CX is transforming how asset managers engage with clients. Unlike traditional CX, which relies on costly manual processes, AI-driven CX is scalable, proactive, and more efficient, offering tailored engagement for all clients at lower costs. It improves client engagement and lets asset managers deliver superior, tailored experiences at a lower cost.
Historically, superior CX was a differentiation strategy due to the expense and complexity of high-touch service. However, as firms compete in a crowded market, AI-driven CX is becoming the next ‘table stake’. This is because AI enables firms to deliver consistently excellent CX, making high-touch service accessible at scale. As client expectations continue to rise, falling short of this standard could put firms at a disadvantage, making AI-driven CX not just an option but a necessity for maintaining competitiveness.
Asset managers that adopt AI-driven CX will benefit from improved client engagement, higher conversion rates, streamlined operations, and stronger client relationships. Those that do not, risk the opposite: inefficiencies, declining engagement, and client churn as investors seek more seamless, data-driven experiences. But to harness the power of AI-driven CX, firms must first lay a strong foundation: clean, reliable data. Without it, even the most advanced AI will fail to deliver.
For senior leaders, AI-driven CX offers measurable ROI. High data maturity can improve conversion rates in prospecting campaigns, reduce cost-to-serve per client through automation, and increase client retention (measured in months of client tenure). Firms that operationalize AI in their client journeys can also unlock new revenue through personalized cross-sell strategies and reduce regulatory overhead by automating record-keeping and reporting.
For a deep dive into how AI-driven CX impacts explicitly each stage of the asset management client journey – from digital engagement to relationship management – and to understand the dollarizable benefits tied to each step, see our accompanying white paper: The New Dawn – AI-driven CX is set to become the next ‘table stake’. It offers an in-depth use case, specific examples across the pre- and post-sale experience, and a value vs. implementation ease matrix.
Clean CX data will be vital
What is CX data? Accomplish runs the asset management CX Benchmark that lets firms see where their CX is helping – or hurting – the bottom line. Achieving this requires data from across the end-to-end client journey, broken down into the pre- and post-sale experiences.
Pre- and post-sale CX data – pre-sale CX data reveals how well your sales funnel converts digital interactions into in-person engagement and sales, while post-sale data measures service effectiveness, client retention, and cross-selling. In both cases, clean data enables precise decision-making, whereas dirty data misleads and erodes performance.
Gifts of time and transfers of money – you can see from these summaries that CX data is measured either in time (seconds on your website, hours at your events, and days in onboarding) or money, where the dollarizable CX metrics are sales conversion, client tenure, and products-per-client.
Why CX data is vital – in the intensely competitive market for asset management, if your CX underperforms at engaging clients, they will give their time to your competitors … and then their money.
Clean data vs dirty data – clean data lets you learn from accurate patterns of behavior, make reliable predictions of the next best action for your client, and move through decision-making cycles faster and more successfully. Meanwhile, dirty data can mislead you, damage client engagement, and waste resources. As a result, studies have shown that firms with strong data maturity are far more agile, while poor data management costs companies billions in lost revenue.
CX data quality supports compliance – beyond operational inefficiencies, poor-quality CX data can lead to serious compliance risks. In asset management, regulations such as MiFID II, the SEC’s marketing rule, and ESG disclosure standards increasingly require firms to maintain auditable, accurate records of client interactions, suitability assessments, and communications. High data maturity reduces the likelihood of regulatory breaches and associated fines, while also making it easier to comply with emerging AI governance frameworks.
“Garbage in, garbage out” – AI will magnify these risks for firms with dirty data. It will amplify any inaccuracies or biases, leading to faulty predictions, incorrect decisions, and reputational costs. Real-world failures, like Unity Technologies’ data issues that led to a $110 million loss, highlight how flawed data can derail AI initiatives. Unlike public AI failures that widespread scrutiny identify quickly, internal AI outputs may go unchecked for longer without proper governance, compounding their negative impact.
A CX data maturity framework will be essential – in an AI-powered world, only firms with a data maturity framework will succeed. And in a market where AI-driven CX becomes the next table stake, those with a CX Data Maturity Framework will leverage CX as a source of competitive advantage. Those who neglect data quality will watch their AI investments – and competitive edge – fall short of expectations. How can asset managers build a data framework that ensures AI-driven CX delivers its promise? That’s what we’ll explore next.
A CX Data Maturity Framework you can adapt to meet your needs
We have designed the steps to help you increase your data maturity decisively. So before diving into the five steps, here’s a simplified view of what CX data maturity might look like across three levels. Use it to assess your current position and start to frame actions you may need to take.
Step 1: Conduct a CX data maturity audit – to keep things practical and actionable, your first task should be to assess the status quo. Across your end-to-end client experience, find the dirty data (e.g. inconsistencies and inaccuracies) as well as the gaps.
- Pre-sale CX data (aka, your sales funnel) covers digital engagement (email, social, and web), in-person engagement (events, buy ratings, and RFPs), and sales conversion rates.
- Post-sale CX data covers key service delivery metrics (onboarding, client reporting, and client portal engagement) and relationship management (meeting volumes, client retention, and cross-selling).
Step 2: Define your strategy for AI-driven CX – your next task is to set a clear goal for what you want to achieve from AI-driven CX. Informed by your audit of the status quo, what would be an appropriate objective from your current position?
- Be in the pack? Maybe your audit identified big gaps in your data, making a feasible goal to be ‘in the pack’ for this new table stake.
- Outperform? Or perhaps your audit gave you confidence that your organization already has a mature data framework, and you see an opportunity to outperform and capitalize on CX as a source of competitive advantage.
Step 3: AI process improvement – now that you have set your strategy, it’s time to map the AI processes that will bring it to life. Identify where incorporating AI will make your processes quicker, safer, and cheaper.
- Where to start – agents are new, so list the processes you want to automate in order of complexity and business risk and start with the least complex and lowest risk use cases.
- Example 1 – maybe automating the movement of a dataset will save you time or reduce the risk of error. This simple task will be easy to automate through an agent. Start your agent-building journey here.
- Example 2 – or perhaps you want to automate the generation of insights for which you currently rely on humans. This will be a complex task for which you will need to programme all the factors and potential scenarios the human considers before forming their conclusion. Don’t start here.
- Source or build the agents you need and test them to breaking point – the input data as well as the agents. Only turn them live after you have tried but failed to break them.
- Mitigate bias with strategies like bias audits on training data and AI models, continuous debiasing techniques such as re-weighting underrepresented data, and ensuring diverse and representative datasets. Ensure someone is accountable for and can explain any AI model. Maintain human oversight in AI decision-making to review outputs and intervene when necessary. Use multiple data sources to validate predictions and minimize the impact of biased data from any single source. These strategies help ensure AI-driven CX delivers fair and accurate recommendations that align with client needs and regulatory requirements.
Step 4: Put guardrails around your gold
With AI now driving CX, your data isn’t just an asset – it’s your competitive edge. But to keep that edge sharp, you need governance systems that protect its integrity and ensure it’s working for you, not against you.
Start with a living data dictionary – a central, evolving reference that defines what ‘clean’ means in your context: how current, how complete, and how consistent your client data needs to be. Include trusted data sources, the location of master copies, and assign ownership.
Next, make trust your default. Validate third-party data before it enters your system, using tools like blockchain or dataset triangulation. Then, clean relentlessly: deduplicate entries, flag anomalies, and unify scattered data points into single client views.
In asset management, where fiduciary duty is paramount, AI-driven decisions must be explainable, auditable, and free from embedded bias. Clean, well-governed data is essential to ensure that AI recommendations – from fund selection to risk profiling – align with client needs and meet regulatory scrutiny. Without proper controls, firms risk reputational damage or legal exposure stemming from flawed AI decisions. This makes data quality not just a technical issue, but also a core governance priority.
Monitor data quality with dashboards that update at appropriate frequencies and flag anomalies. The CX Benchmark’s embedded data quality checks are also excellent for advising you of implausible shifts in your data.
And don’t forget information security: your CX data and AI models are information assets that must sit firmly in-scope of your information security management system (ISMS). Integrating them into your standard ISMS as it evolves in response to new threats will ensure you maintain robust measures such as access controls, incident management, and regular security audits.
Finally, remember that AI needs supervision: at Accomplish, we treat it like a recent MBA graduate – mostly excellent and full of energy, but it doesn’t know everything and has never implemented its own recommendations, so it needs supervision. Build in human checkpoints and enforce accountability. Strong governance not only mitigates bias but also makes your AI investments pay off faster.
Step 5: Equip your people to lead with AI
But even the best AI can’t fix what people don’t understand. So, none of this will work without one final ingredient: people who know how to run it. As we say at Artefact, AI is about people.
That’s why your colleagues – especially those managing data – need to be AI-literate, clear on their responsibilities as data owners, and quality-obsessed.
Begin by training your staff in the essentials of data management for AI: how small errors ripple into big mistakes, and why keeping a ‘human in the loop’ is non-negotiable.
Empower them to spot and avoid common human errors – like labelling mistakes or assumptions baked into data definitions. Clarify who is accountable for the quality of each dataset. Make it cultural, not just procedural.
When your people understand how AI works – and how it can fail – they’ll help prevent problems and accelerate innovation. That’s what it means to be AI-ready.
In short, with the right people, processes, and tools, your AI-driven CX won’t just keep pace with industry standards – it’ll set them.
Real-world tools and experiences you can leverage
If this sounds like a tall order, it doesn’t have to be. Artefact and Accomplish offer tested tools and experience to help you move fast and with confidence.
Accomplish’s CX Data Maturity Audit – to overcome this hurdle, the tool is a structured worksheet with predefined data standards to discovering if your firm is ‘data ready’ or has some data issues / gaps’ to solve – 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.
- You can pull it off the shelf and get started immediately.
- Then, after all your efforts to adopt AI-driven CX, you will have comparable data, so you can find out how your CX stacks up against industry averages.
Accomplish’s CX Benchmark doesn’t just provide absolute scores – it enables peer comparison across key metrics such as client engagement (digital and in-person), sales conversion, onboarding efficiency, client service fulfilment, and cross-sell effectiveness. Participating firms can see how they stack up against top-quartile performers and identify the specific CX areas where improvements will yield the most competitive gain.
Case study 1: a large global investment firm that was ‘data ready’ – ran an 8-week preparatory phase before its first submission to the CX Benchmark. Coordinated by a business manager, they assigned the sub-sections to different teams. This is an uncontroversial task: Marketing should own digital engagement and events; Onboarding should own onboarding stats, etc. Not all metrics applied, for example, they didn’t publish a podcast, putting two metrics out of scope immediately. Across the Americas, EMEA, and Apac, they confirmed each data point’s availability (or not), rated their confidence in it (HML), and developed plans quickly to fix data gaps and improve confidence. After 8 weeks, they submitted 27 metrics across all three regions and now dedicate their efforts to developing strategies to outperform.
Case study 2: a medium-sized asset manager with data issues and gaps – conducted their CX Data Readiness Check and found that while their stakeholders were onboard, the data just wasn’t either available or structured in a way they could exploit easily. Recognizing the risk of its position and armed with the taxonomy’s definitions as an aiming point, this firm then targeted its efforts towards strengthening its data maturity. It then submitted the results to the CX Benchmark for peace of mind that it passed the Benchmark’s quality checks and to unearth how its CX compared against other firms. This example shows that strategic alignment alone isn’t enough – without structured, available data, firms may find their readiness ambitions blocked by practical limitations. Identifying that gap early was the turning point in this firm’s journey.
Artefact’s experience in data and AI strategy with data issues and gaps – Artefact has over 10 years of experience in helping firms with their data and AI strategies. Over that time, we have learned the importance of choosing to be in the pack or to outperform. This is because it will determine where you should deploy AI and what you want to achieve with it. If you want to be in the pack, deploy agents to automate your data capture, cleaning, and maintenance. If you aim to outperform, deploy machine learning and natural language processing to create a world-class AI-driven CX.
Whether you aim to be in the pack or to outperform, your data maturity will determine how far AI can take you. The question is not if AI-driven CX will reshape your client relationships – but whether you’ll be leading that change or scrambling to catch up.
Capitalize on Artefact’s and Accomplish’s strategic relationship
To conclude, this paper has raised key points:
- If you share our belief that AI-driven CX is set to become the next ‘table stake’ for asset managers, then every firm will need an approach to its CX data.
- The five steps in our CX Data Maturity Framework won’t just get you AI-ready – they’ll set you up to win.
- With clean data, smart agents, and trained teams, you’ll be ready to outperform, not just keep up.
Adam Davis is a partner with Artefact – a world-leading data and AI consulting partner.
Adam Grainger is the Head of Insights at Accomplish – a specialist provider of CX services to the asset management industry, including the CX Benchmark.
By leveraging Artefact’s and Accomplish’s capabilities, firms can turn these insights into action and establish themselves as leaders in a crowded market.
If you’re ready to get started, conduct your CX Data Maturity Audit here. Together, we can turn AI-driven CX into your competitive edge.
This work is part of a series showcasing the uniqueness of our strategic relationship. We hope you found it useful; here is the complete series.
- The Differentiation Challenge – five winning strategies for standing out in the crowded asset management market.
- The New Dawn – why AI-driven CX is set to become the next ‘table stake’ and what you can do about it.
- The Vital Piece – a 5-step CX Data Maturity Framework for capitalizing on AI-driven CX.
