Table of Contents
In brief
In our white paper on differentiation strategies for asset managers in 2025, we predicted that AI-driven client experience (CX) is set to become the next ‘table stake’.
In this article, we define AI-driven CX, show how the deployment of AI will transform client interactions and using the institutional client journey as a case study, identify where to start – the quick wins and areas of longer term value.
Whether you’re enhancing relationship management or future-proofing your CX strategy, this piece offers actionable insights backed by industry expertise.
The opportunities of AI are vast, so contact us if you’d like any advice on where to start.
Video to follow … work in progress.
What is AI-driven client experience?
To begin, CX is an effect that a company causes in its clients that it can observe in what they say (feedback) and do (behavior). Feedback is subjective, while behavior is objective – measured in time and money. This is important because if your CX underperforms at engaging clients, they will give their time to your competitors and then their money.
AI-driven CX refers to enhancing client interactions with artificial intelligence (AI) technologies – such as machine learning (ML), natural language processing (NLP), predictive analytics, and agentic automation.
Unlike traditional CX, which relies on expensive manual processes, client tiering, and ‘white glove’ service for the highest tier, AI-driven CX is personalized, predictive, and scalable to all clients at a much lower cost.
Key components
- Personalization and predictive engagement – AI adapts dynamically to client needs, providing personalized recommendations based on behavioral and transactional data, e.g. content and communications suggestions.
- Next-best action (NBA) and AI-driven decision support – AI proactively suggests optimal client interactions, such as recommending a relationship manager contact a client and proposing the talking points.
- AI-powered client service and omnichannel integration – AI automates self-service, chatbots, and voice assistants while ensuring a seamless real-time experience, reducing human intervention for routine queries.
- Sentiment analysis and self-learning AI models – AI scans client emails, chats, and engagement patterns to detect client sentiment in real-time and give early warning of frustration, allowing firms to address concerns proactively.
- AI governance, compliance and ethics – AI-driven CX must be transparent, regulatory-compliant, and free from bias, ensuring trust and accountability.
Example in action
A robo-advisor, like Vanguard Digital Advisor, leverages AI to analyze an investor’s risk tolerance and adjust their portfolio automatically to stay aligned with their financial goals. This ensures that clients receive personalized, data-driven investment strategies with reduced manual oversight.
Why AI-driven CX is set to become the next ‘table stake’
Pre-AI, a high-touch traditional CX was a differentiation strategy: ‘white glove’ service was expensive in terms of human resources, and the customization it brought came with complexity and costs.
This limited its scalability, made it a less common strategy, and therefore enabled it to be a point of differentiation for the firms that chose it.
AI has changed this. At the same time as firms are under huge pressure to stand out, AI has made CX not just controllable, but also affordable and scalable.
As asset managers compete to show how they are mastering AI in the interests of their clients, we expect the level at which firms’ CX needs to operate before it outperforms will rise. As a result, anything less than excellent CX may count against you.
In a consolidating market, firms will seek to avoid this, setting up AI-driven CX to become the next ‘table stake’.
How AI-driven client experience will differ from traditional CX
Traditional CX relies on manual processes, tiering, and reactive engagement, whereas AI-driven CX will deliver real-time, hyper-personalized interactions using behavioral insights and predictive analytics.
While traditional CX depends on humans to handle inquiries, AI-driven CX will provide instant, 24/7 responses, resolving routine queries and escalating complex issues to relationship managers when necessary.
AI agents will enhance speed and efficiency by automating administrative tasks, optimizing workflows, and addressing client needs proactively. Unlike traditional CX, which gathers insights through periodic surveys, AI will monitor sentiment across multiple touchpoints to detect interest or dissatisfaction and recommend the next best action.
Crucially, whereas traditional approaches struggle with scalability, AI-driven CX will scale effortlessly, ensuring consistent, high-quality interactions across a broad client base. As computers learn how to minimize client queries, it is not outlandish to imagine AI making the idea of raising a query obsolete if it finds and fixes the issues before they trouble clients.
Such a frictionless CX should be the goal of AI, enhancing human connections by saving them for higher-value interactions. To quote Artefact’s motto, AI is about people. So, we hope you can see in the use case below how AI will boost the effectiveness of client-facing staff by automating administrative tasks, providing vital insights, and setting up higher-value interactions with clients.
AI-driven CX for institutional asset management
In a systematic way, the table below compares AI-driven client experience against a traditional CX across the institutional client journey.
Spanning the pre- and post-sale experiences, we show how AI will fundamentally reshape how asset managers interact with clients.
All the benefits are measurable and comparable through your scorecard in Accomplish’s CX Benchmark, ensuring you can evaluate your progress.
Key stages of behavioral conversion
Client journey | Pre-sale experience
Digital Engagement
Traditional institutional CX
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Increased conversion to your website from social and email
Increased web engagement
In-Person Engagement: Buy Ratings
Traditional institutional CX
Buy ratings – consultant relations teams keep consultant databases up to date and manage relationships to increase support for their product range.
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
In-Person Engagement: Events
Traditional institutional CX
Event intelligence and lead prioritization – clients register for and attend investment events where relationship managers aim to convert attendance into a follow-up meeting.
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Increased event attendance and turn-up rates
Increased conversion from event attendance to prospecting meetings
In-Person Engagement: RFPs
Traditional institutional CX
Manual creation of RFP content with semi-automated collation into a draft RFP response.
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Sales Conversion ($)
Traditional institutional CX
If called forward to pitch, relationship managers rely on intuition and past experiences to pitch investment solutions.
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Client journey | Post-sale experience
Onboarding ($)
Traditional institutional CX
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Client Reporting
Traditional institutional CX
Clients receive static quarterly reports with little opportunity for dynamic engagement. A client service professional responds to any investor inquiries.
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Client Portal Engagement
Traditional institutional CX
A broad range applies here: some firms do not have client portals, and others use third-party or their own proprietary models. On average, the range of insights they provide on client engagement runs from ‘nil’ to the provision of raw engagement data.
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Increased client engagement and insights into behavior patterns
Relationship Management ($)
Traditional institutional CX
Relationship managers schedule periodic meetings. Client engagement is reactive, responding mainly to client requests and issues. Client retention strategies are often informal, reactive, and triggered when clients express dissatisfaction, by which time it may already be too late.
Product recommendations are based on broad segmentation or the asset manager’s ‘focus funds’ for the year rather than specific client needs.
AI-driven institutional CX
Measurable and ‘benchmark-able’ benefits
Increased ‘discretionary’ client meeting volumes
Increased product-per-client ratio ($)
Longer client tenure ($$$)
Where to start
Following this use case, the key question for firms will be, “Where to start?”
To help you answer this, we have assessed each aspect of AI-driven CX for the value it will deliver to you and the ease with which you can implement it. The result is the AI-driven CX Quadrant.
Because every part of the client journey is high value, the key finding is that ease of implementation creates the greatest separation for firms. Specifically, it creates two categories:
- Quick wins that break down into two waves: the first converts in-person engagement at investment events into RFP automation and sales conversion, while the second wave increases the effectiveness of digital engagement (whether it is through marketing of your client portal) as well as onboarding.
- Areas of longer-term value provide similar levels of value but may take longer to implement owing to their complexity. They include consultant buy ratings, client reporting, and relationship management.
Download the white paper for a full explanation of each AI use case and our assessment of its value and ease of implementation.
In practice, firms should combine this analysis with an assessment of their relative maturity and readiness for change in each area. As a result, some might prioritize an area of longer-term value despite their difficulty if the expected return was critical or their existing level of readiness was high – for example, focusing on relationship management if client retention was an organizational goal.
Risks and mitigation strategies
As AI transforms your end-to-end client interactions, it will introduce new risks for you to manage.
Take care, therefore, to deploy AI in ways that are transparent, explainable, and free from bias. Maintaining client trust and compliance with industry regulations will be vital.
Your risks will be specific to your organization, but this general list of key risks and mitigation strategies will help you make choices and customize your responses.
Key risks and mitigation strategies
1. Client Data Privacy and Security Risks
Description
Mitigation Strategy
2. AI Bias and Fairness Issues
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Mitigation Strategy
3. Over-Reliance on Automation
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Mitigation Strategy
4. Transparency and Explainability Issues
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Mitigation Strategy
5. Regulatory and Compliance Risks
Description
Mitigation Strategy
6. Fiduciary Responsibility and AI-Governance
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Mitigation Strategy
7. Model Risk and Accuracy Concerns
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Mitigation Strategy
Capitalize on Artefact’s and Accomplish’s strategic relationship
To conclude, AI-driven client experience is no longer a future differentiator – it is fast becoming the baseline standard by which asset managers will be judged. This paper has shown how AI is reshaping the entire client journey, from digital prospecting to onboarding, reporting, and relationship management. The most successful firms will be those that prioritize implementation based not only on value, but also on organizational readiness and strategic goals.
The opportunity is substantial – but so are the risks. Firms must approach AI adoption with a clear-eyed view of governance, transparency, and the human role in delivering value.
By leveraging the complementary expertise of Artefact and Accomplish, firms can move from insight to impact – positioning themselves to lead in a market where excellence in CX is no longer optional but expected.
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.
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. Lastly, the opportunities of AI are vast, so contact us if you’d like any advice on where to start.
- 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.
