AI and CRM: Evolution or Revolution?

AI and the Evolution of CRM

The traditional relational database CRM - for most asset managers - was historically the only way to describe client relationships and foster collaboration between sales and marketing.

However, the rise and rise of Large Language Models (LLM) for Generative AI is disrupting the very way sales teams interact with their clients and by extension how they think about CRM and client data.

From Analogue to Digital?

Relational databases provide a structured way to store and retrieve information, ensuring integrity and consistency across mastered data entities. Often great for reference and transaction data, the challenge comes in handling dynamic, unstructured data – from lead lists, emails through to meeting notes and client sentiment.

Generative AI is purported to bring a new level of flexibility and intelligence to these corners of data management. The theory goes that by processing vast amounts of unstructured data, teams can identify patterns and generate insights that were previously unattainable in traditional CRM systems. For asset managers, this means more comprehensive datasets, leading to better decision-making and a competitive edge in the market.

Transforming Day-to-Day CRM

The opportunity to side step manual data entry and rapidly increase the value of their proprietary client data is an enticing prospect for managers seeking to gain an advantage or realise cost efficiencies. Implemented and architected correctly, it is easy to see how material time savings on administrative tasks could translate to improved client service, deeper client relationships or leaner operating models.

‘Prepare me a 30 minute agenda for my next client meeting, referencing the last 3 months sales activity, marketing engagement and their last 90 day trading activity’

‘Transcribe the meeting notes from my next client meeting, creating an action plan to reduce redemption risk and translate these into Japanese for my wider account team’

With these emerging capabilities, it is hard to see the CRM user experience not evolving from the typical data entry and reporting dashboards we see today. As questions can become less explicit and answers can become more interpretive, the view out of the ‘sales cockpit’ is likely to dramatically change.

These developments also have the potential to drive a true 360 view of client relationships and market dynamics at a corporate level – accessible to a wider range of stakeholders, from executive management and strategy through to finance, technology and data science teams.

The Imperative of Clean Data

So far, so good, but for a truly AI-powered CRM to deliver value, the quality of foundational data to feed the models will be critical. Clean, accurate, and well-organised data will still form the foundation of CRM processes and tools.

By way of example, the two use cases or ‘prompts’ explored earlier would yield slightly less useful results if client names were duplicated and ambiguous, client flow data was stale and mis-attributed or missing details on the client jurisdiction, channel and type meant that inappropriate products were recommended in the action plan.

Failing to solve data quality fundamentals can lead to faulty insights and incorrect decisions that ultimately turn busy people away from adopting new technology. Complicating this, increasing use of Generative AI brings with it exponentially increasing data volumes. Getting the governance right for each data domain and determining the right intersection points for different data sets will be key in building sales enablement capabilities that teams actively adopt and engage with.

Addressing the Skills Gap

Flipping this around to consider the client experience, its clear clients are being bombarded with more and more AI generated content – from RFP responses through to investor commentary and more. As such, AI-powered CRM in asset management must be viewed as a mechanism to supplement and personalise client interactions, rather than replacing them.

The transition to AI-driven CRMs necessitates a shift in skill set among sales, marketing and client service professionals. Traditionally learned sales techniques must now be augmented with at a least a high-level understanding of AI tools and their limitations. Training programs and phased implementation of AI will be critical, but it is clear the profile of a sales executive in five or ten year’s time will look dramatically different to five or ten years ago.

So What?

1. GenAI presents a huge opportunity for asset managers to reduce sales and marketing administration costs and master their unstructured data to deepen client relationships
2. It’s not fully clear how sales executives and marketeers will engage with AI in the future, but the CRM user experience will be almost unrecognisably different from the traditional data entry and reporting interfaces we see today
3. Human input will be critical as firms try to appeal to clients and prospects who are bombarded with AI generated content, but doing so without the right training will present critical risks to firms
4. To unlock the potential of AI, firms need to build and connect high-quality, well-governed foundational datasets that can force precision and consistency into AI-enabled client and sales experiences

Aiviq connects asset managers with 300+ sources of AUM, Flow and Revenue data across global markets to build foundational client datasets that increase net sales, deliver regulatory oversight and automate financial reporting.

Our Integration Hub allows the world’s leading global asset managers to exploit emerging Generative AI technologies iteratively and securely.

If you’re interested in finding out more about how Aiviq supports the world’s leading asset managers with financial reporting or the 160+ other enterprise use cases supported by the platform, get in touch with our team today at aiviq.com/getstarted

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