Future-ready financial services: reduce complexity with smarter growth

Financial services organisations don't necessarily have a technology shortage. They have a continuity challenge.

As customer journeys move across marketing, sales, onboarding and service, and between specialist platforms, customer context can be lost at the handoffs. That's where manual work increases, visibility decreases and revenue opportunities can start to leak.

This was the central theme of Future-ready financial services: Less complexity, smarter growth, co-hosted by HubSpot and Lupo Digital.

Lupo Digital's Glenn Miller and Michael Wolf were joined by John Kilkenny, Head of New Business Development at PetSure, and Alex Holzer, Communications Manager at Collins Street Asset Management, who shared two very different real-world examples of solving these challenges. HubSpot Senior Solutions Engineer Kye Bessant then demonstrated how connected data, automation and AI can work together in practice.

Watch the full webinar below, or continue reading for five practical lessons financial services leaders can take from the discussion.

Watch the full discussion featuring HubSpot, Lupo Digital, PetSure and Collins Street Asset Management, including real-world financial services use cases and a demonstration of HubSpot's latest data, automation and AI capabilities.

1. Complexity is an operational problem, not simply a technology problem

Financial services organisations often rely on specialist systems for policy management, investments, advice, lending and other critical functions. Those systems aren't necessarily the problem.

The problem starts when people have to become the integration layer between them — exporting, re-entering, reconciling, chasing and manually recreating customer context.

One practical exercise Glenn recommended during the webinar was to stop starting with a list of systems and instead map the handoffs: Where does ownership change? Where does the system change? Where does somebody manually recreate the context?

Those gaps can expose where customer experience, productivity and revenue are being lost.

2. Your data model determines what your people can do

CRM architecture can sound technical, but its consequences are highly practical.

Collins Street Asset Management found that a conventional CRM structure didn't adequately represent the complexity of its investor relationships and funds. Investment values changed over time, investors topped up or withdrew funds, legal structures changed, and that history needed to remain available and traceable.

Working with Lupo Digital, Collins Street introduced a custom Funds object in HubSpot, allowing the CRM architecture to better represent how the business actually operates and creating a more coherent view of the investor journey.

3. Connected customer context becomes increasingly important as organisations scale

PetSure demonstrated the same principle at a very different scale.

Working across more than 20 distribution brands and thousands of veterinary partners creates multiple customer journeys, systems and levels of marketing maturity. One particular gap occurred between partner marketing and PetSure's quote-and-buy process: potential customers would begin a quote but some abandoned leads weren't flowing effectively back into partner CRM systems for continued email remarketing.

PetSure connected its quote tools directly into HubSpot via API, creating a centralised source of live abandoned-lead data. This allowed PetSure to support partner remarketing through email, testing and targeting — with some partners subsequently extending the approach into SMS and outbound calling.

The lesson is broader than the technology itself: specialist platforms can remain in place while customer context and activity are connected around them.

4. AI readiness starts with the foundation

AI doesn't remove weaknesses in data, processes and governance. It can magnify them.

If customer information is scattered, incomplete or outdated, AI has less reliable context to work from. If ownership and processes aren't defined, an AI agent may identify what should happen next without the organisation having a reliable way of acting on it.

The organisations best positioned to operationalise AI therefore aren't necessarily those adopting the most AI tools. They are those building the strongest foundations: trusted data, connected customer context, defined processes, reliable knowledge, clear permissions and appropriate human oversight.

In other words, AI readiness starts with data, context and processes. Better AI starts with a better foundation.

During the live demonstration, HubSpot showed how those foundations can translate into practical capability — from data enrichment and lead routing to AI-assisted customer context, data quality and agents capable of acting on customer intent.

5. Future-ready doesn't mean implementing everything today

Becoming future-ready doesn't necessarily mean adding more technology. It means having fewer gaps.

Rather than attempting a wholesale transformation, start with one meaningful customer or revenue journey — an enquiry becoming a client, a referral reaching an adviser, a service interaction leading to renewal, or an investor enquiry becoming an ongoing relationship.

Map that journey and ask three questions:

Where does ownership change?
Where does the system change?
Where does somebody manually recreate the context?

Those are often the best places to start.

When customer context follows the relationship, people spend less time stitching processes together, leaders gain visibility earlier, automation becomes more useful, governance becomes easier and AI has a stronger foundation.

 The question for Financial Services leaders therefore isn't simply:

“What technology should we implement next?”

A better starting question may be:

“Where is complexity currently preventing our people, customers and business from performing better?”

That is often where the strongest transformation opportunities begin.