Here's a question every advisory firm should ask about every vendor in their stack: Are they making it easier for you to access, share, and own your data? Or are they making it harder?

Because there's a growing divide in WealthTech between vendors that view data sharing as a feature and vendors that view it as a threat. And the side your partners fall on will determine whether your firm can compete in the next five years.

The data moat problem

Some technology vendors have built their entire business model around keeping your data inside their walls. They'll let you see it through their interface. They'll let you run their reports on it. They might even let you export a CSV if you ask nicely.

But try to integrate it with another system? Connect it to a unified data layer? Pull it into a different analytics tool? Suddenly the API docs are incomplete, the support tickets go unanswered, and the "integration roadmap" is perpetually six months away.

This isn't accidental. It's a strategy. If your data is hard to move, you're hard to leave. The moat isn't the product. The moat is your data.

And for years, this worked. Switching costs were so high that firms stayed with mediocre partners for a decade or more. The pain of staying was less than the pain of migrating.

That calculus is changing.

The pain of staying is catching up

Here's what's different in 2026: AI requires connected data.

If your planning tool won't share data with your CRM, that was annoying before. Now it means your AI can't prepare a comprehensive client review. If your custodian won't integrate cleanly with your portfolio management system, that used to mean manual reconciliation. Now it means your AI is working with an incomplete picture.

Every vendor that holds your data hostage is a vendor that limits what AI can do for your firm. And as AI becomes the primary way firms gain efficiency and competitive advantage, that limitation isn't just annoying — it's existential.

Jessica Perez put it directly on our podcast: "If they are not sharing your data well, if they don't want to be a partner to help you elevate your business experience, you should re-evaluate that partnership."

She's right. The tolerance for partners that don't play well with others should be approaching zero.

What good partners look like

Let's make this concrete. A technology partner that fights for your data does the following:

They invest in open APIs and integrations. Not just one or two strategic partnerships — broad, well-documented APIs that let you connect their system to anything else in your stack. They view integration as a feature, not a concession.

They make data export easy. Your data is your data. A good partner lets you pull it out in standard formats, on your schedule, without jumping through hoops or paying extra.

They participate in the ecosystem. They show up to integration discussions. They build connectors to other popular platforms. They don't act like the only technology that matters is theirs.

They invest in R&D that benefits you. Are they building features that make your life better? Or features that make their competitive moat deeper? There's a difference, and you can usually tell by looking at their product roadmap.

They advocate for data standards. In an industry with no universal data schema, the best partners push for standardization. They want data to flow freely because they're confident their product is strong enough to win on merit, not lock-in.

What bad partners look like

On the other side:

They make integration difficult. Limited APIs. Poor documentation. Slow support for integration requests. Integration is always "on the roadmap" but never shipping.

They charge for data access. Your data, behind their paywall. Need an API call? That's a premium tier. Want real-time access? That's an add-on.

They build walled gardens. Their system works great — as long as you use their other products too. Cross-platform integration? They'd prefer you buy their version instead.

They move slowly on data sharing. While the rest of the industry is opening up, they're still protecting their moat. Every competitor has an open API, but they're still doing quarterly CSV exports.

They frame lock-in as a feature. "Everything in one place" sounds great until you realize it means everything in their place, on their terms, and you can't leave without losing years of data history and workflow configurations.

The model-hopping lesson

Something fascinating is happening in the broader AI world that WealthTech firms should pay attention to.

People switch AI models without fear. When Claude shipped improvements that outperformed ChatGPT for coding tasks, developers moved overnight. When Google upgraded Gemini, people tested it immediately. When a new model beats the benchmark, users are there within days.

The barrier to switching AI models is basically zero. And that's because the models compete on merit, not lock-in. Your conversation history might live in one platform, but you can start a new conversation anywhere. The data you bring to the model — your documents, your context, your questions — is yours.

Now imagine if WealthTech vendors operated the same way. Imagine if switching your CRM or your planning tool was as easy as switching your AI model. Imagine if your data moved with you, cleanly, because every vendor built on open standards and easy integrations.

We're not there yet. But the firms that demand data portability from their vendors are pushing the industry in that direction. And the firms that accept data lock-in are funding the moats that keep everyone stuck.

The price of progress is the pain of change

Yes, switching technology is hard. Migrating a CRM takes months. Moving custodial relationships is complex. Re-training a team on new tools costs time and goodwill.

But staying with a partner that won't share your data has a compounding cost. Every year you stay, your data gets more embedded. The switching cost gets higher. And the gap between your firm and firms with connected data gets wider.

The price of progress is the pain of change. The question is whether you pay it now — when the cost is manageable and the upside is massive — or later, when you're forced to change under pressure and the cost is ten times higher.

What to demand from your stack in 2026

If you're evaluating your technology partners this year, here's the checklist:

Data ownership. Is your data yours? Can you access it, export it, and move it without permission or premium fees?

Integration quality. How many platforms does this vendor integrate with? Are the integrations maintained? Do they break when someone updates an API?

API access. Is the API well-documented, publicly available, and actively developed? Or is it an afterthought?

Data sharing philosophy. Does this vendor talk about data portability? Do they advocate for open standards? Or do they frame their walled garden as a benefit?

Investment trajectory. Is this vendor investing in making your data more accessible and useful? Or are they investing in features that deepen lock-in?

The answers will tell you whether a vendor is a partner or a landlord.

Connect, don't replace

At Milemarker, our philosophy is simple: connect your existing systems, don't replace them. Every vendor in your stack probably does their core job well. The planning tool is good at planning. The CRM is good at contact management. The custodian does custodial things.

The problem isn't the individual tools. The problem is that they don't share data well. Our Data Engine connects 130+ WealthTech platforms into a single unified layer — so you get the benefit of every tool without the fragmentation.

It's a data sharing game now. AI is accelerating it. And the firms that demand open, connected, portable data from their partners will be the firms that lead.

This article is based on the Next Mile podcast episode "Relentless, Relevant, and Ready: Orion Ascent Recap with Jessica Perez." Listen to the full conversation for more on evaluating technology partnerships.

Want to see what connected data looks like for your firm? Book a strategy call and we'll walk through your current stack together.

Related reading:

• The AI swivel chair: why more AI tools means more problems

• From tinkering to transformation: what AI adoption actually looks like

• Building AI-ready data infrastructure for financial firms