The conversation around artificial intelligence in financial advice has moved quickly—from curiosity, to experimentation, to something far more structural. What was once discussed as a set of tools is now being recognised as a shift in how advice businesses are built, how technology is used, and ultimately, who controls the advice process.
In a discussion between Patrick Gardner and Mark Papendieck, Chief Commercial Officer at Dash, this shift is explored in depth. The central theme is not simply that AI is changing financial advice, but that it is redistributing power—away from rigid platforms and toward advisors themselves.
Much of the early focus on AI has centred on tools—what to use, how to prompt, and which platforms deliver the best results. But as Papendieck explains, this is the wrong starting point.
AI is not just another layer of software. It is a capability that allows advisors to build, customise, and deploy solutions in ways that were previously impossible. In practical terms, this means tasks that once required developers, large budgets, or months of work can now be completed in minutes.
One example shared in the discussion highlights this clearly. Using “vibe coding” tools such as Replit, Papendieck demonstrated how an advisor could build a working client fact find—complete with user interface—in under five minutes for less than a dollar.
While the result may not be perfect on the first attempt, the implication is significant. Advisors are no longer constrained by the tools provided to them—they can create their own.
This marks a fundamental change in how advice technology evolves.
Historically, platforms have attempted to provide end-to-end solutions—fact finds, modelling tools, reporting, and execution—all within a single environment. This “one-size-fits-all” approach has defined the industry for decades.
That model is now beginning to break down.
As Papendieck outlines, the future of platforms is less about controlling the entire experience and more about acting as infrastructure—the rails that enable advice to be delivered, rather than the interface through which everything must pass.
This shift is driven by two forces.
First, the rapid emergence of AI tools means advisors can now build bespoke solutions tailored to their specific needs. Second, open architecture and cloud-based systems make it easier than ever to integrate these tools into a broader ecosystem.
The result is a more flexible environment, where advisors can choose how they work, rather than being limited by platform constraints.
Dash’s response to this shift is a marketplace-driven approach.
Rather than attempting to be the best at every component of the advice process, the platform focuses on orchestration—bringing together financial planning software, investment capabilities, and third-party tools within a single, connected environment.
This allows advisors to:
In this model, the value of the platform is not in its individual features, but in its ability to connect and coordinate them.
As Papendieck notes, the lines between advice software and platforms are already beginning to blur—and over time, they may disappear entirely.
Despite the rapid pace of innovation, a recurring theme throughout the discussion is caution.
The biggest risk facing advice businesses is not failing to adopt AI—it is adopting it without a clear strategy.
Papendieck frames this through a simple, but powerful framework: every task in a business should be placed into one of three categories—what to automate, what to augment, and what to protect.
The first category includes repetitive, rules-based tasks that add little value, such as administrative processes or basic reporting. These are natural candidates for automation.
The second category includes more complex activities where AI can assist but not replace human involvement—areas such as analysis, modelling, and workflow optimisation.
The third category is the most important. These are the elements of a business that define its value—typically human relationships, judgment, and empathy. These should not only be protected, but amplified.
The risk, as Papendieck explains, is that businesses rush into automation without recognising what makes them valuable in the first place.
Underlying all of this is data.
For AI to be effective, businesses must have control over their data—how it is stored, accessed, and used. This is why Papendieck emphasises the importance of cloud infrastructure and enterprise architecture as a starting point.
Without this foundation, even the most advanced tools will struggle to deliver meaningful results.
Conversely, when data is structured and accessible, the possibilities expand rapidly. Advisors can build workflows that automate communication, personalise client interactions, and generate insights in real time.
In this environment, the ability to use data becomes a competitive advantage in its own right.
A key concern often raised in discussions about AI is whether it will replace financial advisors.
Papendieck’s view is more nuanced.
In the short term, AI is far more likely to transform back-office processes than client-facing interactions. Tasks such as meeting scheduling, report generation, and data analysis are well suited to automation.
However, the core of financial advice—relationships, trust, and human understanding—remains difficult to replicate.
Instead, the more immediate impact is augmentation.
One example discussed is the use of digital avatars to deliver personalised client updates. Advisors can record a single message, which is then adapted and distributed at scale—maintaining a human presence while significantly increasing efficiency.
The implication is clear: advisors are unlikely to be replaced by AI, but they may be outperformed by those who use it effectively.
Perhaps the most striking aspect of AI’s impact is the speed at which it is unfolding.
Unlike previous technological shifts, such as the rise of the internet, AI is not simply changing how work is done—it is changing who does it. Tasks that once required human input can now be completed by machines, often faster and at lower cost.
This creates a powerful economic incentive for adoption.
As Papendieck notes, businesses that ignore AI risk being overtaken by competitors who can deliver services more efficiently.
At the same time, the pace of improvement continues to accelerate, with capabilities increasing and costs decreasing in a matter of months rather than years.
Looking ahead, the advice technology landscape is likely to become more fragmented, but also more flexible.
Rather than relying on a single provider for all functionality, advisors will increasingly assemble their own ecosystems—combining platforms, tools, and custom-built solutions to suit their specific needs.
This fragmentation is not a weakness, but a sign of maturity.
It reflects a shift away from standardisation toward personalisation—not just for clients, but for advisors themselves.
In this future, the role of platforms is not to dictate how advice is delivered, but to enable it.
The evolution of AI in financial advice is not simply about efficiency or automation. It represents a deeper shift in how the industry operates—one that places greater control in the hands of advisors.
From rapid prototyping and custom workflows to open ecosystems and data-driven insights, the tools now available are fundamentally changing what is possible.
Yet the most important takeaway is not technological.
It is strategic.
Businesses that succeed will not be those that adopt the most tools, but those that understand where AI fits within their model—what it should replace, what it should enhance, and what it should leave untouched.
In that sense, the future of advice is not being built by technology alone. It is being shaped by the decisions advisors make about how to use it.