As we get to the end of the year, many companies across the wealth management sector are beginning to build their budget plans for 2026. All departments are pitching their stake in the allocations and while artificial intelligence (AI) has generated a lot of hype over recent years, teams must still make the case as to why the company needs it.
Global AI spending is expected to hit $375bn in 2025, according to data by UBS, and could rise by 33% to hit $500bn in 2026. It is evident that firms are understanding the benefits of the technology and are investing in it. But, with so many different AI tools available in the market, firms will need to assess which parts of the business AI can make the most difference.
FinTech Global spoke to several players in WealthTech to offer advice for teams looking to pitch AI as mission-critical for wealth management during budget discussions.
A common theme across everyone was the importance of solving major problems that drain time and resources.
Rob Paisley, director – banking, financial services and insurance at SS&C Blue Prism, explained, “Senior leaders take notice when AI solves long-standing problems that were previously thought impossible. In the AI game, the proof needs to be in the pudding. Look for technology partners that have already used their solutions to transform their own businesses, demonstrating the power of AI in a real-world setting.”
In the same vein, Fredrik Davéus, CEO and co-founder of Kidbrooke, emphasised that senior leaders pay attention when AI is being framed as a business enabler.
“The most effective conversations focus on measurable outcomes, e.g. faster time-to-market, improved compliance accuracy, and enhanced advisor productivity. When AI is linked to strategic objectives like scalability or client retention, rather than abstract innovation, it becomes a clear investment priority.”
On top of this, AI still has a lot of trust issues that can dampen adoption. As such, explainability and integration are critical factors to cover during discussions. “Decision-makers want reassurance that AI solutions can be audited, embedded seamlessly into existing workflows, and aligned with regulatory frameworks.”
Finally, fincite founder & co-CEO Friedhelm A. Schmitt, had three nuggets of advice ahead of budget meetings: risk, results and relevance.
Senior leaders are coming to terms with the fact ignoring AI has greater long-term risk than exploring it. “The real threat is not “doing too much AI”, it’s falling behind in client experience and operational leverage.” In addition to highlighting this, teams should highlight the tangible wins that come from implementing AI with specific KPIs AI can influence, such as lower churn faster onboarding, reduced cost per lead. Finally, they must refer to strategic alignment. “When AI initiatives directly improve client proximity or advisor productivity, they’re no longer optional, they become core.”
While these are all important parts of any budget discussion, teams must understand what type of AI they actually need for a job. Schmitt added, “Most companies still lack a systematic framework for deciding where AI adds value.” For instance, if a task involved pattern recognition from unstructured data, AI is perfect, but if the problem is dynamic then agents or LLMs are best. If the process required summary or exploration then co-pilots work well and if the solution needs to be deterministic or binary then use business rules.
Schmitt said, “This kind of clarity is essential, especially in regulated environments like wealth management. Many regulated tasks (e.g. MiFID suitability checks or AML flags) are inherently deterministic. Here, applying AI may not be appropriate or even allowed. But for fuzzy front-end processes, AI shines.”
Misconceptions around investing into AI
When trying to get approval from senior leaders, it can be easy to overpromise, but it is risky. AI can transform workflows, whether it is automating manual data collection, improving insight generation for advisors or provide a personalised chat experience for customers, there is a lot of potential for the technology. But it is not a miracle worker.
Paisley noted that a common misconception he sees is the idea AI is a “magic bullet that can solve any problem.” The reality is that AI is best used where traditional methods are falling short. Carefully examining workflows can help firms identify the places where AI can really make a difference and the ones where alternative solutions are more effective.
Another misconception is that to get the most out of AI, a firm needs a colossal solution that comes with all the bells and whistles. This is not the case, and a smaller solution can be much better for a firm.
Davéus explained, “A common misconception is that AI must be massive or risky to be meaningful. That it requires an overhaul of legacy systems or a huge upfront commitment before value is realised. In reality, AI can be implemented modularly and safely, especially when paired with a strong analytical foundation.”
This was a similar notion Schmitt highlighted. There is often a belief that a huge amount of data is required to even start using AI, when some of the most high-impact use cases involve summarisation, pattern detection and personalisation work, all of which use contextual data and don’t need huge datasets. “It’s not about big data, it’s about the right data in the right place.”
Another misconception that both Davéus and Schmitt noted was the fear of humans losing their jobs. In fact, AI works best as a partner to humans, working alongside advisors, compliance teams and product managers to handle time-intensive manual tasks like data prep and pattern recognition.
Schmitt also highlighted some of the common misconceptions that he has seen in boardrooms and budget discussions. This includes the idea that AI isn’t secure and risks data leaks. While these fears are legitimate, they shouldn’t prevent a firm from adopting AI. “Today, firms can deploy LLMs in EU zones, use fine-tuned on-prem models or rely on vendors with strict SOC 2 / ISO 27001 / GDPR compliance. The real issue is internal clarity, not external threat.”
The final two misconceptions Schmitt mentioned was the need for PhDs to implement AI, which is no longer the case thanks to AI-as-a-service, prompt-based orchestration and vertical co-pilots, and a sense that AI is only relevant to tech companies, when in reality AI is for everyone and ignoring it risks a firm being left behind.
The most impactful use cases for wealth management
As mentioned previously, wealth management is ripe for AI and there are countless processes that could be improved with the technology. However, there are certain areas that could gain the most from the technology and are a better place to start the AI journey.
Paisley noted, “AI can have a significant impact on wealth management by automating complex tasks, such as creating documents, letters of instruction, wire transfers, and communicating rebalancing changes to custodians. By leveraging AI, firms can reduce manual errors, increase efficiency, and improve the overall client experience.”
For Davéus there are three core areas that can benefit the most from AI. The first is by supporting the advisor. AI tools can analyse meeting transcripts, identify compliance gaps, summarise client needs and recommend actions, he explained, all of which can save the advisor multiple hours of work every week.
Secondly, AI can bolster personalised client engagement. Its capabilities allow AI to tailor communication to each customer, leveraging analytics-based insights in real time to boost conversion rates and customer satisfaction.
Finally, AI can transform operational intelligence. “AI can create easily explainable outputs based on monitoring of large-scale data flows to detect anomalies, automate quality checks, and streamline reporting, massively improving quality of service and cutting costs.”
Schmitt had a slightly different opinion. Instead of there being certain areas that can have the biggest impact, he sees the real potential of AI coming from multiple smaller successes.
He said, “Let’s challenge the premise: Quick ROI doesn’t come from a few big bets. It comes from hundreds of small wins that compound over time. That’s why the best approach isn’t to look for one “AI magic bullet”. It’s to adopt an AI-first mindset across the whole organization. Firms that empower everyone, from compliance to marketing, from sales to HR, to identify micro use cases will outperform. Even if AI is not yet better, it may already be faster, cheaper, or more scalable. And because AI learns, those marginal gains will only grow.
“The real breakthrough happens when companies: deploy AI in linked processes or process chains, identify small time or quality wins in each step and treat AI gains like compound interest: small edges that grow exponentially when connected. Over time the compounding effect of AI in process chains will beat any one-shot ROI.”
Final thoughts on pitching AI during budget discussions
Paisley finished off by emphasising the importance of working with a vendor with deep domain expertise. While it might be tempting to work with the latest AI vendors that have fancy tools, if they don’t understand the intricacies of wealth management, it is unlikely the AI tool would be as powerful as one that was built by industry experts.
He added, “Choosing proven solutions that work in the real world is a must. By partnering with a vendor that has already demonstrated the effectiveness of their products in highly regulated environments, firms can minimize the risk of experimentation and maximize the benefits of AI adoption.”
As for Davéus, his final words emphasised the importance of viewing AI as a support, not a replacement. “AI should not be viewed as a multiplier for existing capabilities. The firms seeing the strongest ROI are those that integrate AI into robust analytical ecosystems, ensuring outputs are explainable, consistent, and regulator ready.”
Finally, Schmitt gave a slightly different perspective of the future of AI in wealth management. He said, “Get ready for AI to disappear.
“Not because adoption drops. Quite the opposite will be the case. AI is here to stay, but AI will become so deeply embedded in workflows, interfaces, and decision chains that we’ll stop noticing it. Just like no one today says they’re using “cloud” or “mobile”, they’re just using software. The firms that succeed won’t be the ones talking the most about AI. They’ll be the ones whose clients say: “That was fast.”, whose advisors say: “That was helpful.” and whose compliance teams say: “That saved us time.””
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