The future experience of human-augmented AI advice

By Gaby Diamant, Co-founder and CEO of BridgeWise

Artificial intelligence is becoming a core part of how financial institutions and platforms operate. Asset managers and advisers are increasingly using AI to drive analytics, improve forecasting and increase operational efficiency as markets become more complex and the volume of data continues to grow. It is estimated that around 65% of asset managers now use AI in some form, a 20% jump from 2021, highlighting just how quickly adoption across the industry has accelerated.

This shift is not theoretical. Global asset managers can now use AI to scan thousands of earnings reports during the reporting season, flagging companies where guidance has changed or margins are under pressure. What once took teams of analysts days or weeks to do can now be done in minutes. AI systems are uniquely positioned to analyse large volumes of structured and unstructured data in real time, at a speed unmatched by human resources. They can process financial statements, market data, news and macroeconomic indicators rapidly, helping firms respond more quickly to changing conditions and ensuring advisers are always equipped with relevant guidance for clients.

How AI excels at financial decision-making

AI shines when given tasks that require speed, scale and consistency. In investment environments, this includes screening vast datasets, identifying patterns and supporting forecasting models using historical and real-time information. For example, AI can detect correlations between commodity prices, currency movements and sector performance across multiple markets simultaneously, something that would be extremely difficult for a single analyst to track manually.

Another practical example is in portfolio construction. AI can quickly model thousands of portfolio combinations under different market scenarios, stress-testing outcomes such as interest rate rises or geopolitical shifts. This gives advisers a clearer starting point when discussing with clients.

AI is also playing a shifting role in supporting regulatory and compliance alignment, particularly when systems are designed with specific use cases in mind. Specialised AI models can monitor trading activity, flag potential breaches and interpret regulatory requirements in real time, helping advisors stay ahead of any potential issues rather than being reactive. This is where generic LLMs can fall short, as compliance often depends on the context, the location and any product-specific rules. When applied with more care, tailored AI and specialised models can reduce the risk of human error during repetitive tasks and give compliance teams more time to focus on oversight, and judgement.

Human judgement still remains essential

Despite these advances, AI should not completely replace the core human elements of financial advice. Financial decisions are rarely just technical; they are shaped by personal circumstances, emotions and long-term life goals extending beyond data points.

Human advisors bring ethical reasoning, contextual understanding and relationship-based trust to the advisory process. For example, during a market downturn, an advisor may recognise that a nervous client needs reassurance and perspective rather than immediate portfolio changes. An augmented experience with AI gives advisors more time to communicate with clients and calm their anxieties. These nuances are difficult to embed into algorithms but remain critical for responsible advice delivery. Studies consistently show that clients are more likely to act on advice when a human adviser is involved, especially during periods of uncertainty or personal emotional stress.

A human-augmented AI model

The future of financial advice is not just about splitting tasks between humans and AI, but about how they work together in a more integrated and cohesive way. AI is moving far beyond being a behind-the-scenes tool and is becoming capable of forming informed, well-rounded, and contextual recommendations that reflect both market conditions and an individual client’s needs, wants and goals.

Today, AI can analyse portfolios, explore risk tolerance, test different models and make tailored suggestions in a very short period. AI considers factors like risk tolerance, timeframes and ongoing economic challenges to shape data-driven and hyper-relevant recommendations.

Advisers are, however, still central to this process, but now their role begins to shift. Instead of starting from a blank slate, working with AI-generated insights helps advisors apply judgment and shape insights into advice that fits the client’s individual needs. This creates a more dynamic relationship where both AI and the adviser are contributing to stronger outcomes and returns. The trust between a client and an advisor comes from a sense of reliability, and introducing AI to this process deepens it further.

Trust at the individual investor level

For individual investors, AI is starting to change what it feels like to access financial advice as tools are becoming more capable of delivering insights that are tailored from person to person, rather than offering broad or generic information like it did previously.

An investor can now see how interest rates can impact their portfolio, what their cash flow or longer-term plans might look like, all with the integration of AI. More advanced and hyper-relevant specialised language models can monitor all factors over time and automatically adjust guidance when and if each changes, creating an experience that feels more ongoing and responsive for the client.

This has the potential to open up a level of personalised advice that was once limited to a much smaller group of investors. Human advisers play a pivotal role in maintaining the client relationship and trust, especially when the investment can get emotional, and this trust is built not only through these relationships, but also through consistency, clarity and accuracy of the insights people receive (many of which are informed by AI).

As these tools become more capable, transparency becomes more important, and people need to understand how recommendations are being made, what information is being used and where the limits of this data may be. Explainable AI helps make this process much clearer, turning trust into confidence.

Keeping people at the centre of advice

Looking ahead, financial advice is likely to be shaped by systems that are both highly capable and still grounded in human understanding. AI will become more proactive and personalised, and in some cases, take a more active role in implementing decisions aligned with an investor’s goals.

At the same time, the role of an adviser becomes more focused on the parts of the experience that matter most to people, like building trust, offering reassurance and helping clients (especially early ones) navigate uncertainty and big life decisions. These are areas where human judgment and empathy still make a significant difference.

Rather than reducing the importance of advisers, this shift gives them more space to focus on those relationships, which clients benefit from faster with more tailored and accessible guidance. The strongest model will be one where both sides work together seamlessly, combining the strengths of advanced technology with human elements that make advice feel personal, trustworthy and elevated.