Asset managers will face margin pressure, rising costs and market uncertainty as we approach 2026. Stuart Grant presents seven practical use cases for AI in front, middle and back offices that can improve efficiency, productivity and decision-making without relying on hype.
Stuart Grant is responsible for capital markets, asset and wealth management at SAP.
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From fee compression to adverse changes in macroeconomic conditions to increased technology investments that have yet to bear fruit as expected, asset management organizations face significant headwinds as they approach 2026.
In a Analysis 2025 of the global asset management industry, McKinsey & Company found, for example, that asset manager margins have declined by three percentage points in North America and five percentage points in Europe over the past five years due to factors like these.
But a pressure relief valve is at hand in the form of targeted and well-placed deployments of artificial intelligence. AI in its different forms – generative, agentic, etc. – begins to demonstrate its value across a range of front, middle and back office use cases, empowering asset managers to capture new productivity and efficiency gains, identify and capitalize on profitable new business opportunities ahead of the competition. In its analysis, based on a survey of executives at asset management firms in North America and Europe, McKinsey determined that for an average asset manager, the potential impact of AI, generational AI and agentic AI “could be transformative, equivalent to 25-40% of their cost base.”
The challenge for asset management organizations therefore is to determine where within their organization AI can deliver the most value.
Deploy AI for maximum impact
Companies in the asset management industry are using AI on a variety of fronts. Much of this activity takes place within large organizations that have the resources to develop their own capabilities around big language models, with targeted AI agents, and so on. But the flip side of AI is that it can also help asset managers outside of the largest tier-one organizations compete on a level playing field with these larger companies.
Additionally, while many organizations are focusing their investments on customer-facing AI use cases, it is important not to overlook opportunities to create value with other scalable AI implementations across the front, middle, and back office. Rather than pursuing point solutions that might not integrate well with each other, the wisest approach to generating value from AI might be to target investments that dissolve the virtual walls between the three office levels to create efficiencies, boost productivity, streamline processes, and better inform planning and strategy.
In short, look for AI use cases that encourage – and can leverage – the free flow of data within an organization. Here are a few that seem particularly promising:
1. Automate and accelerate financial close and other financial functions. Finance has always been a field fraught with manual processes. With the help of AI agents, asset management organizations have the ability to automate many processes related to the finance function, including financial close as well as accounts receivable, accounts payable, invoice reconciliation, and more. In these scenarios, AI can support better automation of data movement. It can also provide financial industry users with proactive notification – and actionable scenarios – for potentially invisible issues related to capital surpluses/deficits, balance sheet adjustments, and more.
2. Improve risk management through true alignment with finance. Back office data can be extremely valuable to middle office risk management teams. These teams can use data relating to investor holdings, cash flow, market liquidity, margin/collateral, etc., combined with customer profile and communications data to identify early signals of customer redemptions and associated liquidity risk.
3. Quickly identify and mobilize on opportunities for new pricing structures and business models. Organizations can incentivize their AI tools to research and model the impact of potential fee changes as well as new business models. What does historical data suggest about the impact of a fee change on customer accounts? Are there opportunities to split an existing area of the business (such as a specific asset class or geographic funds) into two or more parts, or to group clients differently, and if so, how strong is the business case for such moves?
4. Inform decisions about expanding into new products or geographies. Your organization is considering entering a promising but relatively risky new geographic market. How have past moves like these turned out in terms of expected and actual costs? What are the likely regulatory and HR impacts of such an approach? A dialogue with an AI generative digital assistant can provide valuable answers to questions like these, leading to better-informed strategic decisions.
5. Model what-if scenarios around the potential impact of portfolio rebalancing on future earnings as well as clients’ investment priorities and risk appetite. AI tools can provide insight into the potential impact of these types of changes, while also offering recommendations on the optimal timing in light of accounts payable obligations and other factors. By making such connections with data, AI helps bridge information gaps between the finance function and front-office portfolio management, enabling more accurate strategic planning and budgeting.
In the case of a firm I work with, for example, it seeks to combine portfolio attribution data on the performance of individual elements of its portfolio with data on clients’ risk appetite and fee structures. The goal is to better understand the financial implications of portfolio rebalancing relative to client expectations and future earnings.
6. Increase productivity. Some asset management executives I’ve spoken with recently say their organizations are looking to double their assets under management without a significant increase in headcount, simply by leveraging AI and AI agents more broadly in their organization. They create AI agents and place them alongside employees – essentially as digital extensions of those employees. Ultimately, the productivity gains generated by these agents allow small and medium-sized businesses to pull their weight to compete on an equal footing with larger companies.
7. Refine fraud detection during customer onboarding. AI is able to quickly analyze and validate the authenticity of onboarding documents, identifying even the most minor anomalies (in font size, document formatting, etc.) that may suggest that a customer is not who they seem and therefore require further vetting.
As impactful as such use cases may be within an asset management organization, maximizing their value is highly dependent on the quality and accessibility of the data that powers them. Above all, data must be understandable by humans and self-service machines. Often, companies extract data from source applications and move it into a data lake. However, this removes vitally important semantics and context specific to the application environment. Without this metadata, AI results – and its overall impact – could be suboptimal. So, in many cases, organizations are better off leaving this data in its natural application environment with accompanying metadata. Think of the data in these applications as the batteries that power generative AI, agentic AI, and intelligent analytics within an organization. The more powerful the batteries, the more an asset management organization will be able to leverage its AI investments to overcome the headwinds it faces.
About the author
Stuart Grant is Head of Capital Markets, Asset and Wealth Management at SAP. For over 20 years he has worked with data in the financial markets industry in roles spanning product management, business development and business management.