The AI revolution will be platformed

Artificial intelligence will not transform investment management through standalone tools or ambitious internal build projects. It will do so through the platforms that already sit at the operational heart of the industry, and that race is already well underway.

For years, the question of how artificial intelligence would take meaningful hold in professional investment management remained tantalisingly open.

Would it arrive through proprietary models built by elite in-house quant teams? Through fintech disruptors promising to displace the incumbent order? Or through the slow, unglamorous process of incremental automation in operations and compliance?

The answer, in my view, is none of the above. The most durable and scalable deployment of AI in the industry is happening inside the platforms that asset managers already use, and cannot easily replace.

Dean Brown

Partner, UK & Global Head of Wealth and Asset Management Consulting

KPMG in the UK

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AI embedded, not bolted on

The change is already underway with AI rapidly being woven into the analytical fabric of systems that already sit at the centre of institutional decision-making.

Platforms such as BlackRock Aladdin, State Street Alpha and SimCorp One are beginning to embed generative AI and agentic capabilities into core workflows, enabling more intuitive, natural-language interaction with portfolio data and risk analytics, thereby reducing reliance on specialist interfaces and accelerating insight generation, but still largely augmenting (rather than replacing) traditional quantitative support.

Other platforms such as Room Zero are embedding AI into the analytics and operational infrastructure that supports investment management, enabling firms to interrogate AUM and flow trends, revenue and profitability metrics, and other critical management information through more intuitive, natural-language interaction across their existing technology platforms.

What is clear is that this is a structural shift remaking the category, not a capability being pioneered by a single dominant player. And the breadth of adoption across platforms of different sizes, ownership structures and geographies is telling.

At the data and analytics layer, the transformation is equally pronounced. Bloomberg’s ASKB initiative brings conversational and agentic AI directly into the Terminal. LSEG and Microsoft are enabling LSEG-licensed financial data to be used within Microsoft Copilot and agentic workflows. FactSet is embedding AI across its data, analytics and workflow tools through capabilities such as FactSet Intelligence and Mercury.

For these providers, AI is not merely an interface improvement. It is a distribution mechanism, and this is one of the biggest reasons that I believe that platforms will drive the AI revolution in wealth and asset management.

If an analyst, portfolio manager, trader or client-service professional can ask a question inside the platform where the data, entitlements and workflow already reside, adoption can scale much faster than it would through a disconnected chatbot or a standalone tool.

Why platforms, not point solutions

The logic of why platforms will prove to be successful in AI adoption in investment management is structural, not merely commercial. These systems already sit at the intersection of data, workflow and decision-making across hundreds of institutional clients. They hold the longitudinal, normalised, multi-asset data sets that AI models require to generate outputs that are genuinely useful rather than superficially impressive. An LLM trained on generic financial text is a research tool; one trained on decades of position-level data, transaction history and risk model outputs from a live institutional platform is an operational advantage.

Trust compounds this structural advantage. So, too, do the economics; the switching costs associated with core investment platforms are among the highest in financial services, measured not just in technology spend but in retraining, workflow redesign and significant operating model change.

As such asset managers that have built operating models around a given platform are, in effect, committed to that platform's innovation trajectory. When that trajectory includes AI, adoption is not a discrete decision, is an update, a configuration change, an upgrade cycle. The friction that makes platform replacement so difficult is the same friction that makes platform-embedded AI so likely to succeed.

The private markets dimension

Nowhere is the platform-AI convergence more consequential than in private markets, where the data challenge has historically been most acute.

Platforms such as Allvue and S&P Global’s iLEVEL, both focused on private markets workflows, are embedding AI into portfolio monitoring, data collection, document ingestion and reporting processes across alternative asset classes. iLEVEL, for example, uses AI-powered data collection and document search to help investors extract portfolio intelligence from investment documents. Carne’s CAMDA, meanwhile, has been enhanced using Microsoft Fabric and GenAI capabilities to categorise and mine PDF documents, scaling from hundreds of files per day to hundreds of thousands.

The task of aggregating financial data from hundreds of underlying portfolio companies, normalising it across inconsistent reporting formats and producing timely risk dashboards has historically consumed significant analyst resource.

AI embedded within these platforms is compressing that cycle from weeks to hours. And that is bringing some much-needed confidence and insight to private market investors.

The concentration question

Granted, there are legitimate caveats to this optimistic framing. The concentration of AI capability within a relatively small number of dominant platforms raises questions about the diversity of analytical thought across the industry, a concern that regulators have begun to articulate, if not yet to act upon.

The potential for synchronised analytical outputs, and in stress conditions synchronised responses, is not hypothetical. When the same risk models, trained on the same data and embedded in the same systems, inform the decisions of competing asset managers, the homogenisation of investment behaviour becomes a systemic rather than merely competitive concern.

Yet the direction of travel is clear. I believe that the firms that will extract the greatest value from artificial intelligence in investment management are unlikely to be those that build the most sophisticated standalone models or commission the most ambitious internal development programmes.

They will be those that most effectively harness AI as it is surfaced through the platforms already embedded in their operations, interrogating its outputs critically, calibrating its limitations carefully, and using it to augment rather than displace the judgment that remains, for now, the industry's core and irreplaceable product.

Get an inside view

At KPMG, we’ve helped many wealth and asset managers develop and execute their technology strategies and investment plans. We also have deep global relationships with the technology and platform players embedded in our sector. Which means we have a unique view into the strategies and approaches the market leaders are taking to maximise the value of their AI investments.

To share in our insider’s view or to discuss your organisation’s unique technology opportunities, I encourage you to get in touch.

The trends that matter

Every day, our industry experts work with the UK’s leading wealth and asset managers across all asset classes, capabilities and segments. That gives us unique insight into the key challenges, opportunities and solutions the sectors are facing today. And we are keen to share what we have learned.

Over the coming weeks and months, our wealth and asset management subject matter experts will share their insights on the challenges and opportunities now transforming the sector. We will explore specific trends like AI, digital assets, tech transformation, platforms, delivery of advice and private credit. And we will focus on the interdependencies, strategic trade-offs and multilayered risks that accompany the trends.

Our focus will be on exploring the implications rather than just observing the challenges, using our experience to help explain and understand not only why the trends matter, but also what can be done to respond. With each article, our goal will be to provide actionable insights and practical advice.

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