I recently joined a BlackRock Aladdin’s webinar to discuss target operating model transformation. The conversation reinforced a point I see playing out across the industry: firms have no lack of change to face. All the more important that they focus on a target operating model to support the allocation of scare capital, and balance the need for stable core operations and innovation. They need a clear direction, reliable foundations and the discipline to invest where change will create real business value.
This increasingly means being deliberate about where capabilities sit. Most firms already operate across a mix of proprietary capabilities, third-party platforms, managed services, external data and strategic partners. The more useful question is not simply whether to build or buy, but where ownership creates genuine differentiation, where partners can deliver more efficiently, and how those components work together as a coherent operating model.
A lot of our discussion during the webinar came back to the same issue. Investment managers can no longer treat transformation as a finite technology initiative. In my experience, change is a continuous business discipline. Best-in-class firms deliver change well. Even those firms require transformation when confidence in data, platforms or processes falters to the point that broader intervention is required. The objective is an effective operating model that can absorb change, support growth and give the business confidence in its data and decisions.
The idea of a single “target” operating model has always been slightly misleading. Firms may be working towards a defined future state, but the market will continue to move around them. Client expectations change, platforms evolve, new products and competitors alter the economics. We have seen this first-hand on multi-year transformation programs where funding constraints appear in, let’s say, year two. The core objectives of lower cost, greater efficiency and scalability remained, but achieving them meant adapting the route to the target state and finding pragmatic ways to keep the transformation moving. Cost, control and scalability remain constant concerns, but the conditions in which firms address them do not stand still.
When we surveyed more than 65 buy-side firms for Citisoft’s 2026 Transformation Survey, every firm profiled was in some form of change. That ranged from focused functional improvements to large, multi-year programs, and most were running several initiatives at once.
A three-to-five-year north star target operating model (TOM) is useful, but aspirational. It gives the organization a common direction and helps management test individual change investments against a longer-term ambition. Is the product “core” or innovative? But the route to achieving the goal is rarely fixed. In practice, firms need a rolling roadmap that can be revisited each year as priorities, budgets and dependencies change.
I rarely see firms debating modernization versus transformation as an abstract choice. Sponsors are deciding where limited budget, resources and attention will have the greatest impact. They also have to weigh that potential value against implementation risk and the organization’s capacity to absorb change.
That is why transformation should not be defined by its size. The better test is whether the proposed change addresses a genuine business imperative. M&A, a shift in strategy or leadership, product growth and accumulated technical debt can all create that imperative.
Left unchecked, those pressures often produce an operating model that reflects years of incremental decisions rather than a coherent strategic design. New capabilities are layered onto legacy technology, specialist tools are added to address immediate needs, and complexity accumulates faster than it is retired.
The challenge is that this compounds technical debt and effects a growing gap between the firm's strategic ambitions and its ability to execute them efficiently.
| Driver | Percent of Firms |
| Business strategy | 70% |
| Operational efficiency | 48% |
| Scalability | 38% |
| M&A | 53% |
The measures of success for transformation initiatives should be equally practical. Can the business trust its data? Can teams deliver change more quickly? Can they cover a broader range of data or launch a new product without rebuilding the operating model around it? Those outcomes tell us far more than the number of systems replaced or projects completed.
Continuous change does not require continuous replacement. A sound core platform may serve a firm for 10 to 15 years if the architecture around it is deliberate and the platform can be extended as the business grows.
The foundation has to do the basics well. It should produce predictable, trusted investment data, support controlled processes and allow change to be introduced at a pace users can absorb.
This becomes harder as complexity builds. Middle-market firms in particular can find themselves supporting more products, private market assets and geographies through a growing collection of disconnected applications. Our survey of buy-side firms linked the efficiency challenge to these pressures, as well as to regulation, legacy technology and accumulated point solutions. Without common standards and a dependable investment book of record, each new demand adds friction.
AI will change several parts of platform operations, including integrations, break management and day-to-day workflows. But firms cannot scale AI safely on top of unreliable data and brittle processes. Books of record, data architecture and a shared ontology matter (deterministic data). Different teams may need different views, but those views must be grounded in the same trusted information.
Sixty-one percent of surveyed firms said AI adoption and implementation was a priority for the next 12 months, while 70% intend to deploy AI within their operating models within three years. It features in 19% of all the functional change initiatives planned over that period, particularly in data-related functions.
The answer is not to hold back until every underlying issue has been solved. It is to be realistic about where AI can create value now and to put the right context, governance and deterministic controls around it. That matters particularly in regulated investment and operational processes, where a plausible answer is not the same thing as a reliable one.
The near-term opportunity is practical: shorter development cycles, more efficient workflows and better connections across the operating model. Adoption is moving fastest where AI can strengthen an already differentiated capability. Human expertise remains central to supervising, validating and governing the result.
Lower cost and faster delivery matter, but they should not be the primary end goal. The real advantage is being able to grow assets, products and client reach without adding the same level of operational complexity.
The firms that stand out over the next few years will not necessarily be the ones changing the fastest. They will be the ones making deliberate choices about where change matters, investing in the right foundations and evolving without adding another layer of complexity each time.
A well-designed operating model allows the platform to absorb more of that change while investment and operations teams focus on work that requires their expertise.
Explore Citisoft’s 2026 Transformation Agenda report to see how more than 65 buy-side firms are prioritising operating-model change, technology investment and AI adoption.
Read the 2026 Transformation Agenda report