A concise guide to successful operating model transformation
Investment organizations have more transformation levers than ever. Modern platforms can replace legacy systems, strategic providers can deliver capabilities once built internally, and AI can automate work and augment decisions. Yet better tools do not automatically create a better enterprise.
Programs often struggle because leaders move from ambition to execution before defining how the future organization should operate. The resulting transformation traps are remarkably consistent – starting with the solution, placing capabilities without sufficient consideration, designing in functional silos, automating legacy processes, or discovering critical assumptions too late.
Avoiding these traps requires intentionally designing the operating model around the desired business outcomes. The available levers may change; the need for a clearly defined operating model does not.
Start with the operating model – not the solution
A platform replacement, outsourcing initiative, or AI program may be the right answer. But starting with the solution can prematurely constrain the outcome. The starting point is a clear definition of the future operating model and the business outcomes it needs to support. Only then should leaders decide whether technology, providers, AI, internal process change – or a combination – should enable the model.
The target operating model should shape the solution – not the other way around
Place capabilities deliberately
The traditional build-versus-buy decision is no longer sufficient. Capabilities now span internal teams, investment platforms, custodians, administrators, managed-service providers, data providers, cloud platforms, and AI. The sharper question is where each capability should live, based on strategic value, economics, scalability, and control.
As execution moves outside the firm, accountability must remain inside it. The retained organization still needs strong governance, oversight, and clear accountability. Execution can be outsourced; accountability cannot.
Design workflows and data end-to-end
Function-by-function requirements rarely reveal the greatest risks. Those risks usually sit between capabilities – in handoffs, control points, and inconsistent definitions. A single workflow may cross an investment team, service provider, technology platform, and control function before it is complete.
Data is the connective tissue. Firms need trusted data, clear ownership, and consistent definitions. These fundamentals are also critical to AI readiness, along with well-defined processes, controls, and accountability.
Transform the work – do not automate the past
Modern technology can still reproduce legacy workflows, spreadsheets, manual controls, and organizational handoffs. Outsourcing can simply transfer inefficient processes, while AI can automate activities that should have been eliminated. Before choosing how work will be performed, leaders should ask why the activity exists, whether it can be simplified, which party or tool should perform it, and where human judgment remains essential.
Validate before committing
Late discovery is expensive. Workflow simulations, structured vendor demonstrations, proofs of concept, and due diligence can surface critical assumptions before major commitments are made. Readiness also extends beyond go-live: the model is ready only when the organization is prepared to operate, govern, and sustain it.
The leadership agenda
Technology, outsourcing, and AI will increasingly be used together. Leaders should therefore evaluate them through one operating-model lens.
Successful transformation requires governance designed to drive decisions, not simply report status – particularly as dependencies span internal teams and third-party providers.
Six Questions Leaders Need to Answer Before Transforming
- What capabilities are required?
- How should work be performed?
- Where should capabilities live?
- What data connects the ecosystem?
- Where does accountability reside?
- How will readiness be demonstrated?
The objective is not to implement more technology, outsource more work, or deploy AI. It is to build a better operating model – and use the right combination of levers to make it possible.
These traps are easiest to address before major commitments are made – but they can also be addressed when a transformation loses momentum.
Preparing to launch a transformation – or struggling to regain momentum?
Olmstead’s Investment Transformation Accelerator helps organizations at two critical points in the transformation journey: establishing the foundation for a successful launch or identifying what needs to change when an initiative has stalled.
Over 2-3 weeks, we assess operating-model alignment, execution risk, data and controls, vendor oversight, testing, and organizational readiness to provide an independent view of what needs to be in place to move forward with confidence – or what is getting in the way of progress.
Leaders receive an executive heatmap, prioritized risks and root causes, and a sequenced 30-, 60-, and 90-day action plan designed to establish the right foundation, resolve barriers, and create a clear path forward.
Start strong. Regain momentum. Move forward with confidence.