The Investment Industry Must Urgently Rethink Its Target Operating Model in the Age of AI
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Why traditional target operating models are no longer fit for purpose
The investment industry is on the brink of a structural reset. Traditional target operating models (TOMs), built around today’s functional requirements and legacy assumptions, are rapidly becoming obsolete. The rise of AI, combined with the need for open architecture and secure, scalable data flows, demands a fundamental rethinking of how operating models are designed and maintained.
At the core of this shift is the need for true ‘openness’ - not just in principle, but in practice. Most TOMs still rely on a patchwork of legacy systems. Yet it only takes one closed architecture to break the entire model.
When a single system restricts data flows or lacks modern APIs, it becomes a bottleneck that undermines the agility and scalability AI requires.
The promise of AI is systemic intelligence, but that promise collapses when data is trapped in silos.
Balancing openness with information security in the age of AI
The counter to ‘openness’ is ruthless control over information security. Multi-tenanted, co-hosted solutions are finished. And don’t forget that even the biggest software providers have had their fair share of security compromises.
The opportunity to embrace API-first architecture has never been greater. AI has dramatically lowered the cost and complexity of integrating systems and automating workflows. What once required months of development and bespoke middleware can now be achieved in days with the right APIs and orchestration layers. But this requires a mindset shift: from building to spec, to building for adaptability.
Where TOMs deliberations fall short is that they are too focused on functional requirements of today. Yet those functional requirements demand a complete reset. They are also predicated on a current product or service mix that is quickly becoming outdated e.g. overly active strategies driven by human judgment in siloed decision-making environments.
The future will be defined by systemic processes, where decision-making is augmented or even led by AI. Humans will still play a critical role, but those roles will be more tightly defined. For example, whether it’s portfolio construction, risk management, or client engagement, the emphasis will shift from subjective calls to data-driven, repeatable processes.
TOMs must be built to support this evolution, not constrain it.
Adaptability – A core requirement for Institutional Investors
The ability to adapt quickly—to reconfigure processes, plug in new tools, and scale insights—is now a core requirement, not a nice-to-have.Yet, RFP processes - the industry’s primary mechanism for evaluating vendors and platforms - rarely probe this deeply. We are yet to see an RFP that demands full API documentation or tests the real-world interoperability of systems. This oversight is critical. Without transparency into how systems connect and share data, firms risk embedding fragility into their operating models from day one.
A simple litmus test reveals the truth: if any of your systems lack robust APIs, impose penalties for data transfers, or require manual workarounds to connect, they are not just outdated—they are liabilities. These are the Achilles’ heel of any scalable, AI-ready operating model. And their true cost far outweighs the direct charges imposed by that system provider as they require a whole set of work-arounds.
The investment industry must stop treating TOMs as static blueprints and start viewing them as living ecosystems. The assumptions of the past - closed systems, rigid workflows, and product-centric design - must be replaced with openness, modularity, and adaptability. AI isn’t just another tool; it’s a catalyst for reimagining how the industry operates. The firms that act now will define the future. The rest will be left behind.
Rethinking the investment industry’s operating model for the AI era
The investment industry stands at a pivotal moment. Traditional Target Operating Models (TOMs), built around legacy systems and outdated assumptions, are no longer fit for purpose. The rise of AI demands a radical overhaul—one that prioritizes openness, adaptability, and secure, scalable data flows.
At the heart of this transformation is the need for true architectural openness. Most firms still operate with fragmented systems, where even a single closed platform can cripple agility. AI thrives on seamless data access and integration; silos and manual workarounds are not just inefficiencies—they’re liabilities. If a system lacks robust APIs or penalizes data transfers, it’s not just outdated—it’s actively undermining scalability and innovation.
Security must evolve in parallel. Multi-tenanted, co-hosted solutions are increasingly vulnerable, and even major providers have faced breaches. The future lies in API-first, modular ecosystems that balance openness with uncompromising security.
Human roles in AI-augmented investment decision-making
AI has dramatically reduced the cost and complexity of integration. What once took months of development can now be achieved in days. But this shift requires a new mindset: building not to spec, but for adaptability. TOMs must be designed as living systems, capable of evolving with changing technologies and business needs.
Current TOM frameworks often focus too narrowly on today’s functional requirements, which themselves are rapidly changing. The industry’s reliance on human-led, siloed decision-making is giving way to systemic, AI-augmented processes. Whether in portfolio construction, risk management, or client engagement, the emphasis is shifting toward data-driven, repeatable workflows. Human expertise remains vital—but in more focused, strategic roles.
Yet, the industry’s primary evaluation mechanism—RFPs—lags behind. Few RFPs demand full API documentation or test real-world interoperability. This oversight embeds fragility from day one. A simple litmus test reveals the truth: if your systems can’t connect seamlessly, they’re not future-ready.
Firms that act now will define the future of investment management
The investment industry must stop treating TOMs as static blueprints. Instead, they must be dynamic ecosystems—open, modular, and built for continuous evolution. AI isn’t just another tool; it’s a catalyst for reimagining how the industry operates. Firms that embrace this shift will lead the future. Those that don’t risk being left behind.
AI Investment Management Insights
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