Why credibility with investors starts with talent

The single most impactful thing ambitious and change-oriented CTOs, COOs and heads of investment technology functions can do to drive high performance is focus on talent — hiring and empowering it drives outcomes and retention.

In earlier papers, I have made the case that excellent investment technology delivery requires an uplift in governance, and specifically, designing governance that is fit for purpose. This is important because an inherited ecosystem will often prove frustrating for talent, while a designed ecosystem can create an empowering environment for talent.

For organisations committed to this aspiration and to the governance uplift, this paper shifts focus to the talent that will drive delivery. This is written to help COOs, CTOs and heads of investment technology enhance capability and turn ambition into delivery.

1. Read the market signal

Markets send signals, and the talent market is telling us something. With the rapid deployment of AI, investment tech stacks are changing, and investment technology roles that a decade ago sat somewhat invisibly in back or middle office tech or ops teams now regularly sit within the investment team. Data roles are fashionable, and bilinguals — those who can code and drive tech and investment outcomes — are rare and priced accordingly.

This is not confined to asset owners. Wall Street has spent the past year watching AI labs recruit quants directly off trading floors, courted with frontier work and compensation to match. For decades the flow ran the other way: finance recruited the technical talent. That the direction has reversed is a signal.

Managing the server room is no longer what tech is about. Institutions that go on hiring investment technology roles the way they hired systems administrators twenty years ago are competing for a different person than the one the role now requires.

2. Build big or go targeted

A COO, CTO or head of investment tech that wants to drive ambition then has a key strategic organisational decision to make in designing their delivery model: build big, or go targeted.

Big is an army: large, resourced for scale, often undifferentiated, but resilient. Targeted is more like the SAS unit within that same army: small, domain-deep and built to be pointed at the highest value problems.

Build big has been an approach deployed widely — this includes managing multiple vendors, stitching together different data sets, and throwing bodies at problems. This means building a team at scale that can frequently become its own bureaucracy.

New AI tools allow a more targeted approach to be pursued. That’s because the layers underneath strategy are compressing. Fewer hands are needed to run a stack that increasingly arrives pre-integrated: which is exactly what makes targeted viable now in a way it wasn’t a few years ago. There will be work to make the transformation — but the end result can be a more nimble delivery model and a more targeted BAU model.

There are both costs and benefits. Targeted is cheaper to run, more innovative, and needs far less management. A function built this way will move quickly and operate differently to the broader corporate technology team it sits alongside.

In terms of costs — targeted is fragile if you lose the wrong person. Other parts of the tech and ops teams will notice, and could build resentment if they perceive that investment tech has decided it plays by different rules. This requires management and communication by leadership, supported by genuine governance and business owners who are actually engaged.

Ironically, what will make a CTO or COO more successful in the long term will make their life more difficult in the short term. Build big can survive less of a focus on talent — there’s enough scale to carry weak links. Targeted can’t. That’s a consequence of going targeted, and it’s why the next decision — who you hire into it — carries more weight in the targeted approach than it would in the bigger model.

3. Bet on bilinguals

If the targeted approach is adopted, the person all of this points toward is the bilingual: neither pure technologist nor pure investor, but credible in both worlds. People fluent enough in the investment problem to know what actually matters, and fluent enough in the technology to get outcomes without losing the meaning in translation.

There’s a third fluency worth naming alongside the other two: comfort with ambiguity. Like the SAS: an ability to operate with incomplete information and a shifting brief. People who need a stable, fully-specified mandate or who want this year’s approach to be the same as last year’s, will struggle in a model built around continuous re-underwriting and transformation.

This talent tends to come from two directions: investment professionals who go deep enough into the technical build to be ‘dangerous’, or technologists who spend real time working alongside or embedded inside an investment team rather than serving it from a distance. These individuals will be hard to surface through a standard search, and require interesting work and empowerment to excite them in their role.

AI is driving continuous re-underwriting across these roles, making certain skillsets largely redundant and forcing the roles themselves to evolve year by year. This evolution in role and the pace of change has a consequence most institutions haven’t reckoned with. Hiring, managing and creating a career path for these roles requires more thought than many are currently applying.

4. Empower talent

Technology functions in big organisations have historically run like government departments — slow moving, siloed, lots of internal meetings, and strong on talking about the ‘customer’, with the customer focus sometimes less evident in actions.

In this world, lots of time is spent planning and managing budget and business plan processes — headcount accumulates, vendors are added, budgets creep — while motivated people get dragged further and further away from the reason they entered the tech field.

For investment technology functions at asset owners striving to avoid this ossification, the unlock is to apply lessons from the investment program: empowering talent.

Talent is a major focus in the investment program, and the best investment organisations identify, nurture, empower and reward talent.

Earlier papers in this series discussed how the people with the deepest understanding of both the investment program and the technology that serves it are precisely the people most consumed by institutional process — budgeting cycles, vendor administration, steering committees that consume bandwidth without improving decisions. That is what disempowerment actually looks like.

Empowering talent means reversing that by design: protecting the time of the people who can do the work that matters — fewer steering committees, status reports, unnecessary meetings.

AI raises the stakes on getting this right. There’s no shortage of people selling themselves as AI experts; there is a shortage of the judgement that actually separates outcomes from activity. AI is a multiplier, not a substitute — it amplifies whatever judgement is already there. An empowered bilingual, trusted to deliver, will drive outcomes and stand up AI tools. A team drowning in bureaucracy will continue to deliver the same mediocre outcomes, just faster.

5. Lean in to investors

At large asset owners, it’s easy for the technology or operations function to drift from the investment mission — measured on its own metrics, running its own processes, solving its own problems. Nobody chooses this. It’s comfortable, but strays from delivering to the mission it exists to serve.

The best way to counter that drift is credibility with investors. And the best way to build credibility is talent; specifically, talent that investors trust. This is why bilinguals matter: they earn that trust because they speak the investors’ language, not just deliver the technology. And that is also why designing the ecosystem matters: to support this talent to deliver outcomes. Both support credibility.

That has a direct implication for how the function operates. Lean in to investors, not away from them. Every process, cadence and decision gate the tech delivery function builds should pull it closer to the investment mission; not create the comfortable distance that lets a function run its own agenda undisturbed.

6. Hire like an allocator

Now is the time for change-oriented tech and data leaders to operate differently.

Organisations tend to build on what they already have, making incremental changes to strategy, team and vendors. Asset owners do something different: they re-underwrite portfolio positions. They don’t ask whether a position was a good idea when they made it; they ask whether they’d make the same decision today, with what they now know and whether the position should still be in the portfolio.

Tech leaders can apply the same discipline — to strategy, processes, team, and partners and vendors — through the lens of capability, empowerment and credibility. That discipline is critical now: the tech and data landscape is moving too fast for decisions made two or three years ago to still be the right ones.

That approach, and the focus on talent, are leadership decisions. Bilinguals are rare, but they are findable, and they stay where their judgement is trusted and their time is protected.

That is what earns credibility with investors: talent empowered to deliver.

Joel Grant is principal of advisory firm, Jolly Rambler.