Before You Hire a “Head of AI”
A Field Guide for Asset and Wealth Management Leaders
By Leah McGillivray-Palko and Andrew Mushore, GlassRatner Executive Search
A consistent theme has emerged in conversations with asset and wealth management clients over the past year, with a request that often takes the same form: “We need to be thinking about AI talent. Can you introduce us to some candidates?”
The instinct is right. The framing, in our experience, is premature.
The firms making this request are often rarely prepared to address two key questions: “What, specifically, do you want that person to build, and is your firm positioned to let them build it?” The gap between the firms that garner true value from AI focused hires and the firms that learn expensive lessons is not the quality of the talent they bring in. It is the clarity of the mandate and the readiness of the organization they bring it into. In other words, the decision in front of you is mostly an organizational design and change-management decision wearing the costume of a hiring decision.
Before making introductions, it can be more valuable to begin with a different conversation. This article sets out the elements of this conversation.
There is no perfect AI leader, only deliberate trade-offs
When firms picture their AI hire, they often imagine a single ideal candidate who combines investment fluency, deep technical command, governance maturity, and the credibility to influence senior stakeholders without formal authority. That candidate is a unicorn—frequently described in job specifications, rarely encountered in the market.
In our searches we see three archetypes, each strong in one dimension but carrying a predictable trade-off. The central decision is not which one is best, but which trade-off your firm can most afford to make.
- The investment or wealth data and AI leader. This is the candidate with the strongest domain credibility, someone who genuinely understands research, portfolio, and advisor use cases and can earn the trust of investment teams quickly. The watch-out is that their mandate may run narrower than the enterprise needs, and they can underestimate the technology and infrastructure realities that determine what is buildable.
- The enterprise data and AI transformation leader. This profile brings strong governance, data, and delivery experience and can stand up a scalable operating model, which is exactly what a firm modernizing its data foundation requires. The watch-out is cultural: many come from large banking or insurance environments and must adapt to the pace, scale, and decision rhythm of a leaner, investment-led platform.
- The product, technology, or consulting AI leader. This candidate offers the strongest product, roadmap, and implementation perspective and brings a current view of what the market is actually shipping. The watch-out is that they may lack the business acumen and investment credibility valued by portfolio managers and advisors, and some have advised or sold AI rather than owned its adoption and outcomes.
Rethinking where AI sits in the organization chart
There is no perfect reporting line either. Place the role under the CIO or equivalent and it gains direct investment relevance but often underweights in technology, operations, and governance. Place it under the COO or president and it gains cross-platform authority but is predicated on strong dotted lines into technology and investments to function. Place it under technology and you get data, infrastructure, and security depth, but the effort can read as an IT initiative and struggle for credibility with the business. The right answer is usually a standalone strategic mandate with hard operating links into technology, data and security, investments, and risk and compliance, rather than a clean box on an org chart.
The practical consequence is that the best candidates are frequently not the most technical AI experts. They are the individuals who make AI useful, trusted, adopted, and governed across the business. Recruiting-market analyses suggest roughly seven in ten people currently working in AI and machine learning roles did not carry an AI title in their prior role, which is why the archetype lens matters more than a keyword search.
The leadership hire, however, is only one part of the equation. Whichever archetype a firm selects, success depends on the delivery capability beneath the leader: applied machine learning and platform engineers who move models from pilot to production; a translator or product owner who decides which capabilities become real tools and drives adoption; and governance and model-risk specialists who are not optional in Canadian investment management. Firms that hire the leader and stop, or that staff only builders and forget the translator, are the ones whose promising pilots quietly stall.
What the leadership market costs
At the senior end, the market is paying material money for a role it has not finished defining. Heidrick & Struggles’ 2025 compensation survey of data, analytics, and AI officers put average total compensation for US executives in these roles at roughly US$1.13 million including equity (cited in a 2024 study) and found that nearly half of AI leader roles are reclassified existing positions, a dynamic the firm summarized as “compensation outpacing clarity”. For a CEO or CHRO, the implication is that a precise mandate is not just an organizational nicety. It is the difference between paying seven figures for defined impact and paying seven figures for a title.
For Canadian asset and wealth managers, geography is the underappreciated advantage. You will typically not win a bidding war with a US megacap or a frontier AI lab, nor should you consider it. But CBRE ranks Toronto the third largest tech talent market in North America, behind only the San Francisco Bay Area and Seattle, anchored by the Vector Institute and a deep university pipeline, and the cost of senior AI leadership remains well below what comparable profiles command in US financial centres. RBC built its Borealis lab to more than 900 people on exactly this foundation, and TD’s acquisition of Layer 6 seeded a research capability it has built on since. The talent is here, and it is not priced like Silicon Valley.
One caution on the AI premium: you will read about. Lightcast’s 2025 analysis of 1.3 billion job postings found that roles requiring AI skills advertise pay roughly 28% higher; Payscale’s 2026 compensation report found most employers pay no premium at all, with the uplift concentrated at senior levels. Both are true. The premium is real for genuinely scarce senior talent and largely imaginary for the resume that simply lists AI as a skill. Knowing the difference is critical.
Where the work actually gets done, and why most pilots fail
It is worth being clear-eyed about what AI is doing in our industry today, because the proven use cases will usually tell you which talent to hire first.
The clearest wins are not in the investment process; they are in productivity and research. For example, Morgan Stanley’s AI assistant gives its roughly 16,000 advisors access to more than 100,000 documents and, according to the firm’s published case study, reached over 98% adoption among advisor teams. Its companion tool transcribes and summarizes client meetings straight into the CRM. These are not moonshots. They are retrieval and summarization over a firm’s own document library, and they require applied ML engineers, a translator to drive adoption, and a compliance partner from day one.
The sobering counterpoint comes from MIT’s 2025 GenAI Divide study, which found that 95% of enterprise generative AI pilots delivered questionable measurable impact on the bottom line. The gap was almost never the technology. It was organizational. The same research found that purchased and partnered AI solutions succeeded roughly twice as often as systems firms tried to build entirely in-house, a finding regulated financial firms should sit with, because the industry has a deep instinct to build everything in a proprietary manner.
The principle that explains all of this is BCG’s “10-20-70” rule: only about 10% of the value from AI comes from the algorithms, 20% from the technology and data, and 70% from people and process. The implication for a CEO or CHRO is direct. The decision in front of you is mostly an organizational design and change-management decision wearing the costume of a hiring decision.
The five pitfalls we watch firms make
In our work in this space, we see the same failures repeat, and they are all avoidable.
- The unicorn hunt. Writing a job description that demands all three archetypes in one person: full investment credibility, deep technical command, and enterprise transformation experience. The search either stalls or ends in an overpriced hire who is wrong for the actual work. Pick the archetype whose ‘watch-out’ you can manage and build the team around the gap.
- Hiring the marquee leader too early. A Chief AI Officer is typically only warranted once AI is a core strategic lever operating at enterprise scale. Hired before the data foundation and use cases exist, that person spends a year building slides and leaves the organization.
- Hiring the leader and stopping there, or staffing only builders underneath. Without a translator to drive adoption and platform engineers to reach production, even a strong leader presides over pilots that never ship.
- Over-indexing on pedigree. Chasing big tech resumes when a business-literate leader who understands wealth management will deliver more value faster.
- Confusing clarity with slowness. Searches launched with urgency as the main objective frequently restart within twelve months, so the mandate must be defined before the search begins. But the distinction is sequencing, not pace. Once the mandate is clear and the search is launched, it must move quickly and decisively, because this talent is scarce, and the strongest candidates are almost always weighing competing processes. Clarity first, then speed.
Readiness comes before recruiting
When a client asks us to introduce them to AI talent, the most valuable first step is to confirm the organization is positioned to attract, empower, and retain that talent.
Readiness comes down to a few things. Is your data clean, accessible and governed, or will your expensive new hire spend six months on plumbing? Is there genuine executive sponsorship of the AI agenda, which McKinsey found that high performing firms are three times more likely to have, and which it identifies as a defining trait of the organizations actually capturing value from AI? Can you name two or three high-return use cases worth building? And in Canada specifically, are you positioned for the regulatory reality? The CSA confirmed in its December 2024 guidance (Staff Notice 11-348) that securities law is technology-neutral and applies fully to AI, with explicit expectations around explainability, human oversight, and avoiding “AI washing,” and CIRO has been consulting on automated advice. Governance is not a hire you make later. It runs in parallel from the first day.
Our practical guidance to clients is to sequence rather than splurge. Begin with a translator or product owner and a platform engineer tied to one concrete use case, almost always advisor enablement or research augmentation, because those are the proven starting points. Add applied scientists as the use cases prove out. Reserve the senior AI leadership hire for the point at which the work has genuinely outgrown its current home. Throughout, lean on upskilling the quants, analysts and engineers you already employ, who understand the business in a way an external marquee hire never will, and reserve external search for the genuinely scarce profiles: senior translators, MLOps, and governance leads.
And compete where you can win. You cannot out-pay a frontier lab, but you can offer proprietary data, real autonomy, and interesting problems to solve. Retention data from the AI labs themselves, including SignalFire’s 2025 talent research, shows that mission and the quality of the team, not the top of the pay band, are what keep the best people.
The conversation worth having
The firms that will pull ahead in the next few years are not the ones that hired an impressive “Head of AI” first. They are the ones that correctly diagnosed what they needed, built the foundation to support it, and sequenced their hires with discipline.
Before you ask who you should hire, ask harder questions. Do you know which archetype your firm actually needs, and which trade-off you can live with? Is your data ready to reward the person you bring in? And have you defined the mandate clearly enough that a strong candidate would stake their career on it? The firms that can answer those questions are the ones that will hire well. The ones that cannot could make costly hiring errors.
That is the conversation we would rather have with you, and where we believe a search partner earns their place: helping you define the mandate, weigh the archetypes, and structure the role before the search begins.
