There is No AI Without Adoption: A Large Asset Manager Understands
Using job postings to interpret a company's strategy and priorities
The product angle for internal development is an insightful twist. It speaks to adoption and outcomes, not just building better mousetraps. —Me
Help Wanted
A job posting from a large asset manager landed in my inbox last week.
[The firm] is looking for an AI Product Leader to spearhead [our] enterprise-wide AI product strategy. This critical role will own, manage, and prioritize all AI initiatives at the intersection of our business and technology, working across Operations, Finance, Investments, Asset Management, and Other teams. You will be instrumental in streamlining our technological capabilities, building AI roadmaps, driving prioritization, and managing successful rollouts of impactful AI solutions.
This is brilliant. It shows a realization that regardless of the quality of your data and technology, it’s all for naught if your people won’t use it.
I have some experience playing at the intersection noted, and if I have learned anything, it’s that you need cooperation and a common understanding if you want the adoption needed to achieve the outcomes you are shooting for. The challenge is that each side has their own north star, measures of success, and language through which they operate. And this is before you start navigating vertically from the C-suite through management down to the end users. Each of those layers has their own priorities and considerations. It gets complicated.
What makes this role compelling is its capacity to take on a broad mandate and focus 24/7 on design, communication and execution. That capacity to focus is critical. Everyone else has their day jobs—the ones they were hired for; the ones they have been doing successfully, and, highly likely, spending more than 40 hours a week at before being asked to take on this AI thing.
That focus becomes even more critical when you consider what this role actually requires: the ability to see across competing and complementary needs simultaneously, identifying the common abstractions that create leverage and do so without losing sight of the details that make or break adoption. It takes a combination of strategic thinking, tactical execution and a fluency across domains that demands both breadth and depth. Reach for the stars.
Responsibilities & Qualifications
There is a lot to the role, so there is, correspondingly, a lot laid out in these two sections. Eighteen bullet points, in fact. To their credit, they offer a coherent listing of activities and capacities needed to manifest them, providing a roadmap that runs through data science, engineering, behavioral science, and the many facets of the business itself.
A summary from the specs:
As the AI Product Leader, you will be the driving force behind shaping our technical AI roadmap in close partnership with engineering. Your mission will be to ensure the development and deployment of impactful, scalable AI solutions that accelerate productivity and innovation across [the firm]. The ideal candidate will possess a strong foundation in product management and/or engineering, with a proven ability to translate complex business needs into cutting-edge technical solutions. You’ll navigate [the firm’s] diverse business units, identifying opportunities for AI to deliver significant value and championing a user-centric, agile approach to product development.
In layman’s terms, this role will lead the AI roadmap, working hand-in-hand with engineering to turn ideas into real products. The goal is simple: build and roll out AI tools that boost productivity and spark innovation across the firm. To do that, leadership needs both product and technical know-how, plus the ability to turn messy business problems into clear outcomes. They’ll work across all of the firm’s businesses to spot where AI can add the most value and make sure the tools are built with users in mind and improved quickly through feedback.
The core elements that jump out are:
Impactful: Perhaps this is obvious, but I still give credit for beginning with the end in mind. Understanding markets and harvesting returns have always been my main thing—technology, including AI, is a means to a business end.
Scalable: GenAI makes prototyping and building functional proof of concepts demonstrably easier. It is easy to be seduced into thinking they can replace existing tools, or be rolled out more broadly. But at this point they are fragile, and when they break the signs can be easy to miss. Or not. Hidden within scale is the issue of security. There is a whole post to be written on this.
Innovate: The I-word can be a cliche—we had an Innovation Day at my old shop twenty years ago. But what I read here is an understanding that like ideas, solutions need to be pursued with conviction, but held loosely. The competencies of the technology are simply evolving too fast to build otherwise.
Translate: This may be the secret sauce. As noted, there is a rat’s nest of north stars, objectives and languages that must be navigated and reconciled. People frequently see differences when there is mostly similarity; just as often, there are stark differences masked by common misunderstandings. Being effectively multi-lingual is table stakes.
Engineering: For all the advancements in data science, enterprise scale AI sits on a foundation of sound engineering fundamentals. Security, transparency, memory, search, agents and tools all need to be deliberately designed and integrated. Engineers do this.
Product Management: This recognizes the softer skills that raise the likelihood that the people whose problems you are solving will participate in the building, and ultimately, use the AI solutions provided. Product oriented leadership helps overcome the barriers of adoption—inertia, fear, misunderstanding, and the lack of time.
Product Mindset
The tell that this unnamed institution “gets” the critical importance of adoption is their explicit framing of the role as Head of AI Product. Including Product in the job title emphasizes the human dimension. It’s worth noting they are also hiring someone to lead their AI Platform development, where the expectations and requirements are appropriately focused on the technical. This separation shows they understand the difference between building capabilities and driving adoption, and that both require dedicated expertise.
I spent over two decades in the front office of a large hedge fund building and innovating with technology in support of my day job. If you had asked me I would have said I was creating capabilities. The product was our returns. It wasn’t until I was readying myself to run product at a fintech that I came to appreciate that much of the process behind creating those capabilities was rooted in a particular mindset. Namely, working backwards from the outcomes wanted, identifying where we could get the most leverage, ease of use, and collaboration.
Coming at it from the direction of thinking “we can build this and you should want it” doesn’t work. I’ve had to step over too many corpses—some of my own making—of projects that failed to launch, reach escape velocity, or land where hoped. Most were well-intentioned, some were vanity projects, none were adopted.
Getting Started
Some public breadcrumbs are available, but I don’t really know the current state of play at this company. That said, the new AI Product Leader will undoubtedly inherit a complex landscape, but there’s a logical sequence to navigating it successfully.
Understand the Third Rails
In a highly regulated industry such as they’re in, the first job isn’t identifying opportunities but staying out of trouble. Age teaches you that. What simply can’t be done? What data can’t leave? What’s allowed in? Which processes require human sign-off? Where does compliance draw hard lines? What are acceptable tradeoffs? These are design parameters that will shape every solution. Better to know upfront so they can be incorporated in the components with which everything else will be built. Solving the security and regulatory requirements at the beginning will save time, effort and legal fees down the road.
Define and Prioritize Outcomes
Start with the business outcomes that matter most: faster decision-making, reduced operational risk, improved communication, whatever moves the needle for this firm. Identify the common abstractions that when addressed, offer the greatest operational and technical leverage. Then work backwards to identify which AI interventions could realistically impact those outcomes. This isn’t about what’s technically possible, per se, it’s about taking what’s possible to accelerate productivity and innovation across all business units to generate higher returns.
Provide Requirements, Resources, and Cover
Once you’ve identified the highest-leverage opportunities, the job becomes enablement. Internal stakeholders need to provide clear requirements and constraints around their particular use cases. Developer resources need to be secured (data access, compute power, but also time and attention from subject matter experts). Most importantly, provide air cover from organizational politics or when experiments don’t work out. Innovation requires permission to fail fast and learn quickly.
Experiment, Implement, Innovate
While there is experimentation and innovation taking place at every step, I’m referring here to a more organizational and sequential context. Large, successful organizations do not have the luxury of revolutionizing how they operate overnight. Better to start by learning what can and cannot be done on representative but clear use cases. At this point, you have many siloed efforts—many unknown even to management.
Next comes the implementation phase, which is where this company is entering. At this stage, you bring in common infrastructure, standardized tooling, governance frameworks, and user feedback systems needed to scale safely and predictably. Executed well, management and users will be delighted, but still able to recognize the organization they work for.
With time, the company will gain deep competencies and a clearer understanding of where AI can best add and capture value. Most importantly, it will become part of the company’s culture. Add to this the ever-growing capabilities of the technology, and there will be opportunities to reimagine how business is done. Just don’t start with that.
Execute the Work
The day-to-day comes down to signal extraction and communication. Identify what’s working (and what isn’t) from user behavior, not just user feedback. Then communicate those insights up, down, and across the organization in language each audience understands. The C-suite cares about business impact; engineers care about technical feasibility; end users care about their daily workflow. Same data, different stories.
Success in this role won’t be measured by the sophistication of their AI, but rather by how much the organization’s behavior changes for the better and the outcomes that result.
Wrapping Up
If you are reading this it’s safe to assume you already believe that we are currently in an extraordinary time. Just from when I entered the workforce, the technical gains starting with the PC have compounded to a level that is hard to comprehend. And now we have GenAI. It can feel like the growth curve is pointing straight up.
With this progression comes hyperbole. It is easy to get swept up in the emotion, or conversely, become overly skeptical. Both reactions are understandable but neither is productive, certainly in the extreme. Balance needs to be struck across risks, rewards and human nature. Part of that balance comes from recognizing the scope of the technology needed paired with an understanding of the needs and experiences of those who will be using it. This is where the worlds of engineering and product meet. The most progressive organizations recognize this duality, and are acting accordingly.
When wanting to understand what a company is actually doing, rather than just saying, I have found job postings invaluable. They show where the company is spending their money; they reveal where management sees their future, and who and what is needed to get there. I hope you have found this particular walk through revealing. Please reply and comment with your thoughts.
I’ve purposefully avoided including the name of the asset manager to avoid the distraction that comes with iconic names. I can tell you, though, it’s not Tudor. That would be too easy.
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