Financial services firms are pouring resources into AI. New applications keep appearing, pilots get launched, and innovation labs grow.
But in the boardrooms and executive teams we advise, a tougher reality often surfaces: AI transformation isn’t getting stuck due to the technology itself.
The real hurdles come from outdated operating models, entrenched power structures, and misaligned leadership.
The core change needed is organizational, not just computational.
The Pitfall of Treating AI as an Add-On
Many firms approach AI like an overlay—something to bolt onto their current setup. They might create a new department, form a committee, or appoint a Chief AI Officer.
Yet AI doesn’t fit neatly into traditional boxes. It spans across areas like risk management, operations, compliance, sales, underwriting, asset management, and customer interactions.
In Financial Services Firms, insurance companies, and investment firms, we keep seeing the same conflicts:
- Risk teams and innovation groups operating at mismatched paces
- IT oversight bogging down key deployments
- Business units guarding their profit centers
- Data scattered and controlled by different parts of the organization
AI highlights flaws that were already lurking in the structure.
It pushes boards to confront a question they’ve long delayed: “Is our setup built for smart automation, or is it still geared toward old-school hierarchies?”
Choosing an Operating Model: Centralized, Embedded, or Hybrid?
No one model fits every firm, but three patterns stand out:
- Centralized AI Hubs: These offer tight control and pooled expertise, but they can slow down adoption across the business.
- Embedded AI Teams: These sit closer to day-to-day operations, allowing faster rollout, though they risk creating silos and inconsistencies.
- Hybrid Approaches: A core governance framework combined with decentralized implementation, balancing oversight with agility.
These choices carry big governance weight. Boards need to settle on:
- Who handles model-related risks?
- Who balances compliance demands against innovation goals?
- Who decides how AI budgets get allocated?
- Where does final responsibility land?
Without clear answers, AI efforts turn into costly trials instead of real competitive edges.
Talent: The True Bottleneck
The biggest constraint isn’t hardware like GPUs—it’s capable leadership.
Financial firms are vying with tech giants, startups, and Big Tech for AI engineers and data experts.
But the real shortage isn’t just in technical skills. It’s in leaders who can bridge gaps.
The firms making genuine headway have:
- CEOs who grasp AI beyond the buzzwords
- CFOs who weave AI into financial planning
- Chief Risk Officers who update risk approaches ahead of time
- Boards building real tech savvy
True AI progress demands executives who can connect code with capital, and governance with fresh ideas.
That mix is still hard to find.
The Power Dynamics at Play
AI reshapes who makes decisions.
For instance, if credit decisions get automated, who bears the blame for errors? If AI helps with investment choices, how does that affect fiduciary duties? If compliance shifts to predictive tools, what happens to reporting chains?
AI doesn’t just streamline workflows—it reallocates authority. And shifts in power always tie back to governance.
To keep things balanced, it’s worth noting that while AI can boost efficiency, it also introduces risks like over-reliance on algorithms, potential biases in decision-making, and job disruptions. Firms must weigh these downsides against the upsides.
Lessons from the Field
Take one global financial institution we worked with, their early AI push aimed at cutting costs and smoothing operations. The board backed a central lab and hired top talent.
After two years, outcomes were mixed.
The breakthrough happened when the board recast AI as a full-company overhaul, not just a tech project.
They assigned clear responsibilities at the top, tied AI goals to key performance metrics, and reworked ties between the CIO, CRO, and business leaders.
That’s when AI shifted from testing to everyday use. The tech was ready long before; the organization wasn’t.
The Board’s Role
In financial services, ignoring AI isn’t an option—but neither is rushing in without solid structure.
Boards that view AI purely as an IT spend may miss its broader ripple effects. Those that see it as a governance puzzle—rethinking decision processes, accountability, talent strategies, and funding—stand a better chance of gaining lasting benefits.
AI isn’t a peripheral effort. It’s a way to probe your operating model’s strengths and weaknesses. In the end, it’s a measure of how mature your leadership really is.
