LinkedIn Live Recap: Turning AI into a Strategic Asset
Artificial intelligence has reached a defining moment in financial services. After several years of experimentation, pilot projects and cautious optimism, firms are now grappling with a more pressing question: how do you turn AI from an interesting tool into a genuine strategic asset?
That question sat at the heart of a recent conversation between Freya Scammells, Head of AI Practice at Caspian One, and Kim Richards, a seasoned financial services strategist who previously led generative AI initiatives across capital markets. What unfolded was a candid look at the realities behind AI adoption, the practical challenges, the organisational shifts, and the cultural tension sitting just beneath the surface.
Freya opened the discussion with a clear observation: many firms have finally crossed the threshold into production AI. “We’re almost beyond pilots,” she said, pointing to clients who are no longer asking whether they should use AI but how to use it in a way that creates tangible change. The move from prototype to performance has been long awaited, but it brings a fresh wave of complexity.
Kim agreed and described the market as “on the cusp” of a maturity shift. Experimentation has run its course. Leaders are looking at AI with more defined expectations, particularly around measurable return. “The time of pursuing AI initiatives with limited focus on ROI is coming to an end,” she said. It was a grounding reminder that the excitement around AI now needs to be matched by financial discipline.
From Tactical Experiments to Strategic Architecture
A recurring theme throughout the discussion was the distinction between using AI and using it well. Freya challenged Kim on what separates a tactical deployment from a strategic one, and Kim broke it down with compelling clarity.
In many organisations, early AI adoption begins in isolated corners; a re-engineered workflow here, an automated task there, or a new tool adopted by a curious team. These efforts are not without value, but they sit within narrow boundaries. They are, in Kim’s words, “limited in scope… off-the-shelf solutions that already have traction,” applied where local leadership happens to have appetite.
The shift toward strategy happens when firms start asking broader, business-first questions:
Where can AI meaningfully reduce risk?
Where are humans overloaded or inconsistent?
Where does speed directly influence revenue or resilience?
This is where AI becomes more connected to real outcomes. Freya noted that the most forward-leaning organisations begin by framing AI against concrete business problems rather than starting with the model. Kim echoed this, describing how leading firms systematically map AI to both customer-facing improvements and the “invisible but essential” internal functions, such as compliance and transaction processing.
Yet the most interesting part of the discussion came when Kim spoke about firms that are pushing even further. Some are now exploring what a fully AI-native function could look like. She referenced emerging thinking around transforming HR, finance or other horizontal functions into agentic platforms. It’s ambitious, she admitted, but ambition is increasingly becoming part of the strategic playbook. “Dream big, then work backwards,” she said - a philosophy many firms quietly recognise but rarely say aloud.
AI Ownership Is Evolving… and So Are The Roles
Freya highlighted another trend shaping the strategic landscape: AI ownership is shifting deeper into the business. Historically housed in IT, AI strategy is now increasingly led by business leaders who understand product, operations and customer dynamics.
Freya’s question about whether AI becomes more strategic when owned by the business sparked an important reflection from Kim. More executives, she said, are starting to ask who is responsible for articulating the AI vision to the C-suite and even the board. The answer is no longer purely technical. The role requires someone who understands the guts of the business and can translate complexity into organisational direction.
This shift is also creating new hybrid roles. Freya noted the rise of technically minded business analysts and AI strategists, people who sit at the intersection of deep domain knowledge and emerging technology. Kim agreed, pointing to examples of emerging C-Suite roles. Not always a dedicated role, sometimes a dual hat, but always someone responsible for connecting the organisational reinvention required for AI to take root.
Where Financial Firms Are Finding Value Today
While much of the discussion centred on strategy, both speakers offered grounded examples of where real value is emerging.
Client-facing functions continue to see strong momentum. AI is helping relationship managers prepare for meetings, automate follow-ups and synthesise client information. Freya described the long-standing “Holy Grail” of a true 360-degree client view, something that now feels more achievable with AI’s ability to bridge siloed data. Sales teams are also seeing early benefits, with AI taking on the administrative workload that has historically limited productivity.
But some of the most compelling opportunities sit within internal functions, particularly in areas of high manual effort or heavy outsourcing. Kim referenced growing interest in reducing third‑party spend and in using AI to reclaim workflows previously handed off to external vendors. Risk reporting, transaction processing and document-heavy areas are also proving ripe for AI innovation. These functions may be less glamorous than front-office deployments, but they often unlock the fastest path to ROI.
The Human Factor: The Largest Barrier to AI at Scale
For all the strategic optimism, both speakers were clear: the biggest barriers to successful AI adoption are human, not technological.
Workforce anxiety continues to shape perceptions. Kim described the fear around employment impact as “very real,” not only for individuals but for managers thinking about the future of their teams. Freya added that trust and transparency, or the lack of them, can amplify resistance. If an organisation already struggles with clear communication, AI initiatives tend to expose those weaknesses even more.
Leadership hesitation compounds the problem. Many business leaders are still building confidence with AI, and the regulatory environment adds pressure. Freya noted that leaders can feel exposed if they don’t fully understand how a model works or how to justify decisions when scrutinised.
Kim’s advice on this topic was refreshingly direct. Honesty and forward planning matter more than messaging. If a transformation will reshape roles, pretending otherwise only deepens distrust. Instead, she emphasised a “human-first” approach: bring senior leaders into the process early, design realistic timeframes, and treat transformation as an opportunity to redeploy talent rather than eliminate it. “Seventy-five per cent of savings,” she said, “should come from roles you don’t need to hire in the future, not from exiting people.”
AI Literacy Will Define the Next Phase
Perhaps one of the most thought-provoking moments came when Kim discussed the future of workforce capability. As models become more agentic, organisations will need people who can supervise, evaluate and work alongside AI systems, a skillset fundamentally different from simply using tools.
She spoke about the need to teach employees how to interpret AI output, challenge it, and understand new error patterns that differ from human reasoning. Freya agreed, highlighting that learning and development functions will play a pivotal role as organisations redefine how intuition, decision-making and oversight work in an AI-augmented world.
A Clear Message for Leaders
As the conversation drew to a close, both Freya and Kim offered a concise set of takeaways.
Leaders should hold a long-term AI-native vision, even if the path there is incremental. They should anchor AI decisions in tangible business value and expand thoughtfully from early successes. And above all, they should recognise that AI transformation is deeply human. Technology may be the catalyst, but leadership, culture and capability determine the outcome.
Freya ended with a reminder that captures the spirit of the entire discussion: AI’s success increasingly depends on human collaboration and clear leadership. The technology is ready, but the real work lies in how organisations choose to shape it.
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Disclaimer: This article is based on publicly available, AI-assisted research and Caspian One’s market expertise as of the time of writing; written by humans. It is intended for informational purposes only and should not be considered formal advice or specific recommendations. Readers should independently verify information and seek appropriate professional guidance before making strategic hiring decisions. Caspian One accepts no liability for actions taken based on this content. © Caspian One, 2026. All rights reserved.
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