Digital transformation has been a challenging process for most financial institutions. Operating models required changes — from orientations around financial products and project-based funding to customer-focused value delivery through ever-evolving experiences. Most organizations are still somewhere on this journey, learning the teaming constructs, talent strategies, and ways of working needed to iteratively create value.
But the financial services industry now stands at the threshold of a more profound shift: one that demands a different approach to transformation.
As AI and autonomous systems advance at unprecedented speed, financial institutions face a critical question: How do we capture the full potential of these technologies when previous digital transformation efforts often fell short of expectations?
The answer lies in systems thinking, a holistic approach that examines how all parts of your organization interact to achieve business outcomes.
Beyond Digitization: The New Transformation Imperative
The World Economic Forum describes the journey to true digital transformation in three distinct phases:
- Digitization — Focused on data collection, visibility, and connectivity, enabling organizations to gain real-time insights into operations.
- Intelligence — Leveraging AI technologies to analyze large datasets, find patterns, and make decisions.
- Autonomy — Creating systems that operate with minimal human intervention, capable of anticipating needs and executing solutions in real time.
Most financial institutions are still grappling with the first phase, having failed to adapt their full ecosystem to achieve the promised benefits. Instead, they’ve digitized within legacy operating model silos, creating a patchwork of digital capabilities that don’t fully connect.
The risk today is repeating the same mistake as we enter the intelligence and autonomy phases. As one financial executive told us, “Digitalization proved challenging for most financial institutions. Legacy financial product organizational models led to siloed approaches that were further exacerbated by dated and disconnected core banking systems.
“What resulted were many partial, financial product-oriented solutions rather than customer-oriented ones.”
But intelligence and autonomy are changing the world exponentially faster than digitization did. Organizations need to learn from past mistakes and adjust their approaches accordingly.
Where Financial Services AI Investments Are Going Wrong
Financial services institutions are making massive investments in AI, but these initiatives often suffer from large blind spots:
Technology in search of users. Many organizations focus on the technology rather than the opportunity space and user needs. AI is just one of many ways to address these needs, but too often it becomes a solution in search of a problem.
Failing to account for impacts across the system. When AI and autonomous solutions eliminate tasks, they free up time for new responsibilities. Most organizations have yet to form a point of view on what the target state ecosystem (people, process, and technology) should look like.
Bolt-on solutions. Rather than AI being built into the product or experience itself, it becomes another side system that isn’t integrated into the user’s experience. This creates friction rather than value.
These blind spots stem from approaching AI as a technology problem rather than an ecosystem challenge.
Systems Thinking: Reframing the AI Challenge
Systems thinking helps us understand how parts of a system interact and influence one another, contributing to the whole.
When applied to AI implementation, it forces us to consider:
- How AI and autonomous solutions impact the full ecosystem: people, processes, and technology
- What “people jobs” exist today, and how new solutions will repackage those functions
- Whether today’s workforce has the skills needed to facilitate tomorrow’s system
- How AI integrates with other employee technologies and processes
- How to prioritize, package, and release new capabilities to capture benefits throughout the journey
This approach is vital for large, public financial institutions with proven, profitable models. Changing established operations isn’t easy, but the alternative, failing to adapt, is an existential threat.
How AI Repackages Core Financial Functions
To make systems thinking tangible, let’s examine how AI changes core financial functions:
Claims Processing Transformation
Before: Litigators only reviewed high-value insurance claims to determine validity, leaving numerous lower-value claims processed with minimal analysis.
After: AI enables 100% review of all claims, flagging anomalies and spotting trends across the full portfolio.
Impact on roles: The litigator’s role shifts from reviewing individual claims to analyzing portfolio-wide patterns and collaborating with underwriting teams to optimize future strategies.
This requires analytical skills, problem-solving abilities, collaboration, and social influence. The role isn’t eliminated but elevated to higher-value work.
Customer Service Evolution
Before: Service representatives handle routine inquiries, troubleshoot problems, and connect customers with resources.
After: AI identifies and routes routine inquiries to automated or self-service solutions.
Impact on roles: Service elevates to relationship management and opportunity identification. Representatives work more closely with business units to spot life changes that warrant additional financial product considerations.
The Bigger Picture
AI doesn’t replace human work; it changes it. Jobs shift from execution to analysis, from focusing on individual cases to managing portfolios, and from reactive service to proactive engagement.
Stephen Wolfram put it well: “When you automate technical execution, what becomes important is not figuring out how to do things — but what to do. And that’s more a story of broad knowledge and general thinking than any kind of narrow specialization.”
Enablement: The Missing Link in AI Success
For AI initiatives to deliver real value, organizations need effective enablement strategies, an important yet often overlooked element.
Here’s how to approach it:
Understand the current and target state ecosystem. Fully map how people, processes, and technologies interact today and how they need to evolve as AI capabilities mature.
Apply the strangler pattern approach. This technique, borrowed from technology transformation, identifies critical parts of the system and replaces them piece by piece until achieving full transformation. The same approach works for people and processes.
Build enablement into products. AI can contextualize learning for employees and end users at the moment they encounter change, spotting behaviors that lead to suboptimal results and intervening in real time.
Recognize this is evolution, not a project. Treat transformation as continuous evolution rather than a one-time initiative with a defined end state.
The World Economic Forum projects that by 2030, nearly 60% of the workforce will need significant upskilling. Organizations that plan for this workforce evolution alongside technology implementation will capture far greater value from their AI investments.
One Action to Take Tomorrow
If you’re a head of product or a business unit leader at a financial institution, you can take a single action to de-risk your AI initiatives and ensure they generate lasting value:
Focus less on the technology and more on what must be true for internal and external users to achieve the objective:
- Gain an understanding of how people, processes, and technology interact in the current state.
- Map the impacts future changes will have across the ecosystem.
- Identify the parts of the ecosystem that need to iteratively evolve to achieve full transformation.
- Accept that this is not a project but a continuous evolution.
The financial institutions that thrive in the intelligence and autonomy phases will apply systems thinking to the full transformation challenge, considering people, processes, and technology as an integrated whole.
Method’s Approach to Systems-Based Transformation
At Method, we believe successful AI transformation begins with clarity and ends with outcomes.
Our approach begins with a deep understanding of your current ecosystem, mapping how value is delivered by people, processes, and technology across your organization.
We then use insights from both data and users to co-design improved workflows that enable business objectives. We incorporate AI and automation where they deliver value rather than wherever they’re technically possible.
Most importantly, we recognize that technology alone doesn’t drive transformation. Our work includes developing the organizational readiness roadmap aligned with your business goals and AI maturity, incorporating change management strategies to ensure successful adoption and lasting impact.
This systems thinking approach delivers AI-powered experiences that users embrace and trust, driving measurable business outcomes rather than just technical implementation.
Looking Forward: The Systems Approach to AI
The journey from digitized to intelligent financial services requires more than just advanced technology. It demands a holistic view of how all parts of your ecosystem work together.
By applying systems thinking to your AI initiatives, you’ll unlock value that siloed approaches just can’t achieve.
Ready to explore how systems thinking can transform your AI approach? Contact Method today.