The financial services industry stands at a crossroads. Artificial intelligence has evolved from a futuristic concept to an urgent strategic imperative. Whether you’re managing billions in client assets or serving millions of retail customers, the message is clear: embrace AI now, or risk being left behind.
If this feels familiar, it should. We’ve been here before.
Echoes of the Internet Revolution
In the mid-1990s, banks faced a similar inflection point when the internet emerged as a “strategic imperative.” Wells Fargo became the first U.S. bank to add account services to its website in 1995, with Presidential Bank opening the first internet bank accounts that same year. By 2001, over 2,000 banks had transactional websites, skyrocketing from near zero just a few years earlier.
Back then, banks initially treated the internet as “simply another channel to distribute bank-related information” before realising its revolutionary potential. Sound familiar? Today’s AI adoption follows an eerily similar pattern — while many organisations sense the revelatory nature of AI, they are still trying to inject it as a tool for operational improvements rather than fundamental transformation.
The parallels are striking, and so are the consequences. Early internet banking pioneers had to overcome significant cultural resistance but it paid off. By 2006, 80% of U.S. banks offered internet banking — those who waited too long found themselves losing both money and market share. You know many of those late adopters today as subsidiaries of faster adopting banks.
In this article, we’re going to look at two titans of finance — an asset manager and a retail bank – racing toward AI transformation. While they share the same starting line and face similar pressures, their approaches, advantages, and requirements are different. Will it stall one while the other takes the lead? Let’s find out!
The Starting Gun: Why AI Matters Now
I’ve heard it from my boss and it’s very likely you’ve heard it to: We’ve got to be doing more with AI. Both asset managers and retail banks are feeling the heat, much like they did during the internet revolution. In the 1990s, institutions worried about “clicks-and-bricks euphoria” and feared being disrupted by internet-only banks. Today, they face similar pressures from AI-native fintech startups and tech giants.
The asset manager faces fee pressure, the shift toward passive investing, and ultra-wealthy clients demanding increasingly personalised services. Their advantage? Deep pockets, sophisticated teams, and clients who appreciate cutting-edge innovation.
The retail bank confronts nimble fin techs and tech giants who’ve made AI-powered experiences the new normal. Their edge? Massive customer data, standardised processes, and urgent competitive pressure that drives fast decision-making.
The asset managers expect “significant or transformative changes” from AI in the short term. Meanwhile, even smaller retail banks recognise that AI offers “massive gains in efficiency, risk management, and customer experience.”
The race is on. Who will cross the finish line first?
Embrace AI now, or risk being left behind.
Phase 1: The Opening Sprint (Months 0-6)
The Asset Manager Takes Early Strategic Ground
The asset manager assembles a cross-functional AI task force within weeks. Their advantage shows immediately: deep resources allow them to hire AI specialists and engage premium consultants. They zero in on portfolio analytics and client reporting enhancements, targeting their most lucrative relationships first.
But there’s a catch. Their sophisticated client base means higher stakes — one AI misstep with a $100 million client could be catastrophic. The asset manager moves deliberately, emphasising augmenting human expertise rather than replacing it. “We can’t damage very personal client relationships,” warns their Chief Investment Officer.
The Retail Bank Charges Hard on Volume
The retail bank takes a different approach, launching multiple pilot programs simultaneously. Customer service chatbots, credit underwriting improvements, fraud detection — they’re throwing everything at the wall to see what sticks. Their advantage? Standardised customer interactions mean they can be more aggressive with automation from day one.
Their challenge? Legacy systems spanning decades of acquisitions. While the asset manager’s infrastructure is complex, the retail bank’s is sprawling. They’re racing to break down silos between branch systems, online platforms, and card networks while the asset manager integrates more focused but equally critical data streams.
Data: The Foundation of Everything
Both institutions quickly discover that AI is only as good as the data feeding it, and only as helpful as the data is easy to access. The retail bank must start to break down silos between branch systems, online platforms, and card networks. Meanwhile, the asset manager must integrate market data, research, and client portfolios into unified repositories (or make it so separate repositories are easy to access cross reference). Legacy systems often demand urgent upgrades or cloud migration – a reality check that hits hard but sets the stage for everything that follows. For both, existing Data Management and Governance shifts toward a more targeted goal of incrementally enabling AI, ensuring data is visible, accurate, standardised, and accessible when and where it’s needed.
Cultural Transformation Begins
Perhaps most critical, this phase involves cultural preparation – a challenge that mirrors the internet banking revolution. In the 1990s, banks struggled with “a striking difference between the culture of the banking community and Internet users.” Today’s AI transformation requires a similar cultural leap, building an “AI-first” mindset across the organisation.
Leading institutions ensure AI initiatives are business-led, not just technology-driven, with business sponsors owning and co-owning projects to align them with real business needs. This mirrors lessons from the internet era: banks that successfully integrated online channels with traditional delivery methods gained strategic advantages, while those that treated them as separate initiatives struggled.
Early Scorecard: Neck and Neck
The asset manager’s smaller datasets give them precision advantages, while the retail bank’s massive customer data offers breadth and pattern recognition opportunities.
Cultural transformation begins at both institutions. The asset manager focuses on building trust with portfolio managers who fear AI might undermine their expertise. The retail bank battles resistance from branch staff worried about job displacement with “doing more with better together” co-piloting training and outreach. Both are learning the same lesson from the internet era: technology adoption without cultural change leads to failure.
AI isn’t just about technology… it’s about reimagining how work gets done.
Phase 2: Finding Their Rhythm (Months 6-12)
The Retail Bank Hits Its Stride
The retail bank deploys their first customer-facing chatbot and immediately sees results: 20% reduction in call centre volume for routine inquiries. Their AI-based fraud detection catches 15% more suspicious transactions than the old rule-based system. The credit underwriting pilot approves qualified borrowers 30% faster.
Success breeds confidence. The retail bank’s standardised processes become a competitive advantage — what works for one customer segment can quickly scale to millions. Their pilot projects start connecting: the chatbot’s natural language processing enhances their credit application review system.
The Asset Manager Plays the Long Game
The asset manager’s pilots are fewer but deeper. Their AI system analysing earnings call transcripts provides investment insights that help one portfolio outperform its benchmark by 80 basis points. A wealth client portal allowing natural language portfolio queries receives rave reviews from their most demanding clients.
But the asset manager faces a unique challenge: their investment managers remain sceptical. Unlike the retail bank’s clear operational metrics, the asset manager must prove AI enhances rather than replaces human judgment in high-stakes investment decisions.
Process Evolution Begins
This phase reveals a crucial truth: AI isn’t just about technology – it’s about reimagining how work gets done. Call centre agents learn to handle AI escalations. Risk officers integrate AI outputs into credit decisions. Investment teams start incorporating AI-generated analysis into committee meetings.
Learning Through Failure
Not every pilot succeeds, and that’s perfectly fine. Both institutions must be prepared to iterate or stop projects that don’t meet targets while doubling down on those that do. This phase is a blame-free, safe environment for learning from failures and refining approaches. This discovery period has knock-on effects, as studies show these sorts of blame-free fail-fast, fail-often, fail-cheap exercises can actually cleanse workplaces from toxins such as blame-culture and territoriality.
Governance Gets Real
As AI prototypes go live, governance practices move from theory to practice. Each pilot undergoes rigorous risk review: Are recommendations explainable? Is there potential bias? Are we protecting customer privacy? The asset manager ensures portfolio managers validate AI suggestions rather than acting blindly. The bank tests credit models for fairness to avoid unintended consequences, such as discriminatory lending.
Scorecard: Asset Management Takes the Lead
While retail banks have the breadth and standardised processes to go hard at AI Innovation, entrenched culture and siloed data stores cause slow-down and lack of adoption. Meanwhile, Asset Management has fewer but more bottom-line impactful success stories and, as they are rolling out primarily to resources focused on the bottom-line, this is more easily and readily adopted.
When we see the power of volume, we see faster and more widespread adoption and use of AI.
Phase 3: Acceleration (Months 12-24)
The Retail Bank Finds Top Gear
The retail bank’s proven solutions scale rapidly across all channels. Their chatbot expands from mobile app to website to social media. AI-driven credit scoring integrates into all consumer lending. Fraud detection monitors every transaction in real-time.
Their transformation touches everything: customer onboarding with AI-powered ID verification cuts account opening from hours to minutes. Customer service shifts to AI handling tier-one inquiries while humans focus on complex cases. New applications emerge by repurposing existing components. Crucially, these successes along with repeated adoption support and integration into day-to-day processes has helped shift the culture. Change is in the air and it’s now seen as an opportunity.
The Asset Manager's Sophisticated Surge
The asset manager scales their AI tools across all portfolio management teams and asset classes. What helped equity analysts now serves fixed-income and multi-asset teams. Client-facing enhancements emerge: AI-driven personalisation in investment reports and robo-advisory offerings for smaller clients.
Portfolio management workflows transform. Routine trading becomes algorithm-centric, allowing portfolio managers to focus on strategy and client relationships. Investment committees begin meetings with AI-generated scenario analysis, enhancing rather than replacing human led decision-making.
The Infrastructure Race
Both institutions finalise migrations to modern, cloud-heavy hybrid platforms. The retail bank’s infrastructure serves millions of customers across diverse products, requiring consistency at massive scale. The asset manager’s deployment involves fewer touchpoints but deeper integration into complex investment processes.
Cultural resistance largely fades at both institutions. The retail bank’s staff see competitive advantages in AI-enhanced capabilities. The asset manager’s relationship managers discover AI actually strengthens client conversations by providing deeper insights.
Scorecard: Different Strengths Emerge but Retail Pushes to the Front
The retail bank leads in breadth and customer impact: their AI touches millions of daily interactions.
The asset manager leads in depth and sophistication: their AI influences billion-dollar investment decisions.
But here we see the power of volume. With culture no longer an issue at the retail bank, and integration of data underway, we see faster and more widespread adoption and use of AI.
Phase 4: The Final Sprint to AI-Native (Months 25+)
The Retail Bank's Volume Victory
The retail bank deploys AI agents capable of complex multi-step tasks: financial coaching pulling data from various accounts, debt restructuring negotiations within ethical bounds. They launch banking-as-a-service with AI-driven risk engines for other companies.
New revenue streams emerge. The retail bank monetises their AI infrastructure by offering white-label solutions to smaller banks — echoing how successful internet banks became technology providers. Their massive scale creates network effects: better data leads to better AI, which attracts more customers, generating even better data.
The Asset Manager's Precision Play
The asset manager offers truly bespoke, AI-crafted portfolios where each client’s holdings continuously adjust within agreed parameters. Predictive analytics anticipate client needs and market shifts, enabling proactive strategies impossible before AI.
They develop new revenue streams too: AI-driven indices and data analytics services as value-adds to clients. Their sophisticated approach attracts institutions seeking cutting-edge portfolio management, expanding their total addressable market beyond traditional wealth management.
Shaping Tomorrow’s Business Landscape
By this stage, these early adopting institutions aren’t just complying with AI governance standards – they’re helping shape them. They work with their respective regulators on best practices, maintain board-level AI oversight, and strive for transparency about AI use to maintain customer trust and market their enhanced capabilities.
Scorecard: Photo Finish!
As both institutions mature into AI-native enterprises, something interesting happens: they both win! Just differently.
The retail bank dominates in operational efficiency and customer reach. Their AI infrastructure processes millions of transactions, serves countless customers, and generates multiple revenue streams. They’ve become the efficiency champion.
The asset manager excels in sophisticated decision-making and premium client service. Their AI enhances complex investment strategies, deepens client relationships, and opens new markets for institutional-grade services. They’ve become the sophistication leader.
AI capabilities are improving exponentially. The gap between early adopters and laggards isn’t just growing. It’s growing faster every quarter.
The Finish Line: Winners, Losers, and the Brutal Reality
Here’s the uncomfortable truth about the AI transformation race: there are clear winners and losers, and the gap between them is growing exponentially.
Both our asset manager and retail bank crossed the finish line successfully, but they ran different races toward the same destination: becoming AI-native organiations capable of thriving in the next era of finance.
The Winners: Early Adopters
Our two institutions succeeded because they started early and moved strategically. They’re now capturing market share, reducing costs, and opening new revenue streams that their Web 2.0 competitors can’t match. The retail bank processes loans in minutes while competitors take days. The asset manager provides insights that consistently outperform benchmarks while others rely on outdated analysis.
The Losers: No Things Come to Those Who Wait
But what about their competitors who are still “exploring AI initiatives” or “forming committees to evaluate potential use cases”? They’re falling behind fast, just like banks that waited too long to embrace internet banking in the 1990s.
The asset management firm still using Excel for portfolio analysis can’t compete with AI-driven scenario modelling. The regional bank still processing credit applications in PDF is losing customers to AI-powered instant approvals. The wealth manager still relying on quarterly reports can’t match AI-enabled real-time portfolio optimisation.
The ones who don’t lace up their running shoes face a brutal reality: the starting gun has fired. The longer they wait, the harder it becomes to catch up. Their AI-native competitors are building data advantages, network effects, and operational efficiencies that compound over time. Just as internet-era late adopters often became acquisition targets, today’s AI holdouts risk similar fates.
What made both institutions successful wasn’t being perfect. It was being committed to transformation.
Exponential Acceleration
What makes this transformation particularly unforgiving is
the acceleration effect. Unlike the gradual adoption of internet banking over a
decade, AI capabilities are improving exponentially. The gap between early
adopters and laggards isn’t just growing — it’s growing faster every quarter.
The asset manager and retail bank in this story aren’t just
competing with each other. They’re setting new industry standards that force
everyone else to catch up or fall behind. Their AI-enhanced capabilities become
the new baseline expectation from clients and customers.
The Real Victory: Transformation, Not Perfection
What made both institutions successful wasn’t being perfect. It was being committed to transformation.
They made mistakes, killed failed pilots, and learned through iteration. But they never stopped moving forward.
The institutions that will struggle aren’t those who made mistakes along the way, they’re those who never started running at all. In the coming years, we’ll see AI-native organisations pull irreversibly ahead of AI-curious ones, just as internet-native banks eventually absorbed traditional institutions that treated digital as an afterthought.
Your Race Starts Now
Whether you’re managing assets, serving retail customers, or operating in any other centre of finance, your race has already begun. The question isn’t whether you’ll face AI transformation, it’s whether you’ll be a winner or a loser in the reshaping of financial services.
The good news? You don’t need to be first across the finish line. You just need to start running, run smart, and keep moving forward.
Need some help getting started? Get in touch below.
Sean Russell
Managing Principal, Ortecha
contentS
- Introduction
- Echoes of the Internet Revolution
- The Starting Gun: Why AI Matters Now
- Phase 1: The Opening Sprint (Months 0-6)
- Phase 2: Finding Their Rhythm (Months 6-12)
- Phase 3: Acceleration (Months 12-24)
- Phase 4: The Final Sprint to AI-Native (Months 25+)
- The Finish Line: Winners, Losers, and the Brutal Reality
- Exponential Acceleration
- The Real Victory: Transformation, Not Perfection
- Your Race Starts Now
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