From Manual Processes to Intelligent Automation - The Next Era of Operating Models

For decades, financial firms ran on old-school business models - think teams tracking data in spreadsheets and filing paper records based on policy manuals. It worked, though barely, as markets grew more complex with insane data volumes. These legacy systems were slow, limited in scale, and siloed knowledge across teams.

The winds changed, blowing in an era where AI, automation, and digitalization are now must-haves, not nice-to-haves. Per a global survey, 90% of large companies will have integrated AI by 2025, with over 60% of business apps powered by it. The shift from manual to intelligent automation is seismic. Rigid structures make way for agile methodologies. Data silos transform into integrated analytics.

For APAC's bustling finance sector, especially staying ahead of the curve is imperative, not just keeping up. The regional digital banking market alone will exceed $8 billion by 2026, rising at 20% CAGR from 2022 per Allied Market Research. The message is clear - evolve now or risk extinction. Efficiency isn't just cost-cutting but boosting capabilities to deliver at light speed. Future readiness means anticipating shifts and needs before competitors, leading change.

As we explore the next-gen operating models, we'll see how automation, AI, and digital convergence don't just reshape operations but redefine financial services leadership. This promises an extraordinary shift - from the manual and mundane to the automated and amazing. Leaders who navigate this journey wisely will unlock unmatched value.

The Strategic Value of Modern Operating Models

Modern operating models powered by automation and AI aren't just slick new tech - they transform companies from cost centers to profit powerhouses.

This trend is underscored by the findings from the 2024 Global InvestOps Report, which highlights increased competition and the control of operating costs as significant factors influencing investment strategies. Specifically, 55% of respondents view competition as the most critical factor, while 51% emphasize the importance of managing operating costs.

Fundamentally, intelligent systems let businesses redirect resources from repetitive tasks to innovation and high-value work. The efficiencies pay off too - cost savings get invested in customer experience initiatives that boost loyalty and revenues. McKinsey found that companies with higher customer experience maturity grew incomes at over twice the rate of competitors. By providing personalized, seamless services, modern operating models become a competitive edge that accelerates growth.

These streamlined, integrated workflows also substantially lower operational expenses. Automation brings consistency, minimizes errors, and allows scalability without old system constraints. In banking, process upgrades have cut loan processing costs by 50% and shortened account opening turnaround times by 80%. Data-driven insights uncover and address efficiency gaps across functions - from customized product recommendations to predictive inventory management.

But the true game changer is data analytics. By gathering customer data across touchpoints and channels, and then applying advanced analytics, companies gain invaluable intel about preferences, behaviors, and market trends. This smart data powers smarter decisions on pricing, new products, risk management, and more. And machine learning refines models and recommendations continuously. The result? A flexible, future-ready operating model that keeps you ahead of the ever-shifting curve.

Key Drivers For Operating Model Changes

What's driving the urgency to overhaul operating models? Several factors that are reshaping the competitive landscape and forcing companies to adapt faster:

• Increased Competition and the Need for Speed: In Asia, over 80% of consumers now use non-bank players for financial services as per McKinsey. With rising alternatives, the stakes are higher than ever for great customer experiences and rapid service delivery. Legacy processes simply can't match the pace expected, where even microsecond delays can impact client acquisitions and retention. The mandate is clear — only operating models designed for velocity can thrive.
• Rising Customer Expectations: Personalized, on-demand services are no longer a luxury but a standard demand across all touchpoints. In APAC, with high mobile penetration and expanding digital services, the demand for tailored offerings is even more pronounced.
• The Complexity of Big Data: With so much information available, companies need sophisticated systems to sift through it all and make smart decisions. According to IDC, we're looking at a world with 175 zettabytes of data by 2025. That's a lot of data to handle, and it calls for some serious analytics and AI muscle.
• Legacy Systems and Agility: While reliable, older systems lack the flexibility to respond swiftly to changes or scale seamlessly. Piecemeal upgrades provide temporary relief rather than a cure. Instead, holistic overhauls are essential to remove constraints and activate organizational agility.
• Controlling Operating Costs: A significant driver for the overhaul of operating models is the imperative to manage and reduce operating costs. This trend is underscored by the findings from the 2024 Global InvestOps Report, which highlights increased competition and the control of operating costs as significant factors influencing investment strategies. Specifically, 55% of respondents view competition as the most critical factor, while 51% emphasize the importance of managing operating costs.

The Transformation in Operating Models

Technology is rewriting the playbook when it comes to efficiency and innovation - just look at the transformation happening in operating models. Central to this is the rise of APIs, the glue connecting everything and enabling seamless data flows and third-party integrations. APIs break down data silos that once hindered collaboration. In banking, for instance, open APIs have sparked a wave of fintech innovation through greater personalization and customer experience.

Automation is another huge shift replacing manual drudgery with precision and speed. Across HR, customer service, and more, RPA and other technologies handle mundane data tasks so talent can focus on high-value strategic work.

And let's not overlook AI's starring role. Beyond automation, AI systems provide predictive analytics and decision support once found only in sci-fi. By analyzing massive datasets, AI can forecast trends, flag risks, and make recommendations with unbelievable speed and insight. In investing, AI predicts markets, optimizes portfolios, and detects fraud better than humans ever could.

So from open APIs to automation to powerful AI, technology is catapulting operating models into a new era where the possibilities for efficiency and innovation seem endless. These tools don't just streamline processes - they enable breakthroughs in how we work and do business.

Challenges in Automation Adoption

The road to automation and smarter operating models is paved with promise, but it's not without its bumps. Companies diving into this transformation often hit a few snags along the way. Understanding these challenges is the first step towards devising effective strategies to overcome them:

• Compatibility Issues with Legacy Systems: Many organizations operate on legacy systems that are deeply entrenched in their core operations. These systems, while reliable for past needs, often lack the flexibility to integrate with newer, more advanced technologies. The result is a technological mismatch that can stymie automation efforts, leading to fragmented processes that fail to leverage the full potential of automation technologies.
• Data Silos and the Difficulty in Achieving a Single Source of Truth: Data silos are like little islands of information scattered across a company, making it hard to see the big picture. This can throw a wrench in the works for automating processes that stretch across different areas and makes it tricky to pull out those golden nuggets of actionable insights from your data.
• Employee Resistance to New Technologies and Change Management: Employees can resist out of fear they'll be replaced or because workflow changes throw them off. Effective change management is crucial to address these concerns, requiring clear communication, training, and involvement of employees in the transformation process to ensure buy-in and facilitate a smooth transition.
• Agility vs. Control: In the quest for quick moves and flexibility, companies can't forget about maintaining control and governance over their processes. This is especially true in industries like finance, where you've got to play by the rules. Organizations must navigate this delicate balance, ensuring that their pursuit of agility does not compromise their adherence to governance frameworks and regulatory requirements.

Tackling these hurdles means looking at the big picture, focusing on people as much as technology, and taking it step by step. Start small with pilot projects, celebrate the quick wins, and scale up from there. With careful planning and everyone pulling in the same direction, companies can unlock the full potential of automation while keeping those risks in check.

Building a Future-Proof Operating Model

Future-proofing your operating model doesn't happen overnight. Here are four key steps to set your company up for long-term success:

First, build adaptable foundations. The transition from rigid legacy systems to flexible cloud platforms allows for modular growth. Prioritize open API architectures to enable integration with emerging tech like blockchain. This lays the groundwork for agility.

Second, invest heavily in people. Training and change management are crucial to driving an innovation culture that sees change as an opportunity, not a threat. Equip teams with future-ready skills in data, automation, and customer experience. Plus, if you really want to get people to embrace new ways of working, involve them in the process and make solutions together.

Third, implementation should focus on high-impact, quick-win areas first to demonstrate the viability of automation and build momentum. Early success stories help secure employee confidence and provide proof-of-concept for scaled adoption. This phased approach allows for controlled innovation.

Finally, keep iterating. Continuously evaluate markets and assess opportunities to enhance efficiencies. Adopt a design thinking mindset focused on better customer outcomes, not just cost-cutting.

The path to building future-proof operating models calls for visionary leadership, a willingness to challenge status-quo and an experimental mindset that views technology not as a cost, but as an investment.

Conclusion

Adapt or die! That old business cliché rings truer than ever today as companies scramble to revamp old-school operating models. Those manual, spreadsheet-heavy ways of working just don’t cut the mustard anymore amid all this competition, through-the-roof customer expectations, data explosions, and tech disruptions.

It’s metamorphosis time! Shedding outdated systems for smarter, automated ones that put customers first is how to gain an edge today. We’re talking seamless data integration with APIs, efficiency via automation, predictive AI power, and boundless cloud scalability. No more data silos, no more reactive decisions - say hello to integration, insight, and innovation!

Sure, it seems scary, but the payoff is huge: operational excellence and strategic advantage. First movers are already staking their claim to the future with intelligent operating models. As Darwin said, “It’s not the strongest species that survive change but the most adaptable.” Business Survival 101 hasn’t changed - you snooze, you lose. Time to leap, folks!