Jonathan Scappaticci, Client and Relationship Manager at AutoRek
With rising operational inefficiencies, increasing transaction volumes and shifting client expectations, the pressure on asset management and capital markets to modernise operations has never been greater.
Yet, despite these unprecedented challenges, many firms continue to rely on outdated operational models – manual reconciliation, fragmented systems, and legacy technology – that fail to keep up with the pace of change.
This is where artificial intelligence (AI), machine learning (ML), and automation come in, offering the potential to revolutionise operations by processing data faster, reducing errors and freeing up valuable resources. However, despite these clear advantages to businesses, adoption remains slow. The real question is, why are so many firms still hesitant to truly embrace AI and automation?
Adapting to complexity: the evolving landscape of asset management
The rise of digital assets, the move toward real-time trading (T+1), and evolving regulatory frameworks are pushing transaction volumes to unprecedented levels. Managing this growing data burden with fragmented, outdated systems is becoming increasingly unsustainable.
AutoRek’s latest industry report reveals that 79% of asset management and capital markets firms say their reconciliation processes are already struggling to keep up with current data volumes – or will soon be overwhelmed if volumes continue to rise. And data volumes are rising – just see Nasdaq’s recent announcement to launch 24-hour trading on its exchange.
The message is clear: to keep pace with industry changes, firms need scalable, integrated, and automated solutions. But for many, the road to these solutions is blocked by a series of misconceptions and structural barriers.
Overcoming the main obstacles to AI, ML, and automation implementation
Why, in an industry that thrives on efficiency and precision, are so many firms hesitant to embrace AI, ML and automation? It comes down to a mix of technical and cultural barriers that have created resistance to change.
1. The perceived skills gap
Many firms believe that adopting AI and automation requires a highly technical workforce. Traditional reconciliation and operational processes have long relied on staff with deep institutional knowledge, making automation seem like a threat to expertise rather than a tool to enhance it.
But modern AI and automation platforms are designed to simplify, not complicate. They offer intuitive interfaces and low-code/no-code functionality that allow non-technical staff to manage complex workflows without needing advanced programming skills. AI and automation doesn’t replace human expertise; they complement it, freeing teams to focus on high-value tasks.
2. Data integration and compatibility issues
Asset managers handle massive volumes of data from multiple sources, such as custodians, brokers, fund administrators, and regulators, often in inconsistent formats. Integrating this data manually is slow and error prone.
This complexity has created a misconception that automation only works if all data sources are standardised and compatible. Firms assume they need to overhaul their entire data infrastructure before adopting automation, an expensive and daunting prospect. In fact, 40% of firms cite data challenges as one of the top 3 barriers to implementing automation.
But modern AI and automation platforms are built to handle messy, inconsistent data. Machine learning algorithms can identify patterns, fill gaps, and reconcile discrepancies without the need for perfect data alignment. Solutions like AutoRek can ingest and process data from any source, in any format, reducing friction and improving accuracy.
3. Legacy technology and data silos
Legacy infrastructure remains one of the biggest obstacles to modernisation. Over half of firms (57%) still rely on spreadsheets or a mix of in-house systems and legacy software for data reconciliation.
High replacement costs, operational disruption, and vendor lock-in lead many firms to patch existing systems rather than upgrade them. This results in operational bottlenecks and increased error rates.
But modern automation solutions don’t require a full system overhaul. AI and automation platforms can integrate with legacy systems through APIs, allowing firms to automate key processes like reconciliation and reporting without disrupting core operations. This allows firms to modernise incrementally, reducing risk and improving efficiency step by step.
4. Cost vs. ROI concerns
Risk aversion is often the most immediate blocker to automation. But this mindset overlooks the already present costs of manual processes and legacy technology. Labour-intensive reconciliation, human error, and compliance risks create direct and indirect costs that add up quickly, drain resources and limit growth. Soon, AI will empower firms to automate large platform migrations faster than ever before, greatly reducing the perceived risks seen by stakeholders. We have already entered an era where the biggest risk will be standing still.
AI-powered reconciliation increases accuracy, reduces processing time, and scales operations efficiently. The long-term value far outweighs the initial investment, allowing firms to allocate resources more effectively and drive strategic growth.
Tackling implementation hurdles
Adopting AI, ML, and automation requires a strategic, phased approach:
- Assess current processes: identify inefficiencies and areas where manual intervention slows operations.
- Select a scalable automation solution: ensure the platform can handle diverse data sources, integrate seamlessly, and support long-term growth.
- Prioritise user-friendly technology: choose solutions with intuitive dashboards to reduce the need for specialised technical skills.
- Emphasise compliance and risk reduction: position automation as a tool for strengthening regulatory oversight and minimising compliance risks.
- Focus on ROI and strategic growth: shift the narrative from cost to value -automation isn’t just about efficiency; it’s about positioning your firm for future success.
Embracing the opportunities ahead
AI, ML, and automation have transitioned from promising trends to vital tools for staying competitive. While the challenges to adoption are real, they are manageable. By reassessing outdated beliefs, integrating modern solutions with existing systems, and focusing on long-term strategic benefits rather than immediate costs, firms can unlock greater efficiency, resilience, and growth.
