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By Anna Itsiopoulos, MD of APAC, ActiveOps

Artificial Intelligence (AI) is in vogue. With ChatGPT, Midjourney and Copilot all out there for anyone to use, it’s become ubiquitous with how people create content and develop ideas. It is a system which teaches itself, iterates as it moves forward, and remembers each nuance it is taught along the way. 

Before AI, it was robotics. Before robotics, it was process automation. Both of these processes either allowed a system or a tool to establish a well-worn process and repeat it as necessary to create results and provide a basic level of insight.  

And before that, operations teams merely mapped processes and hoped that there was enough bandwidth to pick tasks up along the way.  

Every half-decade or so, another process-related “silver bullet” comes along to change the service operations game for the better – but ultimately, incremental change is made but not transformative change. But with AI, it’s clear that transformation is really taking place, and operations leaders must think differently in order to capitalise on the opportunity it presents. AI will allow service operations to leap into a new era whereby critical decisions can be supported by fresh insights on your business, providing never-before-seen adaptability and efficiency. 

In this blog we reveal the true reality for service operations teams from our new research into data maturity, insights and challenges across seven countries worldwide. Throughout the blog series, you’ll be able to learn about 2024 priorities, the benefits to AI adoption, and the barriers that financial services leaders and teams are facing. 

Can’t wait to read the report? Download it here. 

Lack of confidence in priorities

top priorities in 2024 ode operations leader in financial services. CHART ActiveOps

But before we jump into the detail of how data and AI can be best used across financial services, it was imperative to get a handle on what priorities were for 850+ Senior Ops leaders this year. Their approach to data usage was different, depending on plans to grow revenues or increase employee retention. Priorities were split into two overarching themes: 

1. Business-focused priorities

These are priorities that directly relate to the business itself, be that financial, CSR, or operational – and focus more on the larger, SMART goals that a business would have – as opposed to those that might be seen to be less of a measurable target, related to churn or employee satisfaction. 

Priorities that fell into this group were:

  • Grow our revenues (35%) 
  • Increase business resilience (33%) 
  • Improve sustainability (31%)

2. People-focused priorities

These priorities are more around both employees and customers, ensuring that the human side of the business is cared for and nurtured in order to make for a better place to work, be that due to tools and systems in place, or employee satisfaction. 

Priorities that fell into this group were:

  • Increase employee productivity (32%) 
  • Drive a better customer experience (31%) 
  • Retain and support our people (30%) 

Initially, we asked leaders how confident they felt about operational performance in relation to these priorities – and things already became worrying. 4 in 10 were not confident – or simply did not know – if they would reach their goal. But what is driving this lack of confidence? We believe the reasons lie in the data. 

The problems begin with effort, issues and data age

Our research revealed a myriad of data challenges across the board; not only are insights tricky to glean from data, but the data itself is poor and outdated – and almost all respondents are facing region- or country-specific challenges based on their own circumstances.  

If leaders in financial services wish to take advantage of AI across their workstreams, this needs to be fixed – and fast.  

‘Significant’ effort to gain insights

Globally, 91% of respondents believe it takes some – or significant – effort to gain access to any insights from the current operational data they’re working with. That’s a huge amount of resource and bandwidth across a team to even make sense of the data available to teams, and a huge imbalance between those who think it takes effort, and those who are confident in their current processes. 

So, effort is high for the reward of insights into data. But is the data these respondents are using reliable and up-to-date? We’re not so sure. 

Operations leaders can’t make decisions due to poor data

average age of data across financial services operations teams -CHART ActiveOps

Only 6% of global respondents are using real-time data. This number is slightly higher in the UK (at 10%), and much lower in the Republic of Ireland and Australia (2% respectively). The majority of users are working with data which is one week old – but over 1 in 5 respondents (22%) are using data which is at least two months old. 

How can critical operational business decisions be made with data that is not current? Things can change in financial services within days – and sometimes hours. Real-time data is a huge necessity in an environment such as this, in order to provide insight on timely issues and allow businesses to be flexible and adaptable in a more efficient timeframe. 

This age of data, coupled with the issues that respondents face, show a very concerning situation.

Almost all respondents are facing data challenges

Of all 850+ respondents surveyed, a whopping 98% say that they face challenges when it comes to decision-making in operations. These include relying too heavily on human instinct, expectations to justify all decisions to seniors, too many stakeholders being involved in decision-making, or relying too much on how things have always been done. 

Breaking this down country by country, we see that:

  • Ireland struggled the most with relying on human instinct (48%) 
  • The US rely too much on how things have always been done (40%) 
  • New Zealand respondents rely too much on assumption, rather than data (35%) 

This highlighted that there is a clear issue across the board when it comes to operational decision-making – and that comes from a lack of data, or relevant tools and systems, leading to a need for human intervention – often from too many people all at once. 

Data paralysis in financial services

There is a critical moment for operations leaders in financial services right now. With a lack of confidence in achieving both business- and people-focused priorities, a swathe of data-related challenges lying ahead, and a lack of experience in dealing with real-time data, leaders (and teams) are at risk of walking into a data disaster. 

It’s important for leaders to sit up and take notice of how they can ensure a more efficient and data-led environment when it comes to decision-making. And that will come with the utilization of real-time data and AI.  

Stay tuned for the next article in the Global Research Report blog series – and for more information right now, download our report and find out how you stack up against our worldwide results. 

Global research report on AI and data in operations across financial services

Our new research analyzes respondents from seven countries and their thoughts to data maturity and AI usage within financial services.

Global research report on AI and data in operations across financial services

Our new research analyzes respondents from seven countries and their thoughts to data maturity and AI usage within financial services.

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