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Financial Services Industry Is Ready to Lead on AI Adoption, Once Data Concerns Are Addressed

Mike Marks
Riverbed

The financial services industry (FSI) is poised to take the next steps in using AI as a tool to drive business growth, improve operations and deliver a better digital experience for users. However, before taking full advantage of AI's capabilities, leaders first must address several readiness issues as well as concerns over data confidentiality and accuracy.

Leaders in the financial services sector are bullish on AI, with 95% of business and IT decision makers saying that AI is a top C-Suite priority, and 96% of respondents believing it provides their business a competitive advantage, according to Riverbed's Global AI and Digital Experience Survey. Financial services organizations, looking to fend off digital native startups, are pursuing a strategic approach to AI that can reduce costs, increase efficiency, mitigate customer risk and enable customized services.

The industry is also more confident than most sectors in its ability to follow through on widespread AI adoption, with 46% of leaders saying they are fully prepared now to implement their AI strategies, compared with a 37% average across all sectors surveyed. While confidence is high, a majority within the industry are not currently prepared for AI according to the data, revealing a readiness gap in adopting AI, one of three key areas leaders need to address in the year ahead. Leaders in FSI, like those in other sectors, also have a reality gap, with 85% saying they're either "significantly" or "slightly" ahead of their competitors, indicating a level of overconfidence in their own progress. The biggest challenge facing the industry is the data gap, with leaders expressing concerns about both the security of their data and its usability.

Image
Riverbed 2024-11-2

Financial services handle more sensitive customer information than other sectors, and 80% of leaders are worried about the security implications of their data being accessible via the public domain, with their primary concerns being data privacy, regulatory compliance and cybersecurity threats. Despite high confidence in AI's abilities, decision-makers have less faith than those in other sectors in the quality of their data, with only about a third rating their data as excellent for completeness (36%) and accuracy (34%). They need reassurance in data confidentiality and accuracy before they can deliver secure digital experiences for their users, recognizing the need for full-fidelity data.

Younger Employees Are on Board with the Transition

Those concerns notwithstanding, FSI leaders are optimistic about a transformative shift toward AI, with 89% expecting to be fully prepared to implement their AI strategy by 2027 (up from the 46% who say they are ready now). That growth is reflected in their use of generative AI, with 36% saying they have currently implemented or prototyped generative AI, and 71% saying they will in 12 to 18 months.

Leaders say their workforces are mostly enthusiastic about AI, with 62% saying their teams have favorable views (compared with a 59% global average), while only 3% view AI skeptically (compared with 4% globally.) Within their workforces, leaders say Generation Z employees are the most comfortable with AI, at 55%, followed by millennials, at 36%, well ahead of Generation X and baby boomers, at a combined 9%. This suggests AI could eventually replace knowledge-holders; a generational shift in attitudes towards the technology could be why 68% of organizations are increasing investments in infrastructure and talent.

Image
Riverbed 2024-11-1

What Financial Service Leaders Expect from Automated AI

Younger generations of workers are also the most insistent about having a positive digital experience, which leaders believe can be improved via AI automation. Last year's survey found that 92% of business and IT leaders in financial services said the need to provide improved DEX for employees and customers would increase pressure on IT resources. However, nearly half (49%) of financial leaders reported that AI implementations have already optimized resource utilization or will do so within three years, and 94% agreed that AI would help deliver a better digital experience for users. By supporting stretched IT teams, AI implementations can help boost morale.

Other key areas that leaders expect AI will improve includes workflow automation (71%), automated remediation (62%) and autonomously offering 24/7 support via tools like chatbots (62%).

Image
Riverbed-2024-11-3

As IT decision makers increasingly move into C-Suites — 78% said they have a seat at the table — suggesting IT's critical role in driving business innovation is gaining traction. For example, these leaders say technologies such as AI and unified observability are critical to providing exemplary DEX, with 95% saying that unified observability is important (55% said critically important), and 94% calling for greater investment in unified observability solutions.

The next three years will also see a shift toward using AI to drive growth. Currently, leaders say their primary reasons for adopting AI are split almost evenly between operational efficiencies (51%) and driving growth (49%), but 54% expect fueling business growth to be the focus by 2027, ahead of operations, at 46%.

Finally, the survey found that properly implementing AI tools will be essential to boosting productivity, retaining staff, enabling collaboration and staying competitive in the FSI environment.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

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Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...

Financial Services Industry Is Ready to Lead on AI Adoption, Once Data Concerns Are Addressed

Mike Marks
Riverbed

The financial services industry (FSI) is poised to take the next steps in using AI as a tool to drive business growth, improve operations and deliver a better digital experience for users. However, before taking full advantage of AI's capabilities, leaders first must address several readiness issues as well as concerns over data confidentiality and accuracy.

Leaders in the financial services sector are bullish on AI, with 95% of business and IT decision makers saying that AI is a top C-Suite priority, and 96% of respondents believing it provides their business a competitive advantage, according to Riverbed's Global AI and Digital Experience Survey. Financial services organizations, looking to fend off digital native startups, are pursuing a strategic approach to AI that can reduce costs, increase efficiency, mitigate customer risk and enable customized services.

The industry is also more confident than most sectors in its ability to follow through on widespread AI adoption, with 46% of leaders saying they are fully prepared now to implement their AI strategies, compared with a 37% average across all sectors surveyed. While confidence is high, a majority within the industry are not currently prepared for AI according to the data, revealing a readiness gap in adopting AI, one of three key areas leaders need to address in the year ahead. Leaders in FSI, like those in other sectors, also have a reality gap, with 85% saying they're either "significantly" or "slightly" ahead of their competitors, indicating a level of overconfidence in their own progress. The biggest challenge facing the industry is the data gap, with leaders expressing concerns about both the security of their data and its usability.

Image
Riverbed 2024-11-2

Financial services handle more sensitive customer information than other sectors, and 80% of leaders are worried about the security implications of their data being accessible via the public domain, with their primary concerns being data privacy, regulatory compliance and cybersecurity threats. Despite high confidence in AI's abilities, decision-makers have less faith than those in other sectors in the quality of their data, with only about a third rating their data as excellent for completeness (36%) and accuracy (34%). They need reassurance in data confidentiality and accuracy before they can deliver secure digital experiences for their users, recognizing the need for full-fidelity data.

Younger Employees Are on Board with the Transition

Those concerns notwithstanding, FSI leaders are optimistic about a transformative shift toward AI, with 89% expecting to be fully prepared to implement their AI strategy by 2027 (up from the 46% who say they are ready now). That growth is reflected in their use of generative AI, with 36% saying they have currently implemented or prototyped generative AI, and 71% saying they will in 12 to 18 months.

Leaders say their workforces are mostly enthusiastic about AI, with 62% saying their teams have favorable views (compared with a 59% global average), while only 3% view AI skeptically (compared with 4% globally.) Within their workforces, leaders say Generation Z employees are the most comfortable with AI, at 55%, followed by millennials, at 36%, well ahead of Generation X and baby boomers, at a combined 9%. This suggests AI could eventually replace knowledge-holders; a generational shift in attitudes towards the technology could be why 68% of organizations are increasing investments in infrastructure and talent.

Image
Riverbed 2024-11-1

What Financial Service Leaders Expect from Automated AI

Younger generations of workers are also the most insistent about having a positive digital experience, which leaders believe can be improved via AI automation. Last year's survey found that 92% of business and IT leaders in financial services said the need to provide improved DEX for employees and customers would increase pressure on IT resources. However, nearly half (49%) of financial leaders reported that AI implementations have already optimized resource utilization or will do so within three years, and 94% agreed that AI would help deliver a better digital experience for users. By supporting stretched IT teams, AI implementations can help boost morale.

Other key areas that leaders expect AI will improve includes workflow automation (71%), automated remediation (62%) and autonomously offering 24/7 support via tools like chatbots (62%).

Image
Riverbed-2024-11-3

As IT decision makers increasingly move into C-Suites — 78% said they have a seat at the table — suggesting IT's critical role in driving business innovation is gaining traction. For example, these leaders say technologies such as AI and unified observability are critical to providing exemplary DEX, with 95% saying that unified observability is important (55% said critically important), and 94% calling for greater investment in unified observability solutions.

The next three years will also see a shift toward using AI to drive growth. Currently, leaders say their primary reasons for adopting AI are split almost evenly between operational efficiencies (51%) and driving growth (49%), but 54% expect fueling business growth to be the focus by 2027, ahead of operations, at 46%.

Finally, the survey found that properly implementing AI tools will be essential to boosting productivity, retaining staff, enabling collaboration and staying competitive in the FSI environment.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...