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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

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

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

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...