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Banks Are Confident - and Cautious - About Technology Innovation

Fred Fuller
Endava

Amid economic disruption, fintech competition, and other headwinds in recent years, banks have had to quickly adjust to the demands of the market. This adaptation is often reliant on having the right technology infrastructure in place.

Industry attitudes toward the situation tend to be positive, with 80% of retail banking leaders saying that their technology is ahead of their competitors. However, they still recognize areas where improvements can be made. This finding comes from Endava's new Retail Banking Report, which looks at banks' current and future strategies to meet customer demand and explores their plans to address external factors affecting the industry.

The main takeaway from the report is that retail banks are prioritizing AI, data analytics, payments technology, and core system upgrades to improve the experience of their internal and external systems.

Additional insights from the Retail Banking report include:

Customer centricity: 85% of financial institutions prioritize improving the customer experience, recognizing its importance for acquisition and retention. More than 70% are doing this by increasing digital capabilities and payment offerings.

AI investment: 50% of banks are investing in AI within the next year, making it the top category of those evaluated. The financial sector is excited about the potential of AI to create new efficiencies across internal infrastructure and customer-facing products, with applications such as fraud detection, customer service, data analysis, and investment management.

Data analytics: Close behind AI, banks are focused on data analytics, with 45% of respondents indicating they are investing in this area. Leaders continue to see the value of data to improve customer service, strengthen security and risk management, personalize products, and attract new customers.

Payments upgrades: Upgrading payment gateways and adopting new payment rails are top priorities, with over 75% of organizations ranking them as high-priority initiatives. Upgraded payments technology allows banks to offer customers instant money transfers and timely bill payments, which fosters loyalty and reduces attrition. Additionally, it gives them the ability to increase revenue from current payment volumes.

Core modernization: To accommodate these technology priorities, banks are focusing on updating their core banking system. 75% of those surveyed feel they need to modernize their cores, with large numbers embracing cloud-based solutions. The core system is the backbone of a financial institution, impacting everything from application performance management to in-person customer experience. The benefits of a modern core often include lower operating costs, wider range of products, increased efficiency, enhanced security, and improved retention.

Financial institutions currently operate in a demanding and volatile marketplace requiring them to adapt quickly to shifts in consumer preference and external pressures. It's clear from the report findings that banks of the future will capture sustainable market share by focusing on customer preferences and ensuring alignment between their back-end systems and front-end, client-facing operations.

To ensure they are keeping up with the changing market demands, leaders can leverage technology to quickly roll out new offerings like AI assistants and real-time payments. When a bank creates a better user experience, they're encouraging customers to take advantage of more of their products, creating a more profitable and loyal customer base. The organizations that will succeed are those who can use technology to meet these rapidly evolving consumer demands, while demonstrating ongoing resilience and adaptability.

Fred Fuller is EVP, Global Head of Banking and Capital Markets, at Endava

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Banks Are Confident - and Cautious - About Technology Innovation

Fred Fuller
Endava

Amid economic disruption, fintech competition, and other headwinds in recent years, banks have had to quickly adjust to the demands of the market. This adaptation is often reliant on having the right technology infrastructure in place.

Industry attitudes toward the situation tend to be positive, with 80% of retail banking leaders saying that their technology is ahead of their competitors. However, they still recognize areas where improvements can be made. This finding comes from Endava's new Retail Banking Report, which looks at banks' current and future strategies to meet customer demand and explores their plans to address external factors affecting the industry.

The main takeaway from the report is that retail banks are prioritizing AI, data analytics, payments technology, and core system upgrades to improve the experience of their internal and external systems.

Additional insights from the Retail Banking report include:

Customer centricity: 85% of financial institutions prioritize improving the customer experience, recognizing its importance for acquisition and retention. More than 70% are doing this by increasing digital capabilities and payment offerings.

AI investment: 50% of banks are investing in AI within the next year, making it the top category of those evaluated. The financial sector is excited about the potential of AI to create new efficiencies across internal infrastructure and customer-facing products, with applications such as fraud detection, customer service, data analysis, and investment management.

Data analytics: Close behind AI, banks are focused on data analytics, with 45% of respondents indicating they are investing in this area. Leaders continue to see the value of data to improve customer service, strengthen security and risk management, personalize products, and attract new customers.

Payments upgrades: Upgrading payment gateways and adopting new payment rails are top priorities, with over 75% of organizations ranking them as high-priority initiatives. Upgraded payments technology allows banks to offer customers instant money transfers and timely bill payments, which fosters loyalty and reduces attrition. Additionally, it gives them the ability to increase revenue from current payment volumes.

Core modernization: To accommodate these technology priorities, banks are focusing on updating their core banking system. 75% of those surveyed feel they need to modernize their cores, with large numbers embracing cloud-based solutions. The core system is the backbone of a financial institution, impacting everything from application performance management to in-person customer experience. The benefits of a modern core often include lower operating costs, wider range of products, increased efficiency, enhanced security, and improved retention.

Financial institutions currently operate in a demanding and volatile marketplace requiring them to adapt quickly to shifts in consumer preference and external pressures. It's clear from the report findings that banks of the future will capture sustainable market share by focusing on customer preferences and ensuring alignment between their back-end systems and front-end, client-facing operations.

To ensure they are keeping up with the changing market demands, leaders can leverage technology to quickly roll out new offerings like AI assistants and real-time payments. When a bank creates a better user experience, they're encouraging customers to take advantage of more of their products, creating a more profitable and loyal customer base. The organizations that will succeed are those who can use technology to meet these rapidly evolving consumer demands, while demonstrating ongoing resilience and adaptability.

Fred Fuller is EVP, Global Head of Banking and Capital Markets, at Endava

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As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...