Latest news with #LoriMacVittie
Scoop
08-05-2025
- Business
- Scoop
F5 2025 State Of Application Strategy Report Reveals Talk Becomes Action As AI Gets To Work
Press Release – F 5 F5s 2025 State of Application Strategy Report, which surveys global IT decision makers, found that 96 per cent of organisations are now deploying AI models, up from a quarter in 2023. F5 Report Highlights AI-Driven Transformation Amid Operational Complexity 96 per cent of surveyed IT decision-makers have deployed AI models, up from a quarter in 2023 SYDNEY, AUSTRALIA, May 8, 2025 – IT leaders are increasingly trusting AI with business-critical tasks from traffic management to cost optimisation, according to the industry's most comprehensive report on application strategy. F5's 2025 State of Application Strategy Report, which surveys global IT decision makers, found that 96 per cent of organisations are now deploying AI models, up from a quarter in 2023. There is also a growing willingness to elevate AI to the heart of business operations. Almost three-quarters of respondents (72 per cent) said they want to use AI to optimise app performance, whereas 59 per cent support the use of AI for both cost-optimisation and to inject security rules, automatically mitigating zero-day vulnerabilities. Today, half of organisations are using AI gateways to connect applications to AI tools, and another 40 per cent expect to be doing so in the next 12 months. Most are using this technology to protect and manage AI models (62 per cent), provide a central point of control (55 per cent), and to protect their company from sensitive data leaks (55 per cent). 'This year's SOAS Report shows that IT decision makers are becoming confident about embedding AI into ops,' said Lori MacVittie, F5 Distinguished Engineer. 'We are fast moving to a point where AI will be trusted to operate autonomously at the heart of an organisation, generating and deploying code that helps to cut costs, boost efficiency, and mitigate security problems. That is what we mean when we talk about AIOps, and it is now becoming a reality.' Operational Readiness and API Challenges Remain Despite growing AI confidence, the SOAS Report highlights several enduring challenges. For organisations currently deploying AI models, the number one concern is AI model security. And, while AI tools are more autonomous than ever, operational readiness gaps still exist. 60 per cent of organisations feel bogged down by manual workflows, and 54 per cent claim skill shortages are barriers to AI development. Furthermore, almost half (48 per cent) identified the cost of building and operating AI workloads as a problem, up from 42 per cent last year. A greater proportion of organisations also said that they have not established a scalable data practice (39 per cent vs. 33 per cent in 2024) and that they do not trust AI outputs due to potential bias or hallucinations (34 per cent vs. 27 per cent). However, fewer complained about the quality of their data (48 per cent, down from 56 per cent last year). APIs were another concern. 58 per cent reported they have become a pain point, and some organisations spend as much as half of their time managing complex configurations involving numerous APIs and languages. Working with vendor APIs (31 per cent), custom scripting (29 per cent), and integrating with ticketing and management systems (23 per cent) were flagged as the most time-consuming automation-related tasks. 'Organisations need to focus on the simplification and standardisation of operations, including streamlining APIs, technologies, and tasks,' said MacVittie. 'They should also recognise that AI systems are themselves well-suited to handle complexity autonomously by generating and deploying policies or solving workflow issues. Operational simplicity is not just something on which AI is going to rely, but which it will itself help to deliver.' Hybrid App Deployments Prevail Allied to soaring AI appetites is a greater reliance on hybrid cloud architectures. According to the SOAS Report, 94 per cent of organisations are deploying applications across multiple environments – including public clouds, private clouds, on-premises data centres, edge computing, and colocation facilities – to meet varied scalability, cost, and compliance requirements. Consequently, most decision makers see hybrid environments as critical to their operational flexibility. 91 per cent cited adaptability to fluctuating business needs as the top benefit of adopting multiple clouds, followed by improved app resiliency (68 per cent) and cost efficiencies (59 per cent). A hybrid approach is also reflected in deployment strategies for AI workloads, with 51 per cent planning to use models across both cloud and on-premises environments for the foreseeable future. Significantly, 79 per cent of organisations recently repatriated at least one application from the public cloud back to an on-premises or colocation environment, citing cost control, security concerns, and predictability. This marks a dramatic rise from 13 per cent just four years ago, further underscoring the importance of preserving flexibility beyond public cloud reliance. Still, the hybrid model can prove a headache for some. Inconsistent delivery policies (reported by 53 per cent of respondents) and fragmented security strategies (47 per cent) are all top of mind in this respect. 'While spreading applications across different environments and cloud providers can bring challenges, the benefits of being cloud-agnostic are too great to ignore. It has never been clearer that the hybrid approach to app deployment is here to stay,' said Cindy Borovick, Director of Market and Competitive Intelligence, F5. APCJ AI Adoption and Challenges – Key Highlights: AI Gateways on the Rise: Nearly half of APCJ organisations (49 per cent) are already using AI gateways to connect applications to AI tools, with another 46 per cent planning to do so in the next 12 months. Top Use Cases for AI Gateways: Among those leveraging AI gateways, the most common applications include protecting and managing AI models (66 per cent), preventing sensitive data leaks (61 per cent), and observing AI traffic and application demand (61 per cent). Data and Trust Challenges: Over half (53 per cent) struggle with immature data quality, and 45 per cent are deterred by the high costs of building and running AI workloads. Hybrid Complexity: The hybrid model of AI deployment introduces hurdles, with 79 per cent citing inconsistent security policies, 59 per cent highlighting delivery inconsistencies, and 16 per cent dealing with operational difficulties. Toward a Programmable, AI-Driven Future Looking ahead, the SOAS Report suggests that organisations aiming to unlock AI's full potential should focus on creating programmable IT environments that standardise and automate app delivery and security policies. By 2026, AI is expected to move from isolated tasks to orchestrating end-to-end processes, marking a shift toward complete automation within IT operations environments. Platforms equipped with natural language interfaces and programmable capabilities will increasingly eliminate the need for traditional management consoles, streamlining IT workflows with unprecedented precision. 'Flexibility and automation are no longer optional—they are critical for navigating complexity and driving transformation at scale,' Borovick emphasised. 'Organisations that establish programmable foundations will not only enhance AI's potential but create IT strategies capable of scaling, adapting, and delivering exceptional customer experiences in the modern age.'
Techday NZ
08-05-2025
- Business
- Techday NZ
IT leaders embrace hybrid AI strategies amid rising challenges
IT leaders are increasing their use of artificial intelligence (AI) for business-critical operations, with a majority deploying AI models and turning to hybrid application strategies, according to findings from the F5 2025 State of Application Strategy Report. The report, based on responses from global IT decision makers, reveals that 96 per cent of organisations have now deployed AI models, a substantial increase from a quarter in 2023. This points to a notable shift in approach, as leaders trust AI to perform functions ranging from traffic management to cost optimisation. Nearly three-quarters of respondents (72 per cent) expressed intentions to use AI for optimising application performance. Additionally, 59 per cent indicated support for leveraging AI to assist with cost optimisation and to integrate security rules, enabling automatic mitigation of zero-day vulnerabilities. The adoption of AI gateways—tools that connect applications to AI services—has also risen. Half of organisations presently use AI gateways, and another 40 per cent anticipate adopting them within the next year. The principal application of these gateways includes protecting and managing AI models (62 per cent), serving as central points of control (55 per cent), and preventing sensitive data leaks (55 per cent). Lori MacVittie, Distinguished Engineer at F5, commented on the findings: "This year's SOAS Report shows that IT decision makers are becoming confident about embedding AI into ops. We are fast moving to a point where AI will be trusted to operate autonomously at the heart of an organisation, generating and deploying code that helps to cut costs, boost efficiency, and mitigate security problems. That is what we mean when we talk about AIOps, and it is now becoming a reality." Despite heightened enthusiasm, the report highlights ongoing operational barriers. Security of AI models remains the top concern for organisations currently deploying such models. Operational readiness, in particular, is a challenge, with 60 per cent citing manual workflows as a hindrance and 54 per cent reporting skills shortages that complicate AI development efforts. Budgetary constraints also persist. Forty-eight per cent identified the costs associated with building and operating AI workloads as problematic, up from 42 per cent last year. Data practices continue evolving, with a higher proportion of organisations indicating that their data handling is not yet scalable (39 per cent, compared to 33 per cent in 2024). Trust in AI outputs, due to potential bias or erroneous results, is another issue, with 34 per cent expressing a lack of trust, compared to 27 per cent previously. However, there has been an improvement in perceived data quality, as 48 per cent reported concerns this year, down from 56 per cent the year before. The increased integration of APIs also brings its own difficulties. Some 58 per cent of respondents noted APIs as a pain point, with certain organisations dedicating as much as half of their time to managing complex API configurations and coding languages. The most time-consuming tasks involve vendor APIs (31 per cent), custom scripting (29 per cent), and integrating with ticketing or management systems (23 per cent). MacVittie observed, "Organisations need to focus on the simplification and standardisation of operations, including streamlining APIs, technologies, and tasks. They should also recognise that AI systems are themselves well-suited to handle complexity autonomously by generating and deploying policies or solving workflow issues. Operational simplicity is not just something on which AI is going to rely, but which it will itself help to deliver." The report identifies a shift towards hybrid cloud architectures, with 94 per cent of organisations running applications across multiple environments, including public and private clouds, on-premises data centres, edge, and colocation facilities. This approach seeks to balance scalability, cost, and compliance needs. Adaptability was cited as a major advantage of multi-cloud deployments, with 91 per cent of decision makers noting the ability to respond to changing business requirements, followed by improved application resiliency (68 per cent) and cost savings (59 per cent). Most organisations now use a hybrid deployment approach for AI workloads as well, with 51 per cent maintaining models across both cloud and on-premises environments. An increased number of organisations have also repatriated one or more applications from public cloud to on-premises or colocation for reasons relating to cost, security, and predictability—79 per cent reported having done so, up significantly from 13 per cent four years prior. The complexity of managing hybrid environments is not without its challenges. Inconsistent delivery policies were reported by 53 per cent, while fragmented security strategies were noted by 47 per cent of respondents. Cindy Borovick, Director of Market and Competitive Intelligence at F5, said, "While spreading applications across different environments and cloud providers can bring challenges, the benefits of being cloud-agnostic are too great to ignore. It has never been clearer that the hybrid approach to app deployment is here to stay." Data from Asia Pacific, China, and Japan (APCJ) reflects these global trends. Almost half (49 per cent) of APCJ organisations already employ AI gateways, with a further 46 per cent set to follow within the coming year. Their main objectives are protecting AI models (66 per cent), preventing sensitive data leaks (61 per cent), and monitoring AI application demand (61 per cent). Over half (53 per cent) struggle with data maturity, and 45 per cent are concerned about the cost of AI deployments. The hybrid model introduces additional complexity, with 79 per cent reporting inconsistent security policies, 59 per cent noting inconsistent delivery, and 16 per cent citing operational difficulties. The report suggests a way forward through the creation of programmable IT environments that standardise and automate application delivery and security. By 2026, AI is anticipated to move beyond isolated tasks to managing entire IT processes. Platforms with natural language interfaces and programmable features are expected to streamline workflows, reducing the need for conventional management consoles. Borovick added, "Flexibility and automation are no longer optional—they are critical for navigating complexity and driving transformation at scale. Organisations that establish programmable foundations will not only enhance AI's potential but create IT strategies capable of scaling, adapting, and delivering exceptional customer experiences in the modern age."
Scoop
08-05-2025
- Business
- Scoop
F5 2025 State Of Application Strategy Report Reveals Talk Becomes Action As AI Gets To Work
F5 Report Highlights AI-Driven Transformation Amid Operational Complexity 96 per cent of surveyed IT decision-makers have deployed AI models, up from a quarter in 2023 SYDNEY, AUSTRALIA, May 8, 2025 – IT leaders are increasingly trusting AI with business-critical tasks from traffic management to cost optimisation, according to the industry's most comprehensive report on application strategy. F5's 2025 State of Application Strategy Report, which surveys global IT decision makers, found that 96 per cent of organisations are now deploying AI models, up from a quarter in 2023. There is also a growing willingness to elevate AI to the heart of business operations. Almost three-quarters of respondents (72 per cent) said they want to use AI to optimise app performance, whereas 59 per cent support the use of AI for both cost-optimisation and to inject security rules, automatically mitigating zero-day vulnerabilities. Today, half of organisations are using AI gateways to connect applications to AI tools, and another 40 per cent expect to be doing so in the next 12 months. Most are using this technology to protect and manage AI models (62 per cent), provide a central point of control (55 per cent), and to protect their company from sensitive data leaks (55 per cent). 'This year's SOAS Report shows that IT decision makers are becoming confident about embedding AI into ops,' said Lori MacVittie, F5 Distinguished Engineer. 'We are fast moving to a point where AI will be trusted to operate autonomously at the heart of an organisation, generating and deploying code that helps to cut costs, boost efficiency, and mitigate security problems. That is what we mean when we talk about AIOps, and it is now becoming a reality.' Operational Readiness and API Challenges Remain Despite growing AI confidence, the SOAS Report highlights several enduring challenges. For organisations currently deploying AI models, the number one concern is AI model security. And, while AI tools are more autonomous than ever, operational readiness gaps still exist. 60 per cent of organisations feel bogged down by manual workflows, and 54 per cent claim skill shortages are barriers to AI development. Furthermore, almost half (48 per cent) identified the cost of building and operating AI workloads as a problem, up from 42 per cent last year. A greater proportion of organisations also said that they have not established a scalable data practice (39 per cent vs. 33 per cent in 2024) and that they do not trust AI outputs due to potential bias or hallucinations (34 per cent vs. 27 per cent). However, fewer complained about the quality of their data (48 per cent, down from 56 per cent last year). APIs were another concern. 58 per cent reported they have become a pain point, and some organisations spend as much as half of their time managing complex configurations involving numerous APIs and languages. Working with vendor APIs (31 per cent), custom scripting (29 per cent), and integrating with ticketing and management systems (23 per cent) were flagged as the most time-consuming automation-related tasks. 'Organisations need to focus on the simplification and standardisation of operations, including streamlining APIs, technologies, and tasks,' said MacVittie. 'They should also recognise that AI systems are themselves well-suited to handle complexity autonomously by generating and deploying policies or solving workflow issues. Operational simplicity is not just something on which AI is going to rely, but which it will itself help to deliver.' Hybrid App Deployments Prevail Allied to soaring AI appetites is a greater reliance on hybrid cloud architectures. According to the SOAS Report, 94 per cent of organisations are deploying applications across multiple environments – including public clouds, private clouds, on-premises data centres, edge computing, and colocation facilities – to meet varied scalability, cost, and compliance requirements. Consequently, most decision makers see hybrid environments as critical to their operational flexibility. 91 per cent cited adaptability to fluctuating business needs as the top benefit of adopting multiple clouds, followed by improved app resiliency (68 per cent) and cost efficiencies (59 per cent). A hybrid approach is also reflected in deployment strategies for AI workloads, with 51 per cent planning to use models across both cloud and on-premises environments for the foreseeable future. Significantly, 79 per cent of organisations recently repatriated at least one application from the public cloud back to an on-premises or colocation environment, citing cost control, security concerns, and predictability. This marks a dramatic rise from 13 per cent just four years ago, further underscoring the importance of preserving flexibility beyond public cloud reliance. Still, the hybrid model can prove a headache for some. Inconsistent delivery policies (reported by 53 per cent of respondents) and fragmented security strategies (47 per cent) are all top of mind in this respect. 'While spreading applications across different environments and cloud providers can bring challenges, the benefits of being cloud-agnostic are too great to ignore. It has never been clearer that the hybrid approach to app deployment is here to stay,' said Cindy Borovick, Director of Market and Competitive Intelligence, F5. APCJ AI Adoption and Challenges – Key Highlights: AI Gateways on the Rise: Nearly half of APCJ organisations (49 per cent) are already using AI gateways to connect applications to AI tools, with another 46 per cent planning to do so in the next 12 months. Top Use Cases for AI Gateways: Among those leveraging AI gateways, the most common applications include protecting and managing AI models (66 per cent), preventing sensitive data leaks (61 per cent), and observing AI traffic and application demand (61 per cent). Data and Trust Challenges: Over half (53 per cent) struggle with immature data quality, and 45 per cent are deterred by the high costs of building and running AI workloads. Hybrid Complexity: The hybrid model of AI deployment introduces hurdles, with 79 per cent citing inconsistent security policies, 59 per cent highlighting delivery inconsistencies, and 16 per cent dealing with operational difficulties. Toward a Programmable, AI-Driven Future Looking ahead, the SOAS Report suggests that organisations aiming to unlock AI's full potential should focus on creating programmable IT environments that standardise and automate app delivery and security policies. By 2026, AI is expected to move from isolated tasks to orchestrating end-to-end processes, marking a shift toward complete automation within IT operations environments. Platforms equipped with natural language interfaces and programmable capabilities will increasingly eliminate the need for traditional management consoles, streamlining IT workflows with unprecedented precision. 'Flexibility and automation are no longer optional—they are critical for navigating complexity and driving transformation at scale,' Borovick emphasised. 'Organisations that establish programmable foundations will not only enhance AI's potential but create IT strategies capable of scaling, adapting, and delivering exceptional customer experiences in the modern age.'
Tahawul Tech
26-03-2025
- Business
- Tahawul Tech
Data Immaturity: A roadblock to advanced AI
Lori MacVittie, F5 Distinguished Engineer, discusses the nature of data immaturity and its impact on AI adoption in this exclusive op-ed. Every survey on generative AI—including our own—points to one inescapable conclusion: data immaturity is going to get in the way of fully realising the potential of generative AI. When we asked about challenges to AI adoption in our 2024 State of Application Strategy report, the top response with 56% of respondents was 'data immaturity.' A quick look around the industry validates that data immaturity is a serious obstacle on the AI adoption path. What is data immaturity? Data immaturity, in the context of AI, refers to an organisation's underdeveloped or inadequate data practices, which limit its ability to leverage AI effectively. It encompasses issues with data quality, accessibility, governance, and infrastructure such as: Poor data quality: Inconsistent, incomplete, or outdated data leads to unreliable AI outcomes. Limited data availability: Data silos across departments hinder access and comprehensive analysis, limiting insights. Weak data governance: Lack of policies on data ownership, compliance, and security introduces risks and restricts AI usage. Inadequate data infrastructure: Insufficient tools and infrastructure impede data processing and AI model training at scale. Unclear data strategy: Lack of a clear strategy results in uncoordinated initiatives and limited focus on valuable data for AI. Data immaturity prevents organisations from harnessing the full potential of AI because high-quality, well-managed, and accessible data is foundational for developing reliable and effective AI systems. Organisations looking to overcome data immaturity often start by building a data strategy, implementing data governance policies, investing in data infrastructure, and enhancing data literacy across teams. The impact on AI adoption In short, data immaturity is a drag on AI adoption. Adoption is already slowing because organizations have, for the most part, already picked the low-hanging generative AI fruit (chatbots, assistants, co-pilots) and are running into data immaturity issues as they try to move toward the more valuable use cases such as workflow automation. Organisations that fail to prioritize data maturity will struggle to unlock these more advanced AI capabilities. Data immaturity leads to a lack of trust in analysis and predictability of execution. That puts a damper on any plans to leverage AI in a more autonomous manner—whether for business or operational process automation. A 2023 study by MIT Sloan Management Review highlights that organisations with mature data management practices are 60% more likely to succeed in workflow automation than those with immature data practices. Data immaturity limits the predictive accuracy and reliability of AI, which are crucial for autonomous functions where decisions are made without human intervention. Organisations must get their data houses in order before they will be able to truly take advantage of AI's potential to optimise workflows and free up valuable time for humans to focus on strategy and design, tasks for which most AI is not yet well suited. Overcoming data immaturity Addressing data immaturity is crucial for enabling advanced AI capabilities. Key steps include: Develop a clear data strategy Align data collection, management, and quality standards with organisational goals to ensure data supports AI projects effectively. Implement robust data governance Establish policies for data ownership, compliance, security, and privacy to improve data quality and build trust in AI insights. Invest in scalable data infrastructure Adopt modern infrastructure, such as cloud storage and data pipelines, to support efficient processing and scalable AI training. Enhance data quality standards Set standards for data accuracy, consistency, and completeness, with regular monitoring and cleaning. Promote data literacy and collaboration Foster a culture of data literacy and teamwork between data and business units to improve data accessibility and impact. By adopting these practices, organisations can establish a solid data foundation for AI, leading to optimised workflows, reduced risks, and more time for strategic tasks. Data maturity is not just a technical necessity; it's a strategic advantage that empowers organisations to unlock the full potential of AI. By overcoming data immaturity, organisations can transition from basic AI applications to more transformative, value-driven use cases, ultimately positioning themselves for long-term success in an AI-driven future. Image Credit: F5
Tahawul Tech
31-01-2025
- Tahawul Tech
Introducing the Application Delivery Top 10
Lori MacVittie, F5 Distinguished Engineer, discusses the top challenges organisations encounter on their journey to deliver and secure every application and API, anywhere. There are a lot of 'top 10' lists in the industry. Predictions, mostly, but the ones that stick are the ones that provide insight into the top challenges faced by organisations trying to deliver and secure applications and APIs. Well, to be fair, most of the best-known top 10 lists are about security. The Open Worldwide Application Security Project (OWASP) has built and maintained several lists that help organizations every day keep their applications, APIs, and now LLMs, secure from the incredibly robust array of attacks that threaten to disrupt business. But no one to date has a top 10 list of challenges that threaten the delivery of applications, APIs, and, yes, generative AI. Until now. Application delivery may have started with the simple—but powerful—load balancing proxy, but it has evolved along with applications to incorporate a wide array of capabilities designed to ensure availability, enhance performance, and secure the increasingly important digital assets that power today's Internet economy. F5 has been there through every major application shift since the early days of the Internet. We've seen it all through the eyes of our customers. From that experience we've come to understand the most common challenges organisations face—and how to solve them. Based on that, we decided it was time to share that knowledge. And, thus, was born the Application Delivery Top 10. The Application Delivery Top 10 is a list of the top 10 challenges organisations encounter on their journey to deliver and secure every application and API, anywhere. It is our belief that sharing such a list will enable organisations to address—or even better, avoid struggling with—the challenges of delivering and securing a hybrid, multicloud application and API portfolio. Like the OWASP Top 10, this list is not designed to be a 'one and done' effort or encompass every delivery challenge organisations will face. That's why we plan to reexamine the list and, if necessary, update it on an annual basis. Weak DNS Practices The Domain Name System (DNS) is a critical component of the internet's infrastructure, translating domain names into IP addresses to route user requests to the appropriate servers. However, weak DNS practices can compromise application performance, availability, and scalability. It can also significantly degrade application performance by increasing query response times and causing delays in resolving domain names. When Time-to-Live (TTL) settings – numerical values that indicate how long a data packet or record should exist on a network before it is discarded – are too low, DNS queries must be resolved more frequently. This increases the load on DNS servers and slows down application response time. Additionally, improperly configured DNS servers or the lack of DNS security features like DNS Security Extensions (DNSSEC) can introduce delays by allowing unauthorized users to hijack or redirect traffic to slower or malicious servers. Weak DNS practices can severely impact the performance, availability, scalability, and operational efficiency of applications. However, by implementing DNSSEC, optimising TTL settings, and securing dynamic DNS updates, organisations can mitigate these risks and create a more reliable DNS infrastructure. Lack of Fault Tolerance and Resilience The lack of fault tolerance and resilience in application delivery strategies can lead to significant performance issues, reduced availability, and scalability limitations. By implementing load balancing, failover mechanisms, and programmable infrastructure, organisations can create a more resilient system that supports continuous availability and optimal performance, even under challenging conditions. Emphasizing fault tolerance enhances user experience, reduces operational overhead and supports efficient scalability, ensuring that applications can meet the demands of today's fast-paced digital environment. Incomplete Observability Observability is a critical aspect of modern application delivery, providing visibility into the health, performance, and usage of applications and infrastructure. Poor visibility becomes particularly problematic in complex environments, such as AI-driven applications, where real-time insights are essential. Ultimately, incomplete observability in application delivery can lead to performance degradation, reduced availability, limited scalability, and operational inefficiencies. By implementing comprehensive monitoring and logging, adopting standardised observability with OpenTelemetry, and utilizing dynamic alerting with automated responses, organisations can overcome these challenges. Insufficient Traffic Controls Effective traffic management is essential for delivering a seamless user experience, particularly as applications scale to support larger audiences and more dynamic workloads. However, insufficient traffic controls can lead to issues like overloading backend services, susceptibility to Distributed Denial of Service (DDoS) attacks, and inefficient resource usage. By implementing rate limiting, throttling, and caching mechanisms, organisations can manage traffic more effectively, prevent service disruptions, and support scalable growth. Emphasising robust traffic management practices is essential for delivering high-performance, resilient applications that can adapt to changing user demands and provide a consistent experience across diverse environments. Unoptimised Traffic Steering Unoptimised traffic steering—caused by static routing policies, lack of dynamic decision-making, or insufficient load-balancing algorithms—can lead to performance bottlenecks, inconsistent availability, and limited scalability. In AI-driven applications, where processing needs can vary based on data types and user demand, efficient traffic steering is essential for maintaining responsiveness. By adopting best practices such as dynamic routing, intelligent load balancing, and programmable ADCs, organisations can optimize traffic flows, improve resource utilisation, and ensure that applications meet variable demand. Inability to Handle Latency Latency is a key factor affecting application delivery, particularly in data-intensive environments like AI applications. The inability to handle latency effectively can lead to performance issues, reduced availability, and limited scalability, especially as applications grow and user demands fluctuate. Latency bottlenecks result from various issues, such as suboptimal data routing, inefficient processing, and inadequate resource allocation. By implementing optimized data routing, edge computing, and adaptive resource allocation, organisations can mitigate latency challenges and support a high-performance, resilient infrastructure. Incompatible Delivery Policies In hybrid multicloud environments, incompatible delivery policies can pose significant challenges to application performance, availability, scalability. It can also lead to soaring operational overheads. Incompatibilities of this nature often arise when organisations use multiple cloud providers, each with unique traffic routing, security, and data handling protocols. According to LoadView, a leading cloud-based load testing platform, applications with inconsistent delivery policies across multiple regions experience 50% more latency in cross-border data transfers than those with region-specific optimisations. By standardising metrics, aligning service capabilities, and leveraging programmable infrastructure, organisations can overcome these challenges. Emphasising consistency and flexibility in delivery policies ensures that applications can maintain high performance, availability, and scalability across a hybrid multicloud infrastructure. Lack of Security and Regulatory Compliance As governments worldwide enforce stricter laws on data sovereignty, security, and privacy, regulatory compliance has become essential. Organisations failing to meet these regulations exposes applications to security vulnerabilities and introduces performance bottlenecks and scalability constraints. These challenges are particularly prevalent in AI-driven applications. By implementing strong encryption, utilizing Federal Information Processing Standards (FIPS)-compliant devices, and adopting automated compliance tools, organisations can address these risks and support secure, scalable, resilient and compliant application delivery. Bespoke Application Requirements As digital applications become increasingly specialised, organisations are often faced with unique requirements that standard infrastructure cannot support. Programmability within the application delivery infrastructure offers a powerful solution to such challenges, enabling organisations to tailor their infrastructure to support complex, customised requirements. Bespoke application requirements often challenge traditional application delivery solutions, as they require customisation that standard infrastructure cannot provide. By leveraging programmability within the application delivery infrastructure, organisations can adapt to these unique demands, ensuring high performance, availability, and scalability. Furthermore, programmable infrastructure enables seamless transitions, integrates new services efficiently, and supports custom load balancing, allowing organisations to deliver reliable and responsive services that meet the specific needs of their users. Poor Resource Utilisation Many organisations struggle with resource inefficiencies due to mismatched distribution algorithms or inadequate health check mechanisms. These inefficiencies can lead to wasted compute power, increased operational overhead, and strained infrastructure, ultimately impacting performance, availability, and scalability. By leveraging programmability, intelligent health checks, and dynamic traffic steering, organisations can optimise resource usage, improve application performance, and enhance scalability. Full details of the Application Delivery Top 10, including mitigation best practices, can be found here: Image Credit: F5
