Over the past year, many organisations have explored Generative AI and LLMs, with some successfully identifying, piloting, and integrating suitable use cases. As business leaders push tech teams to implement additional use cases, the repercussions on their roles will become more pronounced. Embracing GenAI will require a mindset reorientation, and tech leaders will see substantial impact across various ‘traditional’ domains.
AIOps and GenAI Synergy: Shaping the Future of IT Operations
When discussing AIOps adoption, there are commonly two responses: “Show me what you’ve got” or “We already have a team of Data Scientists building models”. The former usually demonstrates executive sponsorship without a specific business case, resulting in a lukewarm response to many pre-built AIOps solutions due to their lack of a defined business problem. On the other hand, organisations with dedicated Data Scientist teams face a different challenge. While these teams can create impressive models, they often face pushback from the business as the solutions may not often address operational or business needs. The challenge arises from Data Scientists’ limited understanding of the data, hindering the development of use cases that effectively align with business needs.
The most effective approach lies in adopting an AIOps Framework. Incorporating GenAI into AIOps frameworks can enhance their effectiveness, enabling improved automation, intelligent decision-making, and streamlined operational processes within IT operations.
This allows active business involvement in defining and validating use-cases, while enabling Data Scientists to focus on model building. It bridges the gap between technical expertise and business requirements, ensuring AIOps initiatives are influenced by the capabilities of GenAI, address specific operational challenges and resonate with the organisation’s goals.
The Next Frontier of IT Infrastructure
Many companies adopting GenAI are openly evaluating public cloud-based solutions like ChatGPT or Microsoft Copilot against on-premises alternatives, grappling with the trade-offs between scalability and convenience versus control and data security.
Cloud-based GenAI offers easy access to computing resources without substantial upfront investments. However, companies face challenges in relinquishing control over training data, potentially leading to inaccurate results or “AI hallucinations,” and concerns about exposing confidential data. On-premises GenAI solutions provide greater control, customisation, and enhanced data security, ensuring data privacy, but require significant hardware investments due to unexpectedly high GPU demands during both the training and inferencing stages of AI models.
Hardware companies are focusing on innovating and enhancing their offerings to meet the increasing demands of GenAI. The evolution and availability of powerful and scalable GPU-centric hardware solutions are essential for organisations to effectively adopt on-premises deployments, enabling them to access the necessary computational resources to fully unleash the potential of GenAI. Collaboration between hardware development and AI innovation is crucial for maximising the benefits of GenAI and ensuring that the hardware infrastructure can adequately support the computational demands required for widespread adoption across diverse industries. Innovations in hardware architecture, such as neuromorphic computing and quantum computing, hold promise in addressing the complex computing requirements of advanced AI models.
The synchronisation between hardware innovation and GenAI demands will require technology leaders to re-skill themselves on what they have done for years – infrastructure management.
The Rise of Event-Driven Designs in IT Architecture
IT leaders traditionally relied on three-tier architectures – presentation for user interface, application for logic and processing, and data for storage. Despite their structured approach, these architectures often lacked scalability and real-time responsiveness. The advent of microservices, containerisation, and serverless computing facilitated event-driven designs, enabling dynamic responses to real-time events, and enhancing agility and scalability. Event-driven designs, are a paradigm shift away from traditional approaches, decoupling components and using events as a central communication mechanism. User actions, system notifications, or data updates trigger actions across distributed services, adding flexibility to the system.
However, adopting event-driven designs presents challenges, particularly in higher transaction-driven workloads where the speed of serverless function calls can significantly impact architectural design. While serverless computing offers scalability and flexibility, the latency introduced by initiating and executing serverless functions may pose challenges for systems that demand rapid, real-time responses. Increasing reliance on event-driven architectures underscores the need for advancements in hardware and compute power. Transitioning from legacy architectures can also be complex and may require a phased approach, with cultural shifts demanding adjustments and comprehensive training initiatives.
The shift to event-driven designs challenges IT Architects, whose traditional roles involved designing, planning, and overseeing complex systems. With Gen AI and automation enhancing design tasks, Architects will need to transition to more strategic and visionary roles. Gen AI showcases capabilities in pattern recognition, predictive analytics, and automated decision-making, promoting a symbiotic relationship with human expertise. This evolution doesn’t replace Architects but signifies a shift toward collaboration with AI-driven insights.
IT Architects need to evolve their skill set, blending technical expertise with strategic thinking and collaboration. This changing role will drive innovation, creating resilient, scalable, and responsive systems to meet the dynamic demands of the digital age.
Whether your organisation is evaluating or implementing GenAI, the need to upskill your tech team remains imperative. The evolution of AI technologies has disrupted the tech industry, impacting people in tech. Now is the opportune moment to acquire new skills and adapt tech roles to leverage the potential of GenAI rather than being disrupted by it.
While the discussions have centred around AI, particularly Generative AI in 2023, the influence of AI innovations is extensive. Organisations will urgently need to re-examine their risk strategies, particularly in cyber and resilience practices. They will also reassess their infrastructure needs, optimise applications for AI, and re-evaluate their skills requirements.
This impacts the entire tech market, including tech skills, market opportunities, and innovations.
Ecosystm analysts Alea Fairchild, Darian Bird, Richard Wilkins, and Tim Sheedy present the top 5 trends in building an Agile & Resilient Organisation in 2024.
Click here to download ‘Ecosystm Predicts: Top 5 Resilience Trends in 2024’ as a PDF.
#1 Gen AI Will See Spike in Infrastructure Innovation
Enterprises considering the adoption of Generative AI are evaluating cloud-based solutions versus on-premises solutions. Cloud-based options present an advantage in terms of simplified integration, but raise concerns over the management of training data, potentially resulting in AI-generated hallucinations. On-premises alternatives offer enhanced control and data security but encounter obstacles due to the unexpectedly high demands of GPU computing needed for inferencing, impeding widespread implementation. To overcome this, there’s a need for hardware innovation to meet Generative AI demands, ensuring scalable on-premises deployments.
The collaboration between hardware development and AI innovation is crucial to unleash the full potential of Generative AI and drive enterprise adoption in the AI ecosystem.
Striking the right balance between cloud-based flexibility and on-premises control is pivotal, with considerations like data control, privacy, scalability, compliance, and operational requirements.
#2 Cloud Migrations Will Make Way for Cloud Transformations
The steady move to the public cloud has slowed down. Organisations – particularly those in mature economies – now prioritise cloud efficiencies, having largely completed most of their application migration. The “easy” workloads have moved to the cloud – either through lift-and-shift, SaaS, or simple replatforming.
New skills will be needed as organisations adopt public and hybrid cloud for their entire application and workload portfolio.
- Cloud-native development frameworks like Spring Boot and ASP.NET Core make it easier to develop cloud-native applications
- Cloud-native databases like MongoDB and Cassandra are designed for the cloud and offer scalability, performance, and reliability
- Cloud-native storage like Snowflake, Amazon S3 and Google Cloud Storage provides secure and scalable storage
- Cloud-native messaging like Amazon SNS and Google Cloud Pub/Sub provide reliable and scalable communication between different parts of the cloud-native application
#3 2024 Will be a Good Year for Technology Services Providers
Several changes are set to fuel the growth of tech services providers (systems integrators, consultants, and managed services providers).
There will be a return of “big apps” projects in 2024.
Companies are embarking on significant updates for their SAP, Oracle, and other large ERP, CRM, SCM, and HRM platforms. Whether moving to the cloud or staying on-premises, these upgrades will generate substantial activity for tech services providers.
The migration of complex apps to the cloud involves significant refactoring and rearchitecting, presenting substantial opportunities for managed services providers to transform and modernise these applications beyond traditional “lift-and-shift” activities.
The dynamic tech landscape, marked by AI growth, evolving security threats, and constant releases of new cloud services, has led to a shortage of modern tech skills. Despite a more relaxed job market, organisations will increasingly turn to their tech services partners, whether onshore or offshore, to fill crucial skill gaps.
#4 Gen AI and Maturing Deepfakes Will Democratise Phishing
As with any emerging technology, malicious actors will be among the fastest to exploit Generative AI for their own purposes. The most immediate application will be employing widely available LLMs to generate convincing text and images for their phishing schemes. For many potential victims, misspellings and strangely worded appeals are the only hints that an email from their bank, courier, or colleague is not what it seems. The ability to create professional-sounding prose in any language and a variety of tones will unfortunately democratise phishing.
The emergence of Generative AI combined with the maturing of deepfake technology will make it possible for malicious agents to create personalised voice and video attacks. Digital channels for communication and entertainment will be stretched to differentiate between real and fake.
Security training that underscores the threat of more polished and personalised phishing is a must.
#5 A Holistic Approach to Risk and Operational Resilience Will Drive Adoption of VMaaS
Vulnerability management is a continuous, proactive approach to managing system security. It not only involves vulnerability assessments but also includes developing and implementing strategies to address these vulnerabilities. This is where Vulnerability Management Platforms (VMPs) become table stakes for small and medium enterprises (SMEs) as they are often perceived as “easier targets” by cybercriminals due to potentially lesser investments in security measures.
Vulnerability Management as a Service (VMaaS) – a third-party service that manages and controls threats to automate vulnerability response to remediate faster – can improve the asset cybersecurity management and let SMEs focus on their core activities.
In-house security teams will particularly value the flexibility and customisation of dashboards and reports that give them enhanced visibility over all assets and vulnerabilities.
