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.
2024 has started cautiously for organisations, with many choosing to continue with tech projects that have already initiated, while waiting for clearer market conditions before starting newer transformation projects. This means that tech providers must continue to refine their market messaging and enhance their service/product offerings to strengthen their market presence in the latter part of the year. Ecosystm analysts present five key considerations for tech providers as they navigate evolving market and customer trends, this year.
Navigating Market Dynamics
Continuing Economic Uncertainties. Organisations will focus on ongoing projects and consider expanding initiatives in the latter part of the year. This means that tech providers should maintain visibility and trust with existing clients. They also need to help their customers meet multiple KPIs.
Popularity of Generative AI. For organisations, this will be the time to go beyond the novelty factor and assess practical business outcomes, allied costs, and change management. Tech providers need to include ROI discussions for short-term and mid-term perspectives as organisations move beyond pilots.
Infrastructure Market Disruption. Tech leaders will keep an eye out for advancements and disruptions in the market (likely to originate from the semiconductor sector). The disruptions might require tech vendors to re-assess the infrastructure partner ecosystem.
Need for New Tech Skills. Tech leaders will evaluate Generative AI’s impact on AIOps and IT Architecture; invest in upskilling for talent retention. Tech providers must prioritise creating user-friendly experiences to make technology accessible to business users. Training and partner enablement will also need a higher focus.
Increased Focus on Governance. Tech leaders will consult tech vendors on how to implement safeguards for data usage, sharing, and cybersecurity. This opens up opportunities in offering governance-related services.
5 Key Considerations for Tech Vendors
#1 Get Ready for the Year of the AI Startup
While many AI companies have been around for years, this will be the year that many of them make a significant play into enterprises in Asia Pacific. This comes at a time when many organisations are attempting to reduce tech debt and simplify their tech architecture.
For these AI startups to succeed, they will need to create watertight business cases, and do a lot of the hard work in pre-integrating their solutions with the larger platforms to reduce the time to value and simplify the systems integration work.
To respond to these emerging threats, existing tech providers will need to not only accelerate their own use of AI in their platforms, but also ramp up the education and promotion of these capabilities.
#2 Lead With Data, Not AI Capabilities
Organisations recognise the need for AI to enhance their workforce, improve customer experience, and automate processes. However, the initial challenge lies in improving data quality, as trust in early AI models hinges on high-quality training data for long-term success.
Tech vendors that can help with data source discovery, metadata analysis, and seamless data pipeline creation will emerge as trusted AI partners. Transformation tools that automate deduplication and quality assurance tasks empower data scientists to focus on high-value work. Automation models like Segment Anything enhance unstructured data labeling, particularly for images. Finally synthetic data will gain importance as quality sources become scarce.
Tech vendors will be tempted to capitalise on the Generative AI hype but for sake of positive early experiences, they should begin with data quality.
#3 Prepare Thoroughly for AI-driven Business Demand
Besides pureplay AI opportunities, AI will drive a renewed and increased interest in data and data management. Tech and service providers can capitalise on this by understanding the larger picture around their clients’ data maturity and governance. Initial conversations around AI can be door openers to bigger, transformational engagements.
Tech vendors should avoid the pitfall of downplaying AI risks. Instead, they should make all efforts to own and drive the conversation with their clients. They need to be forthcoming about their in-house responsible AI guidelines and understand what is happening in AI legislation world-wide (hint: a lot!)
Tech providers must establish strong client partnerships for AI initiatives to succeed. They must address risk and benefit equally to reap the benefits of larger AI-driven transformation engagements.
#4 Converge Network & Security Capabilities
Networking and security vendors will need to develop converged offerings as these two technologies increasingly overlap in the hybrid working era. Organisations are now entering a new phase of maturity as they evolve their remote working policies and invest in tools to regain control. They will require simplified management, increased visibility, and to provide a consistent user experience, wherever employees are located.
There has already been a widespread adoption of SD-WAN and now organisations are starting to explore next generation SSE technologies. Procuring these capabilities from a single provider will help to remove complexity from networks as the number of endpoints continue to grow.
Tech providers should take a land and expand approach, getting a foothold with SASE modules that offer rapid ROI. They should focus on SWG and ZTNA deals with an eye to expanding in CASB and FWaaS, as customers gain experience.
#5 Double Down on Your Partner Ecosystem
The IT services market, particularly in Asia Pacific, is poised for significant growth. Factors, including the imperative to cut IT operational costs, the growing complexity of cloud migrations and transformations, change management for Generative AI capabilities, and rising security and data governance needs, will drive increased spending on IT services.
Tech services providers – consultants, SIs, managed services providers, and VARs – will help drive organisations’ tech spend and strategy. This is a good time to review partners, evaluating whether they can take the business forward, or whether there is a need to expand or change the partner mix.
Partner reviews should start with an evaluation of processes and incentives to ensure they foster desired behaviour from customers and partners. Tech vendors should develop a 21st century partner program to improve chances of success.
2024 will be another crucial year for tech leaders – through the continuing economic uncertainties, they will have to embrace transformative technologies and keep an eye on market disruptors such as infrastructure providers and AI startups. Ecosystm analysts outline the key considerations for leaders shaping their organisations’ tech landscape in 2024.
Navigating Market Dynamics
Continuing Economic Uncertainties. Organisations will focus on ongoing projects and consider expanding initiatives in the latter part of the year.
Popularity of Generative AI. This will be the time to go beyond the novelty factor and assess practical business outcomes, allied costs, and change management.
Infrastructure Market Disruption. Keeping an eye out for advancements and disruptions in the market (likely to originate from the semiconductor sector) will define vendor conversations.
Need for New Tech Skills. Generative AI will influence multiple tech roles, including AIOps and IT Architecture. Retaining talent will depend on upskilling and reskilling.
Increased Focus on Governance. Tech vendors are guide tech leaders on how to implement safeguards for data usage, sharing, and cybersecurity.
5 Key Considerations for Tech Leaders
#1 Accelerate and Adapt: Streamline IT with a DevOps Culture
Over the next 12-18 months, advancements in AI, machine learning, automation, and cloud-native technologies will be vital in leveraging scalability and efficiency. Modernisation is imperative to boost responsiveness, efficiency, and competitiveness in today’s dynamic business landscape.
The continued pace of disruption demands that organisations modernise their applications portfolios with agility and purpose. Legacy systems constrained by technical debt drag down velocity, impairing the ability to deliver new innovative offerings and experiences customers have grown to expect.
Prioritising modernisation initiatives that align with key value drivers is critical. Technology leaders should empower development teams to move beyond outdated constraints and swiftly deploy enhanced applications, microservices, and platforms.
#2 Empowering Tomorrow: Spring Clean Your Tech Legacy for New Leaders
Modernising legacy systems is a strategic and inter-generational shift that goes beyond simple technical upgrades. It requires transformation through the process of decomposing and replatforming systems – developed by previous generations – into contemporary services and signifies a fundamental realignment of your business with the evolving digital landscape of the 21st century.
The essence of this modernisation effort is multifaceted. It not only facilitates the integration of advanced technologies but also significantly enhances business agility and drives innovation. It is an approach that prepares your organisation for impending skill gaps, particularly as the older workforce begins to retire over the next decade. Additionally, it provides a valuable opportunity to thoroughly document, reevaluate, and improve business processes. This ensures that operations are not only efficient but also aligned with current market demands, contemporary regulatory standards, and the changing expectations of customers.
#3 Employee Retention: Consider the Strategic Role of Skills Acquisition
The agile, resilient organisation needs to be able to respond at pace to any threat or opportunity it faces. Some of this ability to respond will be related to technology platforms and architectures, but it will be the skills of employees that will dictate the pace of reform. While employee attrition rates will continue to decline in 2024 – but it will be driven by skills acquisition, not location of work.
Organisations who offer ongoing staff training – recognising that their business needs new skills to become a 21st century organisation – are the ones who will see increasing rates of employee retention and happier employees. They will also be the ones who offer better customer experiences, driven by motivated employees who are committed to their personal success, knowing that the organisation values their performance and achievements.
#4 Next-Gen IT Operations: Explore Gen AI for Incident Avoidance and Predictive Analysis
The integration of Generative AI in IT Operations signifies a transformative shift from the automation of basic tasks, to advanced functions like incident avoidance and predictive analysis. Initially automating routine tasks, Generative AI has evolved to proactively avoiding incidents by analysing historical data and current metrics. This shift from proactive to reactive management will be crucial for maintaining uninterrupted business operations and enhancing application reliability.
Predictive analysis provides insight into system performance and user interaction patterns, empowering IT teams to optimise applications pre-emptively, enhancing efficiency and user experience. This also helps organisations meet sustainability goals through accurate capacity planning and resource allocation, also ensuring effective scaling of business applications to meet demands.
#5 Expanding Possibilities: Incorporate AI Startups into Your Portfolio
While many of the AI startups have been around for over five years, this will be the year they come into your consciousness and emerge as legitimate solutions providers to your organisation. And it comes at a difficult time for you!
Most tech leaders are looking to reduce technical debt – looking to consolidate their suppliers and simplify their tech architecture. Considering AI startups will mean a shift back to more rather than fewer tech suppliers; a different sourcing strategy; more focus on integration and ongoing management of the solutions; and a more complex tech architecture.
To meet business requirements will mean that business cases will need to be watertight – often the value will need to be delivered before a contract has been signed.
