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Ecosystm research finds Sustainable IT is becoming integral to technology modernisation in Singapore, with 55% of organisations assessing the carbon emissions of their infrastructure – both cloud and on-premises.
Find out the key trends shaping Singapore’s technology landscape.
The Australian economy stands at the brink of a transformative era, driven by the fast adoption of AI. The country’s commitment to leveraging AI across critical sectors as articulated in initiatives such as the CSIRO’s Artificial Intelligence Roadmap, promises significant opportunities for growth and innovation.
This eBook, in partnership with Hewlett Packard Enterprise and NEC explores the key factors organisations need to consider when establishing their strategic AI roadmap. Download your copy.
Download eBook – Take Steps to Prepare Your Organisation for the Oncoming AI Tsunami
(Clicking on this link will take you to the NEC website where you can download the eBook)
GenAI has taken the world by storm, with organisations big and small eager to pilot use cases for automation and productivity boosts. Tech giants like Google, AWS, and Microsoft are offering cloud-based GenAI tools, but the demand is straining current infrastructure capabilities needed for training and deploying large language models (LLMs) like ChatGPT and Bard.
The microchip manufacturing process is intricate, involving hundreds of steps and spanning up to four months from design to mass production. The significant expense and lengthy manufacturing process for semiconductor plants have led to global demand surpassing supply. This imbalance affects technology companies, automakers, and other chip users, causing production slowdowns.
Supply chain disruptions, raw material shortages (such as rare earth metals), and geopolitical situations have also had a fair role to play in chip shortages. For example, restrictions by the US on China’s largest chip manufacturer, SMIC, made it harder for them to sell to several organisations with American ties. This triggered a ripple effect, prompting tech vendors to start hoarding hardware, and worsening supply challenges.
As AI advances and organisations start exploring GenAI, specialised AI chips are becoming the need of the hour to meet their immense computing demands. AI chips can include graphics processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). These specialised AI accelerators can be tens or even thousands of times faster and more efficient than CPUs when it comes to AI workloads.
The surge in GenAI adoption across industries has heightened the demand for improved chip packaging, as advanced AI algorithms require more powerful and specialised hardware. Effective packaging solutions must manage heat and power consumption for optimal performance. TSMC, one of the world’s largest chipmakers, announced a shortage in advanced chip packaging capacity at the end of 2023, that is expected to persist through 2024.
The scarcity of essential hardware, limited manufacturing capacity, and AI packaging shortages have impacted tech providers. Microsoft acknowledged the AI chip crunch as a potential risk factor in their 2023 annual report, emphasising the need to expand data centre locations and server capacity to meet customer demands, particularly for AI services. The chip squeeze has highlighted the dependency of tech giants on semiconductor suppliers. To address this, companies like Amazon and Apple are investing heavily in internal chip design and production, to reduce dependence on large players such as Nvidia – the current leader in AI chip sales.
NVIDIA, one of the largest manufacturers of GPUs, has been forced to pivot its strategy in response to this shortage. The company has shifted focus towards developing chips specifically designed to handle complex AI workloads, such as the A100 and V100 GPUs. These AI accelerators feature specialised hardware like tensor cores optimised for AI computations, high memory bandwidth, and native support for AI software frameworks.
While this move positions NVIDIA at the forefront of the AI hardware race, experts say that it comes at a significant cost. By reallocating resources towards AI-specific GPUs, the company’s ability to meet the demand for consumer-grade GPUs has been severely impacted. This strategic shift has worsened the ongoing GPU shortage, further straining the market dynamics surrounding GPU availability and demand.
Others like Intel, a stalwart in traditional CPUs, are expanding into AI, edge computing, and autonomous systems. A significant competitor to Intel in high-performance computing, AMD acquired Xilinx to offer integrated solutions combining high-performance central processing units (CPUs) and programmable logic devices.
Governments worldwide are boosting chip capacity to tackle the semiconductor crisis and fortify supply chains. Initiatives like the CHIPS for America Act and the European Chips Act aim to bolster domestic semiconductor production through investments and incentives. Leading manufacturers like TSMC and Samsung are also expanding production capacities, reflecting a global consensus on self-reliance and supply chain diversification. Asian governments are similarly investing in semiconductor manufacturing to address shortages and enhance their global market presence.
Japan is providing generous government subsidies and incentives to attract major foreign chipmakers such as TSMC, Samsung, and Micron to invest and build advanced semiconductor plants in the country. Subsidies have helped to bring greenfield investments in Japan’s chip sector in recent years. TSMC alone is investing over USD 20 billion to build two cutting-edge plants in Kumamoto by 2027. The government has earmarked around USD 13 billion just in this fiscal year to support the semiconductor industry.
Moreover, Japan’s collaboration with the US and the establishment of Rapidus, a memory chip firm, backed by major corporations, further show its ambitions to revitalise its semiconductor industry. Japan is also looking into advancements in semiconductor materials like silicon carbide (SiC) and gallium nitride (GaN) – crucial for powering electric vehicles, renewable energy systems, and 5G technology.
South Korea. While Taiwan holds the lead in semiconductor manufacturing volume, South Korea dominates the memory chip sector, largely due to Samsung. The country is also spending USD 470 billion over the next 23 years to build the world’s largest semiconductor “mega cluster” covering 21,000 hectares in Gyeonggi Province near Seoul. The ambitious project, a partnership with Samsung and SK Hynix, will centralise and boost self-sufficiency in chip materials and components to 50% by 2030. The mega cluster is South Korea’s bold plan to cement its position as a global semiconductor leader and reduce dependence on the US amidst growing geopolitical tensions.
Vietnam. Vietnam is actively positioning itself to become a major player in the global semiconductor supply chain amid the push to diversify away from China. The Southeast Asian nation is offering tax incentives, investing in training tens of thousands of semiconductor engineers, and encouraging major chip firms like Samsung, Nvidia, and Amkor to set up production facilities and design centres. However, Vietnam faces challenges such as a limited pool of skilled labour, outdated energy infrastructure leading to power shortages in key manufacturing hubs, and competition from other regional players like Taiwan and Singapore that are also vying for semiconductor investments.
Small language models (SLMs) offer reduced computational requirements compared to larger models, potentially alleviating strain on semiconductor supply chains by deploying on smaller, specialised hardware.
Innovative SLMs like Google’s Gemini Nano and Mistral AI’s Mixtral 8x7B enhance efficiency, running on modest hardware, unlike their larger counterparts. Gemini Nano is integrated into Bard and available on Pixel 8 smartphones, while Mixtral 8x7B supports multiple languages and suits tasks like classification and customer support.
The shift towards smaller AI models can be pivotal to the AI landscape, democratising AI and ensuring accessibility and sustainability. While they may not be able to handle complex tasks as well as LLMs yet, the ability of SLMs to balance model size, compute power, and ethical considerations will shape the future of AI development.
The data architecture outlines how data is managed in an organisation and is crucial for defining the data flow, data management systems required, the data processing operations, and AI applications. Data architects and engineers define data models and structures based on these requirements, supporting initiatives like data science. Before we delve into the right data architecture for your AI journey, let’s talk about the data management options. Technology leaders have the challenge of deciding on a data management system that takes into consideration factors such as current and future data needs, available skills, costs, and scalability. As data strategies become vital to business success, selecting the right data management system is crucial for enabling data-driven decisions and innovation.
A Data Warehouse is a centralised repository that stores vast amounts of data from diverse sources within an organisation. Its main function is to support reporting and data analysis, aiding businesses in making informed decisions. This concept encompasses both data storage and the consolidation and management of data from various sources to offer valuable business insights. Data Warehousing evolves alongside technological advancements, with trends like cloud-based solutions, real-time capabilities, and the integration of AI and machine learning for predictive analytics shaping its future.
A Data Lake is a centralised repository for storing, processing, and securing large volumes of structured and unstructured data. Unlike traditional Data Warehouses, which are structured and optimised for analytics with predefined schemas, Data Lakes retain raw data in its native format. This flexibility in data usage and analysis makes them crucial in modern data architecture, particularly in the age of big data and cloud.
A Data Lakehouse is an innovative data management system that merges the strengths of Data Lakes and Data Warehouses. This hybrid approach strives to offer the adaptability and expansiveness of a Data Lake for housing extensive volumes of raw, unstructured data, while also providing the structured, refined data functionalities typical of a Data Warehouse. By bridging the gap between these two traditional data storage paradigms, Lakehouses enable more efficient data analytics, machine learning, and business intelligence operations across diverse data types and use cases.
The choice between Data Warehouse, Data Lake, or Lakehouse systems is pivotal for businesses in harnessing the power of their data. Each option offers distinct advantages and challenges, requiring careful consideration of organisational needs and goals. By embracing the right data management system, organisations can pave the way for informed decision-making, operational efficiency, and innovation in the digital age.
CX leaders in Australia are actively refining their customer and employee strategies. Due to high contact centre operational costs, outsourcing to countries like the Philippines, Fiji, and South Africa has gained popularity. However, compliance issues restrict some organisations from outsourcing. Despite cost constraints, elevating customer experience (CX) through AI, self-service, and digital channels remains crucial. High agent attrition also highlights the need to enhance employee experience (EX).
Meeting these challenges has prompted organisations to assess AI and automation solutions to enhance efficiency, cut costs, and improve EX. Australian CX teams hold extensive data from diverse applications, underscoring the need for a robust data strategy – that can provide deeper insights into customer journeys, proactive service, improved self-service options, and innovative customer engagement.
Here are 5 ways organisations in Australia can achieve their CX objectives.
Download ‘Australian CX Dynamics: Balancing Cost, Compliance, and Employee Experience‘ as a PDF.
Customers want the flexibility to select a channel that aligns with their preferences – often switching between channels – prompting organisations to offer more engagement channels.
Aim for unified customer context across channels for deeper customer engagement.
Coordinating all channels ensures consistent experiences for customers, with CX teams and agents accessing real-time information across channels. This boosts key metrics like First Call Resolution (FCR) and reduces Average Handle Time (AHT).
It is important not to overlook voice when crafting an omnichannel strategy. Despite digital growth, human interaction remains crucial for complex inquiries and persistent challenges. Context is vital for understanding customer needs, and without it, experiences suffer. This contributes to long waiting times, a common customer complaint in Australia.
Despite having access to customer information from multiple interactions, organisations often struggle to construct a comprehensive customer data profile capable of transforming all available data into actionable intelligence.
A Customer Data Platform (CDP) can eliminate data silos and provide actionable insights.
The emergence of GenAI and Large Language Models (LLMs) has thrust AI into the spotlight, promising to humanise its capabilities. However, there’s untapped potential for AI and automation beyond this.
Australian organisations are primarily considering AI to address key CX priorities: enhancing efficiency, cutting costs, and improving EX.
Agent Assist solutions offer real-time insights before customer interactions, improving CX and saving time. Integrated with GenAI, these solutions automate tasks like call summaries, freeing agents to focus on high-value activities such as sales collaboration, proactive feedback management, personalised outbound calls, and skill development. Predictive AI algorithms go beyond chatbots and Agent Assist solutions, leveraging customer data to forecast trends and optimise resource allocation.
Compliance in contact centres is more than just a legal requirement; it is core to maintaining customer trust and safeguarding brand’s reputation.
Maintaining compliance in contact centres is challenging due to factors such as the need to follow different industry guidelines, constantly changing regulatory environment, and the shift to hybrid work.
Organisations should focus on:
Organisations often struggle to modernise legacy systems and integrate newer technologies, hindering CX transformation.
Delivering CX transformation while managing multiple disparate systems requires a platform that can integrate desired capabilities for holistic CX and EX experiences.
A unified platform streamlines application management, ensuring cohesion, unified KPIs, enhanced security, simplified maintenance, and single sign-on for agents. This approach offers consistent experiences across channels and early issue detection, eliminating the need to navigate multiple applications or projects.
Capabilities that a platform should have:
Organisations in Australia must pivot to meet customers on their terms, and it will require a comprehensive re-evaluation of their CX strategy.
This includes transforming the contact centre into an “Intelligent” Data Hub, leveraging intelligent APIs for seamless customer interaction management; evolving agents into AI-powered brand ambassadors, armed with real-time insights and decision-making capabilities; and redesigning channels and brand experiences for consistency and personalisation, using innovative technologies.
Historically, data scientists have been the linchpins in the world of AI and machine learning, responsible for everything from data collection and curation to model training and validation. However, as the field matures, we’re witnessing a significant shift towards specialisation, particularly in data engineering and the strategic role of Large Language Models (LLMs) in data curation and labelling. The integration of AI into applications is also reshaping the landscape of software development and application design.
AI is being embedded into applications to enhance user experience, optimise operations, and provide insights that were previously inaccessible. For example, natural language processing (NLP) models are being used to power conversational chatbots for customer service, while machine learning algorithms are analysing user behaviour to customise content feeds on social media platforms. These applications leverage AI to perform complex tasks, such as understanding user intent, predicting future actions, or automating decision-making processes, making AI integration a critical component of modern software development.
This shift towards AI-embedded applications is not only changing the nature of the products and services offered but is also transforming the roles of those who build them. Since the traditional developer may not possess extensive AI skills, the role of data scientists is evolving, moving away from data engineering tasks and increasingly towards direct involvement in development processes.
The emergence of LLMs has introduced a novel approach to handling data curation and processing tasks traditionally performed by data scientists. LLMs, with their profound understanding of natural language and ability to generate human-like text, are increasingly being used to automate aspects of data labelling and curation. This not only speeds up the process but also allows data scientists to focus more on strategic tasks such as model architecture design and hyperparameter tuning.
The accuracy of AI models is directly tied to the quality of the data they’re trained on. Incorrectly labelled data or poorly curated datasets can lead to biased outcomes, mispredictions, and ultimately, the failure of AI projects. The role of data engineers and the use of advanced tools like LLMs in ensuring the integrity of data cannot be overstated.
Traditional software developers have primarily focused on writing code, debugging, and software maintenance, with a clear emphasis on programming languages, algorithms, and software architecture. However, as applications become more AI-driven, there is a growing need for developers to understand and integrate AI models and algorithms into their applications. This requirement presents a challenge for developers who may not have specialised training in AI or data science. This is seeing an increasing demand for upskilling and cross-disciplinary collaboration to bridge the gap between traditional software development and AI integration.
In response to this shift, the role of data scientists is expanding beyond the confines of traditional data engineering and data science, to include more direct involvement in the development of applications and the embedding of AI features and functions.
Data engineering has always been a foundational element of the data scientist’s role, and its importance has increased with the surge in data volume, variety, and velocity. Integrating LLMs into the data collection process represents a cutting-edge approach to automating the curation and labelling of data, streamlining the data management process, and significantly enhancing the efficiency of data utilisation for AI and ML projects.
Accurate data labelling and meticulous curation are paramount to developing models that are both reliable and unbiased. Errors in data labelling or poorly curated datasets can lead to models that make inaccurate predictions or, worse, perpetuate biases. The integration of LLMs into data engineering tasks is facilitating a transformation, freeing them from the burdens of manual data labelling and curation. This has led to a more specialised data scientist role that allocates more time and resources to areas that can create greater impact.
Data scientists, with their deep understanding of AI models and algorithms, are increasingly working alongside developers to embed AI capabilities into applications. This collaboration is essential for ensuring that AI models are effectively integrated, optimised for performance, and aligned with the application’s objectives.
The integration of AI into applications is leading to a transformation in the roles within the software development ecosystem. As applications become increasingly AI-driven, the distinction between software development and AI model development is blurring. This convergence needs a more collaborative approach, where traditional developers gain AI literacy and data scientists take on more active roles in application development. The evolution of these roles highlights the interdisciplinary nature of building modern AI-embedded applications and underscores the importance of continuous learning and adaptation in the rapidly advancing field of AI.
The Philippines, renowned as a global contact centre hub, is experiencing heightened pressure on the global stage, leading to intensified competition within the country. Smaller BPOs are driving larger players to innovate, requiring a stronger focus on empowering customer experience (CX) teams, and enhancing employee experience (EX) in organisations in the Philippines.
As the Philippines expands its global footprint, organisations must embrace progressive approaches to outpace rivals in the CX sector.
These priorities can be achieved through a robust data strategy that empowers CX teams and contact centres to glean actionable insights.
Here are 5 ways organisations in the Philippines can achieve their CX objectives.
Download ‘Securing the CX Edge: 5 Strategies for Organisations in the Philippines’ as a PDF.
Ensuring that all channels are connected and integrated at the core is critical in delivering omnichannel experiences. Organisations must ensure that the conversation can be continued seamlessly irrespective of the channel the customer chooses, without losing the context.
Voice must be integrated within the omnichannel strategy. Even with the rise of digital and self-service, voice remains crucial, especially for understanding complex inquiries and providing an alternative when customers face persistent challenges on other channels.
Transition from a siloed view of channels to a unified and integrated approach.
An Intelligent Data Hub aggregates, integrates, and organises customer data across multiple data sources and channels and eliminates the siloed approach to collecting and analysing customer data.
Drive accurate and proactive conversations with your customers through a unified customer data platform.
AI and automation should be the cornerstone of an organisation’s CX efforts to positively impact both customers and employees.
Evaluate all aspects of AI/automation to enhance both customer and employee experience.
Many organisations face challenges in fully modernising legacy systems and reducing reliance on multiple tech providers.
CX transformation while managing multiple disparate systems will require a platform that integrates desired capabilities for holistic CX and EX experiences.
A unified platform streamlines application management, ensuring cohesion, unified KPIs, enhanced security, simplified maintenance, and single sign-on for agents. This approach offers consistent experiences across channels and early issue detection, eliminating the need to navigate multiple applications or projects.
Capabilities that a platform should have:
In the new CX economy, organisations must meet customers on their terms, proactively engaging them before they initiate interactions. This requires a re-evaluation of all aspects of CX delivery.
In my last Ecosystm Insights, I spoke about why organisations need to think about the Voice of the Customer (VoC) quite literally. Organisations need to listen to what their customers are telling them – not just to the survey questions they responded to, answering pre-defined questions that the organisations want to hear about.
The concept of customer feedback is evolving, and how organisations design and manage VoC programs must also change. Technology is now capable of enabling customer teams to tap into all those unsolicited, and often unstructured, raw feedback sources. Think contact centre conversations (calls, chats, chatbots, emails, complaints, call notes), CRM notes, online reviews, social media, etc. Those are all sources of raw customer feedback, waiting to be converted into customer insights.
Organisations can now find the capability of extracting customer insight from raw data across a wide range of solutions, from VoC platforms, data management platforms, contact centre solutions, text analytics players, etc. The expanding tech ecosystem presents opportunities for organisations to enhance their programs. However, navigating this breadth of options can also be confusing as they strive to identify the most suitable tools for their requirements.
As CX programs mature and shift from survey feedback to truly listening to customers, the demand for tech solutions tailored to various needs increases.
As part of my job as CX Consultant & Tech Advisor, I spend a lot of time working with my clients. But I also spend a lot of time speaking with technology vendors, who provide the solutions my clients need. Over the last few weeks and months there’s been a flurry of activity across the CX technology market with lots of product announcements around one specific topic. You guessed it, GenAI.
So, I invested some time in finding out how tech vendors are evolving their offerings. From Medallia, InMoment, Thematic, LiquidVoice, Concentrix, Snowflake, Nice, to Tethr – a broad variety of different vendors, but all with one thing in common; they help analyse customer feedback data.
And I like what I hear. The conversation has not been about GenAI because of GenAI, but about use cases and real-life applications for CX practitioners, including Insights & Research team, Contact Centre, CX, VoC, Digital teams, and so on. The list is long when we include everyone who has a role to play in creating, maintaining, and improving customer experiences.
It’s no wonder that many different vendors have started to embed those capabilities into their solutions and launch new products or features. The tech landscape is becoming increasingly fragmented at this stage.
But what does that leave organisations with? Apart from very confused tech users trying to find the right solution for their organisation.
At this stage, there is immense market fragmentation, with many vendors from different core capabilities starting to incorporate capabilities to analyse unstructured data in the wake of the GenAI boom. However, a market convergence is expected.
While we watch how the market unfolds, one thing is certain. Organisations and customer teams will need to adjust – and that includes the tech stack as well as the CX program set up. With customer feedback now coming from anywhere within or outside the organisation, there is a need for a consolidated source of truth to make sense of it all and move from raw data to customer insights. While organisations will benefit immensely from a consolidated customer data repository, it’s also crucial to break down organisational silos at the same time and democratise insights as widely as possible to enable informed decision-making.
AI systems are creating huge amounts of data at a rapid rate. While this flood of information is extremely valuable, it is also difficult to analyse and understand. Organisations need to make sense of these large data sets to derive useful insights and make better decisions. Data visualisation plays a pivotal role in the interpretation of complex data, making it accessible, understandable, and actionable. Well-designed visualisation can translate complex, high-dimensional data into intuitive, visually appealing representations, helping stakeholders to understand patterns, trends, and anomalies that would otherwise be challenging to recognise.
There are some data visualisation methods that you are using already; and some that you definitely should master as data complexity increases and there is more demand from business teams for better data visualisation.
Download Common Data Visualisation Methods as a PDF
There are additional visualisation tools that you should be using to tell a better data story. Each of these visualisation techniques serves specific purposes in data analysis, offering unique advantages for representing data insights.
Waterfall charts depict the impact of intermediate positive and negative values on an initial value, often resulting in a final value. They are commonly employed in financial analysis to illustrate the contribution of various factors to a total, making them ideal for visualising step-by-step financial contributions or tracking the cumulative effect of sequentially introduced factors.
Advantages:
Box and Whisker Plots summarise data distribution using a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. They are valuable for showcasing data sample variations without relying on specific statistical assumptions. Box and Whisker Plots excel in comparing distributions across multiple groups or datasets, providing a concise overview of various statistics.
Advantages:
Bullet charts, a bar graph variant, serve as a replacement for dashboard gauges and meters. They showcase a primary measure alongside one or more other measures for context, such as a target or previous period’s performance, often incorporating qualitative ranges like poor, satisfactory, and good. Ideal for performance dashboards with limited space, bullet charts efficiently demonstrate progress towards goals.
Advantages:
Each data visualisation type has its unique strengths, making it better suited for certain types of data and analysis than others. The key to effective data visualisation lies in matching the visualisation type to your data’s specific needs, considering the story you want, to tell or the insights you aim to glean. Choosing the right data representation helps you to make informed decisions that enhance your data analysis and communication efforts.
Incorporating Waterfall Charts, Box and Whisker Plots, and Bullet Charts into the data visualisation toolkit allows for a broader range of insights to be derived from your data. From analysing financial data, comparing distributions, to tracking performance metrics, these additional types of visualisation can communicate complex data stories clearly and effectively. As with all data visualisation, the key is to choose the type that best matches the organisation’s data story, making it accessible and understandable to the audience.
