Southeast Asia’s banking sector is poised for significant digital transformation. With projected Net Interest Income reaching USD 148 billion by 2024, the market is ripe for continued growth. While traditional banks still hold a dominant position, digital players are making significant inroads. To thrive in this evolving landscape, financial institutions must adapt to rising customer expectations, stringent regulations, and the imperative for resilience. This will require a seamless collaboration between technology and business teams.
To uncover how banks in Southeast Asia are navigating this complex landscape and what it takes to succeed, Ecosystm engaged in in-depth conversations with senior banking executives and technology leaders as part of our research initiatives. Here are the highlights of the discussions with leaders across the region.
#1 Achieving Hyper-Personalisation Through AI
As banks strive to deliver highly personalised financial services, AI-driven models are becoming increasingly essential. These models analyse customer behaviour to anticipate needs, predict future behaviour, and offer relevant services at the right time. AI-powered tools like chatbots and virtual assistants further enhance real-time customer support.
Hyper-personalisation, while promising, comes with its challenges – particularly around data privacy and security. To deliver deeply tailored services, banks must collect extensive customer information, which raises the question: how can they ensure this sensitive data remains protected?
AI projects require a delicate balance between innovation and regulatory compliance. Regulations often serve as the right set of guardrails within which banks can innovate. However, banks – especially those with cross-border operations – must establish internal guidelines that consider the regulatory landscape of multiple jurisdictions.
#2 Beyond AI: Other Emerging Technologies
AI isn’t the only emerging technology reshaping Southeast Asian banking. Banks are increasingly adopting technologies like Robotic Process Automation (RPA) and blockchain to boost efficiency and engagement. RPA is automating repetitive tasks, such as data entry and compliance checks, freeing up staff for higher-value work. CIMB in Malaysia reports seeing a 35-50% productivity increase thanks to RPA. Blockchain is being explored for secure, transparent transactions, especially cross-border payments. The Asian Development Bank successfully trialled blockchain for faster, safer bond settlements. While AR and VR are still emerging in banking, they offer potential for enhanced customer engagement. Banks are experimenting with immersive experiences like virtual branch visits and interactive financial education tools.
The convergence of these emerging technologies will drive innovation and meet the rising demand for seamless, secure, and personalised banking services in the digital age. This is particularly true for banks that have the foresight to future-proof their tech foundation as part of their ongoing modernisation efforts. Emerging technologies offer exciting opportunities to enhance customer engagement, but they shouldn’t be used merely as marketing gimmicks. The focus must be on delivering tangible benefits that improve customer outcomes.
#3 Greater Banking-Fintech Collaboration
The digital payments landscape in Southeast Asia is experiencing rapid growth, with a projected 10% increase between 2024-2028. Digital wallets and contactless payments are becoming the norm, and platforms like GrabPay, GoPay, and ShopeePay are dominating the market. These platforms not only offer convenience but also enhance financial inclusion by reaching underbanked populations in remote areas.
The rise of digital payments has significantly impacted traditional banks. To remain relevant in this increasingly cashless society, banks are collaborating with fintech companies to integrate digital payment solutions into their services. For instance, Indonesia’s Bank Mandiri collaborated with digital credit services provider Kredivo to provide customers with access to affordable and convenient credit options.
Partnerships between traditional banks and fintechs are essential for staying competitive in the digital age, especially in areas like digital payments, data analytics, and customer experience.
While these collaborations offer opportunities, they also pose challenges. Banks must invest in advanced fraud detection, AI monitoring, and robust authentication to secure digital payments. Once banks adopt a mindset of collaboration with innovators, they can leverage numerous innovations in the cybersecurity space to address these challenges.
#4 Agile Infrastructure for an Agile Business
While the banking industry is considered a pioneer in implementing digital technologies, its approach to cloud has been more cautious. While interest remained high, balancing security and regulatory concerns with cloud agility impacted the pace. Hybrid multi-cloud environments has accelerated banking cloud adoption.
Leveraging public and private clouds optimises IT costs, offering flexibility and scalability for changing business needs. Hybrid cloud allows resource adjustments for peak demand or cost reductions off-peak. Access to cloud-native services accelerates innovation, enabling rapid application development and improved competitiveness. As the industry adopts GenAI, it requires infrastructure capable of handling vast data, massive computing power, advanced security, and rapid scalability – all strengths of hybrid cloud.
Replicating critical applications and data across multiple locations ensures disaster recovery and business continuity. A multi-cloud strategy also helps avoid vendor lock-in, diversifies cloud providers, and reduces exposure to outages.
Hybrid cloud adoption offers benefits but also presents challenges for banks. Managing the environment is complex, needing coordination across platforms and skilled personnel. Ensuring data security and compliance across on-prem and public cloud infrastructure is demanding, requiring robust measures. Network latency and performance issues can arise, making careful design and optimisation crucial. Integrating on-prem systems with public cloud services is time-consuming and needs investment in tools and expertise.
#5 Cyber Measures to Promote Customer & Stakeholder Trust
The banking sector is undergoing rapid AI-driven digital transformation, focusing on areas like digital customer experiences, fraud detection, and risk assessment. However, this shift also increases cybersecurity risks, with the majority of banking technology leaders anticipate inevitable data breaches and outages.
Key challenges include expanding technology use, such as cloud adoption and AI integration, and employee-related vulnerabilities like phishing. Banks in Southeast Asia are investing heavily in modernising infrastructure, software, and cybersecurity.
Banks must update cybersecurity strategies to detect threats early, minimise damage, and prevent lateral movement within networks.
Employee training, clear security policies, and a culture of security consciousness are critical in preventing breaches.
Regulatory compliance remains a significant concern, but banks are encouraged to move beyond compliance checklists and adopt risk-based, intelligence-led strategies. AI will play a key role in automating compliance and enhancing Security Operations Centres (SOCs), allowing for faster threat detection and response. Ultimately, the BFSI sector must prioritise cybersecurity continuously based on risk, rather than solely on regulatory demands.
Breaking Down Barriers: The Role of Collaboration in Banking Transformation
Successful banking transformation hinges on a seamless collaboration between technology and business teams. By aligning strategies, fostering open communication, and encouraging cross-functional cooperation, banks can effectively leverage emerging technologies to drive innovation, enhance customer experience, and improve efficiency.
A prime example of the power of collaboration is the success of AI initiatives in addressing specific business challenges.
This user-centric approach ensures that technology addresses real business needs.
By fostering a culture of collaboration, banks can promote continuous learning, idea sharing, and innovation, ultimately driving successful transformation and long-term growth in the competitive digital landscape.
India is undergoing a remarkable transformation across various industries, driven by rapid technological advancements, evolving consumer preferences, and a dynamic economic landscape. From the integration of new-age technologies like GenAI to the adoption of sustainable practices, industries in India are redefining their operations and strategies to stay competitive and relevant.
Here are some organisations that are leading the way.
Download ‘From Tradition to Innovation: Industry Transformation in India’ as a PDF
Redefining Customer Experience in the Financial Sector
Financial inclusion. India’s largest bank, the State Bank of India, is leading financial inclusion with its YONO app, to enhance accessibility. Initial offerings include five core banking services: cash withdrawals, cash deposits, fund transfers, balance inquiries, and mini statements, with plans to include account opening and social security scheme enrollments.
Customer Experience. ICICI Bank leverages RPA to streamline repetitive tasks, enhancing customer service with its virtual assistant, iPal, for handling queries and transactions. HDFC Bank customer preference insights to offer tailored financial solutions, while Axis Bank embraces a cloud-first strategy to digitise its platform and improve customer interfaces.
Indian banks are also collaborating with fintechs to harness new technologies for better customer experiences. YES Bank has partnered with Paisabazaar to simplify loan applications, and Canara HSBC Life Insurance has teamed up with Artivatic.AI to enhance its insurance processes via an AI-driven platform.
Improving Healthcare Access
Indian healthcare organisations are harnessing technology to enhance efficiency, improve patient experiences, and enable remote care.
Apollo Hospitals has launched an automated patient monitoring system that alerts experts to health deteriorations, enabling timely interventions through remote monitoring. Manipal Hospitals’ video consultation app reduces emergency department pressure by providing medical advice, lab report access, bill payments, appointment bookings, and home healthcare requests, as well as home medication delivery and Fitbit monitoring. Omni Hospitals has also implemented AI-based telemedicine for enhanced patient engagement and remote monitoring.
The government is also driving the improvement of healthcare access. eSanjeevani is the world’s largest government-owned telemedicine system, with the capacity to handle up to a million patients a day.
Driving Retail Agility & Consumer Engagement
India’s Retail sector, the fourth largest globally, contributes over 10% of the nation’s GDP. To stay competitive and meet evolving consumer demands, Indian retailers are rapidly adopting digital technologies, from eCommerce platforms to AI.
Omnichannel Strategies. Reliance Retail integrates physical stores with digital platforms like JioMart to boost sales and customer engagement. Tata CLiQ’s “phygital” approach merges online and offline shopping for greater convenience while Shoppers Stop uses RFID and data analytics for improved in-store experiences, online shopping, and targeted marketing.
Retail AI. Flipkart’s AI-powered shopping assistant, Flippi uses ML for conversational product discovery and intuitive guidance. BigBasket employs IoT-led AI to optimise supply chain and improve product quality.
Reshaping the Automotive Landscape
Tech innovation, from AI/ML to connected vehicle technologies, is revolutionising the Automotive sector. This shift towards software-defined vehicles and predictive supply chain management underscores the industry’s commitment to efficiency, transparency, safety, and environmental sustainability.
Maruti Suzuki’s multi-pronged approach includes collaborating with over 60 startups through its MAIL program and engaging Accenture to drive tech change. Maruti has digitised 24 out of 26 customer touchpoints, tracking every interaction to enhance customer service. In the Auto OEM space, they are shifting to software-defined vehicles and operating models.
Tata Motors is leveraging cloud, AI/ML, and IoT to enhancing efficiency, improving safety, and driving sustainability across its operations. Key initiatives include connected vehicles, automated driving, dealer management, cybersecurity, electric powertrains, sustainability, and supply chain optimisation.
Streamlining India’s Logistics Sector
India’s logistics industry is on the cusp of a digital revolution as it embraces cutting-edge technologies to streamline processes and reduce environmental impact.
Automation and Predictive Analytics. Automation is transforming warehousing operations in India, with DHL India automating sortation centres to handle 6,000 shipments per hour. Predictive analytics is reshaping logistics decision-making, with Delhivery optimising delivery routes to ensure timely service.
Sustainable Practices. The logistics sector contributes one-third of global carbon emissions. To combat this, Amazon India will convert its delivery fleet to 100% EVs by 2030 to reduce emissions and fuel costs. Blue Energy Motors is also producing 10,000 heavy-duty LNG trucks annually for zero-emission logistics.
At a recently held Ecosystm roundtable, in partnership with Qlik and 121Connects, Ecosystm Principal Advisor Manoj Chugh, moderated a conversation where Indian tech and data leaders discussed building trust in data strategies. They explored ways to automate data pipelines and improve governance to drive better decisions and business outcomes. Here are the key takeaways from the session.
Data isn’t just a byproduct anymore; it’s the lifeblood of modern businesses, fuelling informed decisions and strategic growth. But with vast amounts of data, the challenge isn’t just managing it; it’s building trust. AI, once a beacon of hope, is now at risk without a reliable data foundation. Ecosystm research reveals that a staggering 66% of Indian tech leaders doubt their organisation’s data quality, and the problem of data silos is exacerbating this trust crisis.
At the Leaders Roundtable in Mumbai, I had the opportunity to moderate a discussion among data and digital leaders on the critical components of building trust in data and leveraging it to drive business value. The consensus was that building trust requires a comprehensive strategy that addresses the complexities of data management and positions the organisation for future success. Here are the key strategies that are essential for achieving these goals.
1. Adopting a Unified Data Approach
Organisations are facing a growing wave of complex workloads and business initiatives. To manage this expansion, IT teams are turning to multi-cloud, SaaS, and hybrid environments. However, this diverse landscape introduces new challenges, such as data silos, security vulnerabilities, and difficulties in ensuring interoperability between systems.
A unified data strategy is crucial to overcome these challenges. By ensuring platform consistency, robust security, and seamless data integration, organisations can simplify data management, enhance security, and align with business goals – driving informed decisions, innovation, and long-term success.
Real-time data integration is essential for timely data availability, enabling organisations to make data-driven decisions quickly and effectively. By integrating data from various sources in real-time, businesses can gain valuable insights into their operations, identify trends, and respond to changing market conditions.
Organisations that are able to integrate their IT and operational technology (OT) systems find their data accuracy increasing. By combining IT’s digital data management expertise with OT’s real-time operational insights, organisations can ensure more accurate, timely, and actionable data. This integration enables continuous monitoring and analysis of operational data, leading to faster identification of errors, more precise decision-making, and optimised processes.
2. Enhancing Data Quality with Automation and Collaboration
As the volume and complexity of data continue to grow, ensuring high data quality is essential for organisations to make accurate decisions and to drive trust in data-driven solutions. Automated data quality tools are useful for cleansing and standardising data to eliminate errors and inconsistencies.
As mentioned earlier, integrating IT and OT systems can help organisations improve operational efficiency and resilience. By leveraging data-driven insights, businesses can identify bottlenecks, optimise workflows, and proactively address potential issues before they escalate. This can lead to cost savings, increased productivity, and improved customer satisfaction.
However, while automation technologies can help, organisations must also invest in training employees in data management, data visualisation, and data governance.
3. Modernising Data Infrastructure for Agility and Innovation
In today’s fast-paced business landscape, agility is paramount. Modernising data infrastructure is essential to remain competitive – the right digital infrastructure focuses on optimising costs, boosting capacity and agility, and maximising data leverage, all while safeguarding the organisation from cyber threats. This involves migrating data lakes and warehouses to cloud platforms and adopting advanced analytics tools. However, modernisation efforts must be aligned with specific business goals, such as enhancing customer experiences, optimising operations, or driving innovation. A well-modernised data environment not only improves agility but also lays the foundation for future innovations.
Technology leaders must assess whether their data architecture supports the organisation’s evolving data requirements, considering factors such as data flows, necessary management systems, processing operations, and AI applications. The ideal data architecture should be tailored to the organisation’s specific needs, considering current and future data demands, available skills, costs, and scalability.
4. Strengthening Data Governance with a Structured Approach
Data governance is crucial for establishing trust in data, and providing a framework to manage its quality, integrity, and security throughout its lifecycle. By setting clear policies and processes, organisations can build confidence in their data, support informed decision-making, and foster stakeholder trust.
A key component of data governance is data lineage – the ability to trace the history and transformation of data from its source to its final use. Understanding this journey helps organisations verify data accuracy and integrity, ensure compliance with regulatory requirements and internal policies, improve data quality by proactively addressing issues, and enhance decision-making through context and transparency.
A tiered data governance structure, with strategic oversight at the executive level and operational tasks managed by dedicated data governance councils, ensures that data governance aligns with broader organisational goals and is implemented effectively.
Are You Ready for the Future of AI?
The ultimate goal of your data management and discovery mechanisms is to ensure that you are advancing at pace with the industry. The analytics landscape is undergoing a profound transformation, promising to revolutionise how organisations interact with data. A key innovation, the data fabric, is enabling organisations to analyse unstructured data, where the true value often lies, resulting in cleaner and more reliable data models.
GenAI has emerged as another game-changer, empowering employees across the organisation to become citizen data scientists. This democratisation of data analytics allows for a broader range of insights and fosters a more data-driven culture. Organisations can leverage GenAI to automate tasks, generate new ideas, and uncover hidden patterns in their data.
The shift from traditional dashboards to real-time conversational tools is also reshaping how data insights are delivered and acted upon. These tools enable users to ask questions in natural language, receiving immediate and relevant answers based on the underlying data. This conversational approach makes data more accessible and actionable, empowering employees to make data-driven decisions at all levels of the organisation.
To fully capitalise on these advancements, organisations need to reassess their AI/ML strategies. By ensuring that their tech initiatives align with their broader business objectives and deliver tangible returns on investment, organisations can unlock the full potential of data-driven insights and gain a competitive edge. It is equally important to build trust in AI initiatives, through a strong data foundation. This involves ensuring data quality, accuracy, and consistency, as well as implementing robust data governance practices. A solid data foundation provides the necessary groundwork for AI and GenAI models to deliver reliable and valuable insights.
As AI adoption continues to surge, the tech infrastructure market is undergoing a significant transformation. Traditional IT infrastructure providers are facing increasing pressure to innovate and adapt to the evolving demands of AI-powered applications. This shift is driving the development of new technologies and solutions that can support the intensive computational requirements and data-intensive nature of AI workloads.
At Lenovo’s recently held Asia Pacific summit in Shanghai they detailed their ‘AI for All’ strategy as they prepare for the next computing era. Building on their history as a major force in the hardware market, new AI-ready offerings will be prominent in their enhanced portfolio.
At the same time, Lenovo is adding software and services, both homegrown and with partners, to leverage their already well-established relationships with client IT teams. Sustainability is also a crucial message as it seeks to address the need for power efficiency and zero waste lifecycle management in their products.
Ecosystm Advisor Darian Bird comment on Lenovo’s recent announcements and messaging.
Click here to download Lenovo’s Innovation Roadmap: Takeaways from the APAC Analyst Summit as a PDF
1. Lenovo’s AI Strategy
Lenovo’s AI strategy focuses on launching AI PCs that leverage their computing legacy.
As the adoption of GenAI increases, there’s a growing need for edge processing to enhance privacy and performance. Lenovo, along with Microsoft, is introducing AI PCs with specialised components like CPUs, GPUs, and AI accelerators (NPUs) optimised for AI workloads.
Energy efficiency is vital for AI applications, opening doors for mobile-chip makers like Qualcomm. Lenovo’s latest ThinkPads, featuring Qualcomm’s Snapdragon X Elite processors, support Microsoft’s Copilot+ features while maximising battery life during AI tasks.
Lenovo is also investing in small language models (SLMs) that run directly on laptops, offering GenAI capabilities with lower resource demands. This allows users to interact with PCs using natural language for tasks like file searches, tech support, and personal management.
2. Lenovo’s Computer Vision Solutions
Lenovo stands out as one of the few computing hardware vendors that manufactures its own systems.
Leveraging precision engineering, Lenovo has developed solutions to automate production lines. By embedding computer vision in processes like quality inspection, equipment monitoring, and safety supervision, Lenovo customises ML algorithms using customer-specific data. Clients like McLaren Automotive use this technology to detect flaws beyond human capability, enhancing product quality and speeding up production.
Lenovo extends their computer vision expertise to retail, partnering with Sensormatic and Everseen to digitise branch operations. By analysing camera feeds, Lenovo’s solutions optimise merchandising, staffing, and design, while their checkout monitoring system detects theft and scanning errors in real-time. Australian customers have seen significant reductions in retail shrinkage after implementation.
3. AI in Action: Autonomous Robots
Like other hardware companies, Lenovo is experimenting with new devices to futureproof their portfolio.
Earlier this year, Lenovo unveiled the Daystar Bot GS, a six-legged robotic dog and an upgrade from their previous wheeled model. Resembling Boston Dynamics’ Spot but with added legs inspired by insects for enhanced stability, the bot is designed for challenging environments. Lenovo is positioning it as an automated monitoring assistant for equipment inspection and surveillance, reducing the need for additional staff. Power stations in China are already using the robot to read meters, detect temperature anomalies, and identify defective equipment.
Although it is likely to remain a niche product in the short term, the robot is an avenue for Lenovo to showcase their AI wares on a physical device, incorporating computer vision and self-guided movement.
Considerations for Lenovo’s Future Growth
Lenovo outlined an AI vision leveraging their expertise in end user computing, manufacturing, and retail. While the strategy aligns with Lenovo’s background, they should consider the following:
Hybrid AI. Initially, AI on PCs will address performance and privacy issues, but hybrid AI – integrating data across devices, clouds, and APIs – will eventually dominate.
Data Transparency & Control. The balance between convenience and privacy in AI is still unclear. Evolving transparency and control will be crucial as users adapt to new AI tools.
AI Ecosystem. AI’s value lies in data, applications, and integration, not just hardware. Hardware vendors must form deeper partnerships in these areas, as Lenovo’s focus on industry-specific solutions demonstrates.
Enhanced Experience. AI enhances operational efficiency and customer experience. Offloading level one support to AI not only cuts costs but also resolves issues faster than live agents.
The increasing alignment between IT and business functions, while crucial for organisational success, complicates the management of enterprise systems. Tech leaders must balance rapidly evolving business needs with maintaining system stability and efficiency. This dynamic adds pressure to deliver agility while ensuring long-term ERP health, making management increasingly complex.
As tech providers such as SAP enhance their capabilities and products, they will impact business processes, technology skills, and the tech landscape.
At SAP NOW Southeast Asia in Singapore, SAP presented their future roadmap, with a focus on empowering their customers to transform with agility. Ecosystm Advisors Sash Mukherjee and Tim Sheedy provide insights on SAP’s recent announcements and messaging.
Click here to download SAP NOW Southeast Asia: Highlights
What was your key takeaway from the event?
TIM SHEEDY. SAP is making a strong comeback in Asia Pacific, ramping up their RISE with SAP program after years of incremental progress. The focus is on transitioning customers from complex, highly customised legacy systems to cloud ERP, aligning with the region’s appetite for simplifying core processes, reducing customisations, and leveraging cloud benefits. Many on-prem SAP users have fallen behind on updates due to over-customisation, turning even minor upgrades into major projects – and SAP’s offerings aim to solve for these challenges.
SASH MUKHERJEE. A standout feature of the session was the compelling customer case studies. Unlike many industry events where customer stories can be generic, the stories shared were examples of SAP’s impact. From Mitr Phol’s use of SAP RISE to enhance farm-to-table transparency to CP Group’s ambitious sustainability goals aligned with the SBTi, and Standard Chartered Bank’s focus on empowering data analytics teams, these testimonials offered concrete illustrations of SAP’s value proposition.
How is SAP integrating AI into their offerings?
TIM SHEEDY. SAP, like other tech platforms, is ramping up their AI capabilities – but with a twist.
They are not only highlighting GenAI but also emphasising their predictive AI features. SAP’s approach focuses on embedded AI, integrating it directly into systems and processes to offer low-risk, user-friendly solutions.
Joule, their AI copilot, is enterprise-ready, providing seamless integration with SAP backend systems and meeting strict compliance standards like GDPR and SOC-II. By also integrating with Microsoft 365, Joule extends its reach to daily tools like Outlook, Teams, Word, and Excel.
While SAP AI may lack the flash of other platforms, it is designed for SAP users – managers and board members – who prioritise consistency, reliability, and auditability alongside business value.
What is the value proposition of SAP’s Clean Core?
SASH MUKHERJEE. SAP’s Clean Core marks a strategic shift in ERP management.
Traditionally, businesses heavily customised SAP to meet specific needs, resulting in complex and costly IT landscapes. Clean Core advocates for a standardised system with minimal customisations, offering benefits like increased agility, lower costs, and reduced risk during upgrades. However, necessary customisations can still be achieved using SAP’s BTP.
The move to the Clean Core is often driven by CEO mandates, as legacy SAP solutions have become too complex to fully leverage data. For example, an Australian mining company reduced customisations from 27,000 to 200, and Standard Chartered Bank used Clean Core data to launch a carbon program within four months.
However, the transition can be challenging and will require enhanced developer productivity, expansion of tooling, and clear migration paths.
How is SAP shifting their partner strategy?
As SAP customers face significant transformations, tech partners – cloud hyperscalers, systems integrators, consulting firms and managed services providers – will play a crucial role in executing these changes before SAP ECC loses support in 2027.
TIM SHEEDY. SAP has always relied on partners for implementations, but with fewer large-scale upgrade projects in recent years, many partner teams have shrunk. Recognising this, SAP is working to upskill partners on RISE with SAP. This effort aims to ensure they can effectively manage and optimise the modern Cloud ERP platform, utilise assets, templates, accelerators, and tools for rapid migration, and foster continuous innovation post-migration. The availability of these skills in the market will be essential for SAP customers to ensure successful transitions to the Cloud ERP platform.
SASH MUKHERJEE. SAP’s partner strategy emphasises business transformation over technology migration. This shift requires partners to focus on delivering measurable business outcomes rather than solely selling technology. Given the prevalence of partner-led sales in Southeast Asia, there is a need to empower partners with tools and resources to effectively communicate the value proposition to business decision-makers. While RISE certifications will be beneficial for larger partners, a significant portion of the market comprises SMEs that rely on smaller, local partners – and they will need support mechanisms too.
What strategies should SAP prioritise to maintain market leadership?
TIM SHEEDY. Any major platform change gives customers an opportunity to explore alternatives.
Established players like Oracle, Microsoft, and Salesforce are aggressively pursuing the ERP market. Meanwhile, industry-specific solutions, third-party support providers, and even emerging technologies like those offered by ServiceNow are challenging the traditional ERP landscape.
However, SAP has made significant strides in easing the transition from legacy platforms and is expected to continue innovating around RISE with SAP. By offering incentives and simplifying migration, SAP aims to retain their customer base. While SAP’s focus on renewal and migration could pose challenges for growth, the company’s commitment to execution suggests they will retain most of their customers. GROW with SAP is likely to be a key driver of new business, particularly in mid-sized organisations, especially if SAP can tailor offerings for the cost-sensitive markets in the region.
Exiting the North-South Highway 101 onto Mountain View, California, reveals how mundane innovation can appear in person. This Silicon Valley town, home to some of the most prominent tech giants, reveals little more than a few sprawling corporate campuses of glass and steel. As the industry evolves, its architecture naturally grows less inspiring. The most imposing structures, our modern-day coliseums, are massive energy-rich data centres, recursively training LLMs among other technologies. Yet, just as the unassuming exterior of the Googleplex conceals a maze of shiny new software, GenAI harbours immense untapped potential. And people are slowly realising that.
It has been over a year that GenAI burst onto the scene, hastening AI implementations and making AI benefits more identifiable. Today, we see successful use cases and collaborations all the time.
Finding Where Expectations Meet Reality
While the data centres of Mountain View thrum with the promise of a new era, it is crucial to have a quick reality check.
Just as the promise around dot-com startups reached a fever pitch before crashing, so too might the excitement surrounding AI be entering a period of adjustment. Every organisation appears to be looking to materialise the hype.
All eyes (including those of 15 million tourists) will be on Paris as they host the 2024 Olympics Games. The International Olympic Committee (IOC) recently introduced an AI-powered monitoring system to protect athletes from online abuse. This system demonstrates AI’s practical application, monitoring social media in real time, flagging abusive content, and ensuring athlete’s mental well-being. Online abuse is a critical issue in the 21st century. The IOC chose the right time, cause, and setting. All that is left is implementation. That’s where reality is met.
While the Googleplex doesn’t emanate the same futuristic aura as whatever is brewing within its walls, Google’s AI prowess is set to take centre stage as they partner with NBCUniversal as the official search AI partner of Team USA. By harnessing the power of their GenAI chatbot Gemini, NBCUniversal will create engaging and informative content that seamlessly integrates with their broadcasts. This will enhance viewership, making the Games more accessible and enjoyable for fans across various platforms and demographics. The move is part of NBCUniversal’s effort to modernise its coverage and attract a wider audience, including those who don’t watch live television and younger viewers who prefer online content.
From Silicon Valley to Main Street
While tech giants invest heavily in GenAI-driven product strategies, retailers and distributors must adapt to this new sales landscape.
Perhaps the promise of GenAI lies in the simple storefronts where it meets the everyday consumer. Just a short drive down the road from the Googleplex, one of many 37,000-square-foot Best Buys is preparing for a launch that could redefine how AI is sold.
In the most digitally vogue style possible, the chain retailer is rolling out Microsoft’s flagship AI-enabled PCs by training over 30,000 employees to sell and repair them and equipping over 1,000 store employees with AI skillsets. Best Buy are positioning themselves to revitalise sales, which have been declining for the past ten quarters. The company anticipates that the augmentation of AI skills across a workforce will drive future growth.
The Next Generation of User-Software Interaction
We are slowly evolving from seeking solutions to seamless integration, marking a new era of User-Centric AI.
The dynamic between humans and software has mostly been transactional: a question for an answer, or a command for execution. GenAI however, is poised to reshape this. Apple, renowned for their intuitive, user-centric ecosystem, is forging a deeper and more personalised relationship between humans and their digital tools.
Apple recently announced a collaboration with OpenAI at its WWDC, integrating ChatGPT into Siri (their digital assistant) in its new iOS 18 and macOS Sequoia rollout. According to Tim Cook, CEO, they aim to “combine generative AI with a user’s personal context to deliver truly helpful intelligence”.
Apple aims to prioritise user personalisation and control. Operating directly on the user’s device, it ensures their data remains secure while assimilating AI into their daily lives. For example, Siri now leverages “on-screen awareness” to understand both voice commands and the context of the user’s screen, enhancing its ability to assist with any task. This marks a new era of personalised GenAI, where technology understands and caters to individual needs.
We are beginning to embrace a future where LLMs assume customer-facing roles. The reality is, however, that we still live in a world where complex issues are escalated to humans.
The digital enterprise landscape is evolving. Examples such as the Salesforce Einstein Service Agent, its first fully autonomous AI agent, aim to revolutionise chatbot experiences. Built on the Einstein 1 Platform, it uses LLMs to understand context and generate conversational responses grounded in trusted business data. It offers 24/7 service, can be deployed quickly with pre-built templates, and handles simple tasks autonomously.
The technology does show promise, but it is important to acknowledge that GenAI is not yet fully equipped to handle the nuanced and complex scenarios that full customer-facing roles need. As technology progresses in the background, companies are beginning to adopt a hybrid approach, combining AI capabilities with human expertise.
AI for All: Democratising Innovation
The transformations happening inside the Googleplex, and its neighbouring giants, is undeniable. The collaborative efforts of Google, SAP, Microsoft, Apple, and Salesforce, amongst many other companies leverage GenAI in unique ways and paint a picture of a rapidly evolving tech ecosystem. It’s a landscape where AI is no longer confined to research labs or data centres, but is permeating our everyday lives, from Olympic broadcasts to customer service interactions, and even our personal devices.
The accessibility of AI is increasing, thanks to efforts like Best Buy’s employee training and Apple’s on-device AI models. Microsoft’s Copilot and Power Apps empower individuals without technical expertise to harness AI’s capabilities. Tools like Canva and Uizard empower anybody with UI/UX skills. Platforms like Coursera offer certifications in AI. It’s never been easier to self-teach and apply such important skills. While the technology continues to mature, it’s clear that the future of AI isn’t just about what the machines can do for us—it’s about what we can do with them. The on-ramp to technological discovery is no longer North-South Highway 101 or the Googleplex that lays within, but rather a network of tools and resources that’s rapidly expanding, inviting everyone to participate in the next wave of technological transformation.
When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities.
AWS Catches Up as Enterprise Gains Importance
Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure.
As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner.
AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code.
Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.
Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business.
AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases.
To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models.
Google Targeting Creators
Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria).
Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era.
At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once.
The Hardware Problem of AI
The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation.
Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance.
The Future of the GenAI Landscape
As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.
In today’s competitive business landscape, delivering exceptional customer experiences is crucial to winning new clients and fostering long-lasting customer loyalty. Research has shown that poor customer service can cost businesses around USD 75 billion in a year and that 1 in 3 customers is likely to abandon a brand after a single negative experience. Organisations excelling at personalised customer interactions across channels have a significant market edge.
In a recent webinar with Shivram Chandrasekhar, Solutions Architect at Twilio, we delved into strategies for creating this edge. How can contact centres optimise interactions to boost cost efficiency and customer satisfaction? We discussed the pivotal role of voice in providing personalised customer experiences, the importance of balancing AI and human interaction for enhanced satisfaction, and the operational advantages of voice intelligence in streamlining operations and improving agent efficiency.
The Voice Advantage
Despite the rise of digital channels, voice interactions remain crucial for organisations seeking to deliver exceptional customer experiences. Voice calls offer nuanced insights and strategic advantages, allowing businesses to address issues effectively and proactively meet customer needs, fostering loyalty and driving growth.
There are multiple reasons why voice will remain relevant including:
- In many countries it is mandatory in several industries such as Financial Services, Healthcare, & Government & Emergency Services.
- There are customers who simply favour it over other channels – the human touch is important to them.
- It proves to be the most effective when it comes to handling complex and recurrent issues, including facilitating effective negotiations and better sales closures; Digital and AI channels cannot do it alone yet.
- Analysing voice data reveals valuable patterns and customer sentiments, aiding in pinpointing areas for improvement. Unlike static metrics, voice data offers dynamic feedback, helping in proactive strategies and personalised opportunities.
AI vs the Human Agent
There has been a growing trend towards ‘agentless contact centres’, where businesses aim to pivot away from human agents – but there has also been increasing customer dissatisfaction with purely automated interactions. A balanced approach that empowers human agents with AI-driven insights and conversational AI can yield better results. In fact, the conversation should not be about one or the other, but rather about a combination of an AI + Human Agent.
Where organisations rely on conversational AI, there must be a seamless transitioning between automated and live agent interactions, maintaining a cohesive customer experience. Ultimately, the goal should be to avoid disruptions to customer journeys and ensure a smooth, integrated approach to customer engagement across different channels.
Exploring AI Opportunities in Voice Interactions
Contact centres in Asia Pacific are looking to deploy AI capabilities to enhance both employee and customer experiences.
Using predictive AI algorithms on customer data helps organisations forecast market trends and optimise resource allocation. Additionally, AI-driven identity validation swiftly confirms customer identities, mitigating fraud risks. By automating transactional tasks, particularly FAQs, contact centre operations are streamlined, ensuring that critical calls receive prompt attention. AI-powered quality assurance processes provide insights into all voice calls, facilitating continuous improvement, while AI-driven IVR systems enhance the customer experience by simplifying menu navigation.
Agent Assist solutions, integrated with GenAI, offer real-time insights before customer interactions, streamlining service delivery and saving valuable time. These solutions automate mundane tasks like call summaries, enabling agents to focus on high-value activities such as sales collaboration, proactive feedback management, and personalised outbound calls.
Actionable Data
Organisations possess a wealth of customer data from various touchpoints, including voice interactions. Accessing real-time, accurate data is essential for effective customer and agent engagement. Advanced analytics techniques can uncover hidden patterns and correlations, informing product development, marketing strategies, and operational improvements. However, organisations often face challenges with data silos and lack of interconnected data, hindering omnichannel experiences.
Integrating customer data with other organisational sources provides a holistic view of the customer journey, enabling personalised experiences and proactive problem-solving. A Customer Data Platform (CDP) breaks down data silos, providing insights to personalise interactions, address real-time issues, identify compliance gaps, and exceed customer expectations throughout their journeys.
Considerations for AI Transformation in Contact Centres
- Prioritise the availability of live agents and voice channels within Conversational AI deployments to prevent potential issues and ensure immediate human assistance when needed.
- Listen extensively to call recordings to ensure AI solutions sound authentic and emulate human conversations to enhance user adoption.
- Start with data you can trust – the quality of data fed into AI systems significantly impacts their effectiveness.
- Test continually during the solution testing phase for seamless orchestration across all communication channels and to ensure the right guardrails to manage risks effectively.
- Above all, re-think every aspect of your CX strategy – the engagement channels, agent roles, and contact centres – through an AI lens.
Ecosystm research shows that customer engagement is emerging as the main beneficiary of AI implementations in Malaysia with 44% of AI investments being led by CX/Marketing/Sales teams.
Explore the key trends that are transforming the Malaysian technology landscape and stay tuned for more data-backed insights on Southeast Asia’s tech markets.
