Financial Services Modernisation: A Priority for Asia-Pacific in 2024

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Banks, insurers, and other financial services organisations in Asia Pacific have plenty of tech challenges and opportunities including cybersecurity and data privacy management; adapting to tech and customer demands, AI and ML integration; use of big data for personalisation; and regulatory compliance across business functions and transformation journeys.

Modernisation Projects are Back on the Table

An emerging tech challenge lies in modernising, replacing, or retiring legacy platforms and systems. Many banks still rely on outdated core systems, hindering agility, innovation, and personalised customer experiences. Migrating to modern, cloud-based systems presents challenges due to complexity, cost, and potential disruptions. Insurers are evaluating key platforms amid evolving customer needs and business models; ERP and HCM systems are up for renewal; data warehouses are transforming for the AI era; even CRM and other CX platforms are being modernised as older customer data stores and models become obsolete.

For the past five years, many financial services organisations in the region have sidelined large legacy modernisation projects, opting instead to make incremental transformations around their core systems. However, it is becoming critical for them to take action to secure their long-term survival and success.

Benefits of legacy modernisation include:

  • Improved operational efficiency and agility
  • Enhanced customer experience and satisfaction
  • Increased innovation and competitive advantage
  • Reduced security risks and compliance costs
  • Preparation for future technologies

However, legacy modernisation and migration initiatives carry significant risks.  For instance, TSB faced a USD 62M fine due to a failed mainframe migration, resulting in severe disruptions to branch operations and core banking functions like telephone, online, and mobile banking. The migration failure led to 225,492 complaints between 2018 and 2019, affecting all 550 branches and required TSB to pay more than USD 25M to customers through a redress program.

Modernisation Options

  • Rip and Replace. Replacing the entire legacy system with a modern, cloud-based solution. While offering a clean slate and faster time to value, it’s expensive, disruptive, and carries migration risks.
  • Refactoring. Rewriting key components of the legacy system with modern languages and architectures. It’s less disruptive than rip-and-replace but requires skilled developers and can still be time-consuming.
  • Encapsulation. Wrapping the legacy system with a modern API layer, allowing integration with newer applications and tools. It’s quicker and cheaper than other options but doesn’t fully address underlying limitations.
  • Microservices-based Modernisation. Breaking down the legacy system into smaller, independent services that can be individually modernised over time. It offers flexibility and agility but requires careful planning and execution.

Financial Systems on the Block for Legacy Modernisation

Data Analytics Platforms. Harnessing customer data for insights and targeted offerings is vital. Legacy data warehouses often struggle with real-time data processing and advanced analytics.

CRM Systems. Effective customer interactions require integrated CRM platforms. Outdated systems might hinder communication, personalisation, and cross-selling opportunities.

Payment Processing Systems. Legacy systems might lack support for real-time secure transactions, mobile payments, and cross-border transactions.

Core Banking Systems (CBS). The central nervous system of any bank, handling account management, transactions, and loan processing. Many Asia Pacific banks rely on aging, monolithic CBS with limited digital capabilities.

Digital Banking Platforms. While several Asia Pacific banks provide basic online banking, genuine digital transformation requires mobile-first apps with features such as instant payments, personalised financial management tools, and seamless third-party service integration.

Modernising Technical Approaches and Architectures

Numerous technical factors need to be addressed during modernisation, with decisions needing to be made upfront. Questions around data migration, testing and QA, change management, data security and development methodology (agile, waterfall or hybrid) need consideration.

Best practices in legacy migration have taught some lessons.

Adopt a data fabric platform. Many organisations find that centralising all data into a single warehouse or platform rarely justifies the time and effort invested. Businesses continually generate new data, adding sources, and updating systems. Managing data where it resides might seem complex initially. However, in the mid to longer term, this approach offers clearer benefits as it reduces the likelihood of data discrepancies, obsolescence, and governance challenges.

Focus modernisation on the customer metrics and journeys that matter. Legacy modernisation need not be an all-or-nothing initiative. While systems like mainframes may require complete replacement, even some mainframe-based software can be partially modernised to enable services for external applications and processes. Assess the potential of modernising components of existing systems rather than opting for a complete overhaul of legacy applications.

Embrace the cloud and SaaS. With the growing network of hyperscaler cloud locations and data centres, there’s likely to be a solution that enables organisations to operate in the cloud while meeting data residency requirements. Even if not available now, it could align with the timeline of a multi-year legacy modernisation project. Whenever feasible, prioritise SaaS over cloud-hosted applications to streamline management, reduce overhead, and mitigate risk.

Build for customisation for local and regional needs. Many legacy applications are highly customised, leading to inflexibility, high management costs, and complexity in integration. Today, software providers advocate minimising configuration and customisation, opting for “out-of-the-box” solutions with room for localisation. The operations in different countries may require reconfiguration due to varying regulations and competitive pressures. Architecting applications to isolate these configurations simplifies system management, facilitating continuous improvement as new services are introduced by platform providers or ISV partners.

Explore the opportunity for emerging technologies. Emerging technologies, notably AI, can significantly enhance the speed and value of new systems. In the near future, AI will automate much of the work in data migration and systems integration, reducing the need for human involvement. When humans are required, low-code or no-code tools can expedite development. Private 5G services may eliminate the need for new network builds in branches or offices. AIOps and Observability can improve system uptime at lower costs. Considering these capabilities in platform decisions and understanding the ecosystem of partners and providers can accelerate modernisation journeys and deliver value faster.

Don’t Let Analysis Paralysis Slow Down Your Journey!

Yes, there are a lot of decisions that need to be made; and yes, there is much at stake if things go wrong! However, there’s a greater risk in not taking action. Maintaining a laser-focus on the customer and business outcomes that need to be achieved will help align many decisions. Keeping the customer experience as the guiding light ensures organisations are always moving in the right direction.

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Accelerate AI Adoption: Guardrails for Effective Use

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“AI Guardrails” are often used as a method to not only get AI programs on track, but also as a way to accelerate AI investments. Projects and programs that fall within the guardrails should be easy to approve, govern, and manage – whereas those outside of the guardrails require further review by a governance team or approval body. The concept of guardrails is familiar to many tech businesses and are often applied in areas such as cybersecurity, digital initiatives, data analytics, governance, and management.

While guidance on implementing guardrails is common, organisations often leave the task of defining their specifics, including their components and functionalities, to their AI and data teams. To assist with this, Ecosystm has surveyed some leading AI users among our customers to get their insights on the guardrails that can provide added value.

Data Security, Governance, and Bias

AI: Data, Security, and Bias
  • Data Assurance. Has the organisation implemented robust data collection and processing procedures to ensure data accuracy, completeness, and relevance for the purpose of the AI model? This includes addressing issues like missing values, inconsistencies, and outliers.
  • Bias Analysis. Does the organisation analyse training data for potential biases – demographic, cultural and so on – that could lead to unfair or discriminatory outputs?
  • Bias Mitigation. Is the organisation implementing techniques like debiasing algorithms and diverse data augmentation to mitigate bias in model training?
  • Data Security. Does the organisation use strong data security measures to protect sensitive information used in training and running AI models?
  • Privacy Compliance. Is the AI opportunity compliant with relevant data privacy regulations (country and industry-specific as well as international standards) when collecting, storing, and utilising data?

Model Development and Explainability

AI: Model Development and Explainability
  • Explainable AI. Does the model use explainable AI (XAI) techniques to understand and explain how AI models reach their decisions, fostering trust and transparency?
  • Fair Algorithms. Are algorithms and models designed with fairness in mind, considering factors like equal opportunity and non-discrimination?
  • Rigorous Testing. Does the organisation conduct thorough testing and validation of AI models before deployment, ensuring they perform as intended, are robust to unexpected inputs, and avoid generating harmful outputs?

AI Deployment and Monitoring

AI: Deployment and Monitoring
  • Oversight Accountability. Has the organisation established clear roles and responsibilities for human oversight throughout the AI lifecycle, ensuring human control over critical decisions and mitigation of potential harm?
  • Continuous Monitoring. Are there mechanisms to continuously monitor AI systems for performance, bias drift, and unintended consequences, addressing any issues promptly?
  • Robust Safety. Can the organisation ensure AI systems are robust and safe, able to handle errors or unexpected situations without causing harm? This includes thorough testing and validation of AI models under diverse conditions before deployment.
  • Transparency Disclosure. Is the organisation transparent with stakeholders about AI use, including its limitations, potential risks, and how decisions made by the system are reached?

Other AI Considerations

AI: Ethical Considerations
  • Ethical Guidelines. Has the organisation developed and adhered to ethical principles for AI development and use, considering areas like privacy, fairness, accountability, and transparency?
  • Legal Compliance. Has the organisation created mechanisms to stay updated on and compliant with relevant legal and regulatory frameworks governing AI development and deployment?
  • Public Engagement. What mechanisms are there in place to encourage open discussion and engage with the public regarding the use of AI, addressing concerns and building trust?
  • Social Responsibility. Has the organisation considered the environmental and social impact of AI systems, including energy consumption, ecological footprint, and potential societal consequences?

Implementing these guardrails requires a comprehensive approach that includes policy formulation, technical measures, and ongoing oversight. It might take a little longer to set up this capability, but in the mid to longer term, it will allow organisations to accelerate AI implementations and drive a culture of responsible AI use and deployment.

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Prepare for an Explosion in IT Services Spend

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2024 and 2025 are looking good for IT services providers – particularly in Asia Pacific. All types of providers – from IT consultants to managed services VARs and systems integrators – will benefit from a few converging events.

However, amidst increasing demand, service providers are also challenged with cost control measures imposed in organisations – and this is heightened by the challenge of finding and retaining their best people as competition for skills intensifies. Providers that service mid-market clients might find it hard to compete and grow without significant process automation to compensate for the higher employee costs.

Why Organisations are Opting for IT Service

Choosing the Right Cost Model for IT Services

Buyers of IT services must implement strict cost-control measures and consider various approaches to align costs with business and customer outcomes, including different cost models:

Fixed-Price Contracts. These contracts set a firm price for the entire project or specific deliverables. Ideal when project scope is clear, they offer budget certainty upfront but demand detailed specifications, potentially leading to higher initial quotes due to the provider assuming more risk.

Time and Materials (T&M) Contracts with Caps. Payment is based on actual time and materials used, with negotiated caps to prevent budget overruns. Combining flexibility with cost predictability, this model offers some control over total expenses.

Performance-Based Pricing. Fees are tied to service provider performance, incentivising achievement of specific KPIs or milestones. This aligns provider interests with client goals, potentially resulting in cost savings and improved service quality.

Retainer Agreements with Scope Limits. Recurring fees are paid for ongoing services, with defined limits on work scope or hours within a given period. This arrangement ensures resource availability while containing expenses, particularly suitable for ongoing support services.

Other Strategies for Cost Efficiency and Effective Management

Technology leaders should also consider implementing some of the following strategies:

Phased Payments. Structuring payments in phases, tied to the completion of project milestones, helps manage cash flow and provides a financial incentive for the service provider to meet deadlines and deliverables. It also allows for regular financial reviews and adjustments if the project scope changes.

Cost Transparency and Itemisation. Detailed billing that itemises the costs of labour, materials, and other expenses provides transparency to verify charges, track spending against the budget, and identify areas for potential savings.

Volume Discounts and Negotiated Rates. Negotiating volume discounts or preferential rates for long-term or large-scale engagements, makes providers to offer reduced rates for a commitment to a certain volume of work or an extended contract duration.

Utilisation of Shared Services or Cloud Solutions. Opting for shared or cloud-based solutions where feasible, offers economies of scale and reduces the need for expensive, dedicated infrastructure and resources.

Regular Review and Adjustment. Conducting regular reviews of the services and expenses with the provider to ensure alignment with the budget and objectives, prepares organisations to adjust the scope, renegotiate terms, or implement cost-saving measures as needed.

Exit Strategy. Planning an exit strategy that include provisions for contract termination, transition services, protects an organisation in case the partnership needs to be dissolved.

Conclusion

Many businesses swing between insourcing and outsourcing technology capabilities – with the recent trend moving towards insourcing development and outsourcing infrastructure to the public cloud. But 2024 will see demand for all types of IT services across nearly every geography and industry. Tech services providers can bring significant value to your business – but improved management, monitoring, and governance will ensure that this value is delivered at a fair cost.

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Evolving Landscape: AI Startups Take Centre Stage in 2024

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The tech industry tends to move in waves, driven by the significant, disruptive changes in technology, such as cloud and smartphones. Sometimes, it is driven by external events that bring tech buyers into sync – such as Y2K and the more recent pandemic. Some tech providers, such as SAP and Microsoft, are big enough to create their own industry waves. The two primary factors shaping the current tech landscape are AI and the consequential layoffs triggered by AI advancements. 

While many of the AI startups have been around for over five years, this will be the year they emerge as legitimate solutions providers to organisations. Amidst the acceleration of AI-driven layoffs, individuals from these startups will go on to start new companies, creating the next round of startups that will add value to businesses in the future. 

Tech Sourcing Strategies Need to Change 

The increase in startups implies a change in the way businesses manage and source their tech solutions. Many organisations are trying to reduce tech debt, by typically consolidating the number of providers and tech platforms. However, leveraging the numerous AI capabilities may mean looking beyond current providers towards some of the many AI startups that are emerging in the region and globally. 

The ripple effect of these decisions is significant. If organisations opt to enhance the complexity of their technology architecture and increase the number of vendors under management, the business case must be watertight. There will be less of the trial-and-error approach towards AI from 2023, with a heightened emphasis on clear and measurable value. 

AI Startups Worth Monitoring 

Here is a selection of AI startups that are already starting to make waves across Asia Pacific and the globe. 

  • ADVANCE.AI provides digital transformation, fraud prevention, and process automation solutions for enterprise clients. The company offers services in security and compliance, digital identity verification, and biometric solutions. They partner with over 1,000 enterprise clients across Southeast Asia and India across sectors, such as Banking, Fintech, Retail, and eCommerce. 
  • Megvii is a technology company based in China that specialises in AI, particularly deep learning. The company offers full-stack solutions integrating algorithms, software, hardware, and AI-empowered IoT devices. Products include facial recognition software, image recognition, and deep learning technology for applications such as consumer IoT, city IoT, and supply chain IoT. 
  • I’mCloud is based in South Korea and specialises in AI, big data, and cloud storage solutions. The company has become a significant player in the AI and big data industry in South Korea. They offer high-quality AI-powered chatbots, including for call centres and interactive educational services. 
  • H2O.ai provides an AI platform, the H2O AI Cloud, to help businesses, government entities, non-profits, and academic institutions create, deploy, monitor, and share data models or AI applications for various use cases. The platform offers automated machine learning capabilities powered by H2O-3, H2O Hydrogen Torch, and Driverless AI, and is designed to help organisations work more efficiently on their AI projects. 
  • Frame AI provides an AI-powered customer intelligence platform. The software analyses human interactions and uses AI to understand the driving factors of business outcomes within customer service. It aims to assist executives in making real-time decisions about the customer experience by combining data about customer interactions across various platforms, such as helpdesks, contact centres, and CRM transcripts. 
  • Uizard offers a rapid, AI-powered UI design tool for designing wireframes, mockups, and prototypes in minutes. The company’s mission is to democratise design and empower non-designers to build digital, interactive products. Uizard’s AI features allow users to generate UI designs from text prompts, convert hand-drawn sketches into wireframes, and transform screenshots into editable designs. 
  • Moveworks provides an AI platform that is designed to automate employee support. The platform helps employees to automate tasks, find information, query data, receive notifications, and create content across multiple business applications. 
  • Tome develops a storytelling tool designed to reduce the time required for creating slides. The company’s online platform creates or emphasises points with narration or adds interactive embeds with live data or content from anywhere on the web, 3D renderings, and prototypes. 
  • Jasper is an AI writing tool designed to assist in generating marketing copy, such as blog posts, product descriptions, company bios, ad copy, and social media captions. It offers features such as text and image AI generation, integration with Grammarly and other Chrome extensions, revision history, auto-save, document sharing, multi-user login, and a plagiarism checker. 
  • Eightfold AI provides an AI-powered Talent Intelligence Platform to help organisations recruit, retain, and grow a diverse global workforce. The platform uses AI to match the right people to the right projects, based on their skills, potential, and learning ability, enabling organisations to make informed talent decisions. They also offer solutions for diversity, equity, and inclusion (DEI), skills intelligence, and governance, among others. 
  • Arthur provides a centralised platform for model monitoring. The company’s platform is model and platform agnostic, and monitors machine learning models to ensure they deliver accurate, transparent, and fair results. They also offer services for explainability and bias mitigation. 
  • DNSFilter is a cloud-based, AI-driven content filtering and threat protection service, that can be deployed and configured within minutes, requiring no software installation. 
  • Spot AI specialises in building a modern AI Camera System to create safer workplaces and smarter operations for every organisation. The company’s AI Camera System combines cloud and edge computing to make video footage actionable, allowing customers to instantly surface and resolve problems. They offer intelligent video recorders, IP cameras, cloud dashboards, and advanced AI alerts to proactively deliver insights without the need to manually review video footage. 
  • People.ai is an AI-powered revenue intelligence platform that helps customers win more revenue by providing sales, RevOps, marketing, enablement, and customer success teams with valuable insights. The company’s platform is designed to speed up complex enterprise sales cycles by engaging the right people in the right accounts, ultimately helping teams to sell more and faster with the same headcount.  

These examples highlight a few startups worth considering, but the landscape is rich with innovative options for organisations to explore. Similar to other emerging tech sectors, the AI startup market will undergo consolidation over time, and incumbent providers will continue to improve and innovate their own AI capabilities. Till then, these startups will continue to influence enterprise technology adoption and challenge established providers in the market.

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Microsoft Copilot’s Real Battle: Going Beyond Business Proposals and Use Cases

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Earlier in the year, Microsoft unveiled its vision for Copilot, a digital companion that aims to provide a unified user experience across Bing, Edge, Microsoft 365, and Windows. This vision includes a consistent user experience. The rollout began with Windows in September and expanded to Microsoft 365 Copilot for enterprise customers this month.

Many organisations across Asia Pacific will soon face the question on whether to invest in Microsoft 365 Copilot – despite its current limitations in supporting all regional languages. Copilot is currently supported in English (US, GB, AU, CA, IN), Japanese, and Chinese Simplified. Microsoft plans to support more languages such as Arabic, Chinese Traditional, Korean and Thai over the first half of 2024. There are still several languages used across Asia Pacific that will not be supported until at least the second half of 2024 or later.

Access to Microsoft 365 Copilot comes with certain prerequisites. Organisations need to have either a Microsoft 365 E3 or E5 license and an Azure Active Directory account. F3 licenses do not currently have access to 365 Copilot. For E3 license holders the cost per user for adding Copilot would nearly double – so it is a significant extra spend and will need to deliver measurable and tangible benefits and a strong business case. It is doubtful whether most organisations will be able to justify this extra spend.

However, Copilot has the potential to significantly enhance the productivity of knowledge workers, saving them many hours each week, with hundreds of use cases already emerging for different industries and user profiles. Microsoft is offering a plethora of information on how to best adopt, deploy, and use Copilot. The key focus when building a business case should revolve around how knowledge workers will use this extra time.

Maximising Copilot Integration: Steps to Drive Adoption and Enhance Productivity

Identifying use cases, building the business proposal, and securing funding for Copilot is only half the battle. Driving the change and ensuring all relevant employees use the new processes will be significantly harder. Consider how employees currently use their productivity tools compared to 15 years ago, with many still relying on the same features and capabilities in their Office suites as they did in earlier versions. In cases where new features were embraced, it typically occurred because knowledge workers didn’t have to make any additional efforts to incorporate them, such as the auto-type ahead functions in email or the seamless integration of Teams calls.

The ability of your organisation to seamlessly integrate Copilot into daily workflows, optimising productivity and efficiency while harnessing AI-generated data and insights for decision-making will be of paramount importance. It will be equally important to be watchful to mitigate potential risks associated with an over-reliance on AI without sufficient oversight.

Implementing Copilot will require some essential steps:

  • Training and onboarding. Provide comprehensive training to employees on how to use Copilot’s features within Microsoft 365 applications.
  • Integration into daily tasks. Encourage employees to use Copilot for drafting emails, documents, and generating meeting notes to familiarise them with its capabilities.
  • Customisation. Tailor Copilot’s settings and suggestions to align with company-specific needs and workflows.
  • Automation. Create bots, templates, integrations, and other automation functions for multiple use cases. For example, when users first log onto their PC, they could get a summary of missed emails, chats – without the need to request it.
  • Feedback loop. Implement a feedback mechanism to monitor how Copilot is used and to make adjustments based on user experiences.
  • Evaluating effectiveness. Gauge how Copilot’s features are enhancing productivity regularly and adjust usage strategies accordingly. Focus on the increased productivity – what knowledge workers now achieve with the time made available by Copilot.

Changing the behaviours of knowledge workers can be challenging – particularly for basic processes that they have been using for years or even decades. Knowledge of use cases and opportunities for Copilot will not just filter across the organisation. Implementing formal training and educational programs and backing them up with refresher courses is important to ensure compliance and productivity gains.

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Starting Strong: Successful AI Projects Start with a Proof of Concept

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The challenge of AI is that it is hard to build a business case when the outcomes are inherently uncertain. Unlike a traditional process improvement procedure, there are few guarantees that AI will solve the problem it is meant to solve. Organisations that have been experimenting with AI for some time are aware of this, and have begun to formalise their Proof of Concept (PoC) process to make it easily repeatable by anyone in the organisation who has a use case for AI. PoCs can validate assumptions, demonstrate the feasibility of an idea, and rally stakeholders behind the project.

PoCs are particularly useful at a time when AI is experiencing both heightened visibility and increased scrutiny. Boards, senior management, risk, legal and cybersecurity professionals are all scrutinising AI initiatives more closely to ensure they do not put the organisation at risk of breaking laws and regulations or damaging customer or supplier relationships.

13 Steps to Building an AI PoC

Despite seeming to be lightweight and easy to implement, a good PoC is actually methodologically sound and consistent in its approach. To implement a PoC for AI initiatives, organisations need to:

  • Clearly define the problem. Businesses need to understand and clearly articulate the problem they want AI to solve. Is it about improving customer service, automating manual processes, enhancing product recommendations, or predicting machinery failure?
  • Set clear objectives. What will success look like for the PoC? Is it about demonstrating technical feasibility, showing business value, or both? Set tangible metrics to evaluate the success of the PoC.
  • Limit the scope. PoCs should be time-bound and narrow in scope. Instead of trying to tackle a broad problem, focus on a specific use case or a subset of data.
  • Choose the right data. AI is heavily dependent on data. For a PoC, select a representative dataset that’s large enough to provide meaningful results but manageable within the constraints of the PoC.
  • Build a multidisciplinary team. Involve team members from IT, data science, business units, and other relevant stakeholders. Their combined perspectives will ensure both technical and business feasibility.
  • Prioritise speed over perfection. Use available tools and platforms to expedite the development process. It’s more important to quickly test assumptions than to build a highly polished solution.
  • Document assumptions and limitations. Clearly state any assumptions made during the PoC, as well as known limitations. This helps set expectations and can guide future work.
  • Present results clearly. Once the PoC is complete, create a clear and concise report or presentation that showcases the results, methodologies, and potential implications for the business.
  • Get feedback. Allow stakeholders to provide feedback on the PoC. This includes end-users, technical teams, and business leaders. Their insights will help refine the approach and guide future iterations.
  • Plan for the next steps. What actions need to follow a successful PoC demonstration? This might involve a pilot project with a larger scope, integrating the AI solution into existing systems, or scaling the solution across the organisation.
  • Assess costs and ROI. Evaluate the costs associated with scaling the solution and compare it with the anticipated ROI. This will be crucial for securing budget and support for further expansion.
  • Continually learn and iterate. AI is an evolving field. Use the PoC as a learning experience and be prepared to continually iterate on your solutions as technologies and business needs evolve.
  • Consider ethical and social implications. Ensure that the AI initiative respects privacy, reduces bias, and upholds the ethical standards of the organisation. This is critical for building trust and ensuring long-term success.

Customising AI for Your Business

The primary purpose of a PoC is to validate an idea quickly and with minimal risk. It should provide a clear path for decision-makers to either proceed with a more comprehensive implementation or to pivot and explore alternative solutions. It is important for the legal, risk and cybersecurity teams to be aware of the outcomes and support further implementation.

AI initiatives will inevitably drive significant productivity and customer experience improvements – but not every solution will be right for the business. At Ecosystm, we have come across organisations that have employed conversational AI in their contact centres to achieve entirely distinct results – so the AI experience of peers and competitors may not be relevant. A consistent PoC process that trains business and technology teams across the organisation and encourages experimentation at every possible opportunity, would be far more useful.

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Expanding AI Applications: From Generative AI to Business Transformation

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Generative AI has stolen the limelight in 2023 from nearly every other technology – and for good reason. The advances made by Generative AI providers have been incredible, with many human “thinking” processes now in line to be automated.  

But before we had Generative AI, there was the run-of-the-mill “traditional AI”. However, despite the traditional tag, these capabilities have a long way to run within your organisation. In fact, they are often easier to implement, have less risk (and more predictability) and are easier to generate business cases for. Traditional AI systems are often already embedded in many applications, systems, and processes, and can easily be purchased as-a-service from many providers.  

Traditional vs Generative AI

Unlocking the Potential of AI Across Industries 

Many organisations around the world are exploring AI solutions today, and the opportunities for improvement are significant: 

  • Manufacturers are designing, developing and testing in digital environments, relying on AI to predict product responses to stress and environments. In the future, Generative AI will be called upon to suggest improvements. 
  • Retailers are using AI to monitor customer behaviours and predict next steps. Algorithms are being used to drive the best outcome for the customer and the retailer, based on previous behaviours and trained outcomes. 
  • Transport and logistics businesses are using AI to minimise fuel usage and driver expenses while maximising delivery loads. Smart route planning and scheduling is ensuring timely deliveries while reducing costs and saving on vehicle maintenance. 
  • Warehouses are enhancing the safety of their environments and efficiently moving goods with AI. Through a combination of video analytics, connected IoT devices, and logistical software, they are maximising the potential of their limited space. 
  • Public infrastructure providers (such as shopping centres, public transport providers etc) are using AI to monitor public safety. Video analytics and sensors is helping safety and security teams take public safety beyond traditional human monitoring. 

AI Impacts Multiple Roles 

Even within the organisation, different lines of business expect different outcomes for AI implementations. 

  • IT teams are monitoring infrastructure, applications, and transactions – to better understand root-cause analysis and predict upcoming failures – using AI. In fact, AIOps, one of the fastest-growing areas of AI, yields substantial productivity gains for tech teams and boosts reliability for both customers and employees. 
  • Finance teams are leveraging AI to understand customer payment patterns and automate the issuance of invoices and reminders, a capability increasingly being integrated into modern finance systems. 
  • Sales teams are using AI to discover the best prospects to target and what offers they are most likely to respond to.  
  • Contact centres are monitoring calls, automating suggestions, summarising records, and scheduling follow-up actions through conversational AI. This is allowing to get agents up to speed in a shorter period, ensuring greater customer satisfaction and increased brand loyalty. 

Transitioning from Low-Risk to AI-Infused Growth 

These are just a tiny selection of the opportunities for AI. And few of these need testing or business cases – many of these capabilities are available out-of-the-box or out of the cloud. They don’t need deep analysis by risk, legal, or cybersecurity teams. They just need a champion to make the call and switch them on.  

One potential downside of Generative AI is that it is drawing unwarranted attention to well-established, low-risk AI applications. Many of these do not require much time from data scientists – and if they do, the challenge is often finding the data and creating the algorithm. Humans can typically understand the logic and rules that the models create – unlike Generative AI, where the outcome cannot be reverse-engineered. 

The opportunity today is to take advantage of the attention that LLMs and other Generative AI engines are getting to incorporate AI into every conceivable aspect of a business. When organisations understand the opportunities for productivity improvements, speed enhancement, better customer outcomes and improved business performance, the spend on AI capabilities will skyrocket. Ecosystm estimates that for most organisations, AI spend will be less than 5% of their total tech spend in 2024 – but it is likely to grow to over 20% within the next 4-5 years. 

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Return to Office Challenges: Aligning Employee and Manager Expectations

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It seems for many employees, the benefits of working from home or even adopting a hybrid model are a thing of the past. Employees are returning to the grind of long commutes and losing hours in transit. What is driving this shift in sentiment? CEOs, who once rooted for remote work, have undergone a change of heart – many say that remote work hampers their ability to innovate.

That may not be the real reason, however. There is a good chance that the CEO and/or other managers feel they have lost control or visibility over their employees. Returning to a more traditional management approach, where everyone is within direct sight, might seem like a simpler solution.  

The Myths of Workplace Innovation

I find it ironic that organisations say they want employees to come into the office because they cannot innovate at the same rate. What the last few years have demonstrated – and quite conclusively – is that employees can innovate wherever they are, if they are driven to it and have the right tools. So, organisations need to evaluate whether they have innovated on and evolved their hybrid and remote work solutions effectively, to continue to support hybrid work – and innovation.

What is confusing about this stance that many organisations are taking, is that when an organisation has multiple offices, they are effectively a hybrid business – they have had people working from different locations, but have never felt the need to get all their staff together for 3-5 days every week for organisation-wide innovation that is suddenly so important today.

The CEO of a tech research firm once said – the office used to be considered the place to get together to use the tools we need to innovate; but the reality is that the office is just one of the tools that businesses have, to drive their organisation forward. Ironically, this same CEO has recently called everyone back into the office 3 days a week!

Is Remote Work the Next Step in Employee Rights?

It has become clear that remote and hybrid work is the next step in employees getting greater rights. Many organisations fought against the five-day work weeks, claiming they wouldn’t make as much money as they did when employees worked whenever they were told. They fought against the 40-hour work week (in France some fought against the 35-hour work week!) They fight against the introduction of new public holidays, against increases to the minimum wages, against paid parental leave.

Some industries, companies, unions, and countries are looking to (or already have) formalised hybrid and remote work in their policies and regulations. More unions and businesses will do this – and employees will have choice.

People will have the option to work for an employer who wants their employees to come into the office – or work for someone else. And this will depend on preferences and working styles – some employees enjoy the time spent away from home and like the social nature of office environments. But many also like the extra time, money, and flexibility that remote work allows.

There might be many reasons why leadership teams would want employees to come into the office – and establishing and maintaining a common corporate culture would be a leading reason. But what they need to do is stop pretending it is about “innovation”. Innovation is possible while working remotely, as it is when working from separate offices or even different floors within the same building. 

Evolving Employee Experience & Collaboration Needs

Organisations today face a challenge – and it is not the inability to innovate in a hybrid work environment! It is in their ability to deliver the employee experience that their employees want. This is more challenging now because there are more preferences, options, and technologies available. But it is established that organisations need to continue to evolve their employee experience.

Technology does and will continue to play an important role in keeping our employees connected and productive. AI – such as Microsoft Copilot – will continue to improve our productivity. But the management needs to evolve with the technology. If the senior management feels that connecting people will help to solve the current growth challenges in the business, then it becomes the role of managers to better connect people – not just teams in offices, but virtual teams across the entire organisation.

Organisations that have focused their energies on connecting their employees better, regardless of their location (such as REA in Australia), find that productivity and innovation rates are better than when people are physically together. What do they do differently?

  • Managers find their roles have moved from supporting individual employees to connecting employees
  • Documentation of progress and challenges means that everyone knows where to focus their energies
  • Managed virtual (and in-person) meetings mean that everyone has a voice and gets to contribute (not the loudest, most talkative or most senior person)

Remote and hybrid workers are often well-positioned to come up with new and innovative ideas. Senior management can encourage innovation and risk-taking by creating a safe environment for employees to share their ideas and by providing them with the resources they need to develop and implement their ideas. Sometimes these resources are in an office – but they don’t have to be. Manufacturers are quickly moving to complete digital development, prototyping, and testing of their new and improved products and services. Digital is often faster, better, and more innovative than physical – but employees need to be allowed to embrace these new platforms and tools to drive better organisational and customer outcomes.

What the pandemic has taught us is that people are good at solving problems; they are good at innovating irrespective of whether their managers are watching or not.

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Breaches are Inevitable – Build Resiliency through Recovery & Backup

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A lot gets written about cybersecurity – and organisations spend a lot on it! Ecosystm research finds that 63% of organisations across Asia Pacific are planning to increase their cyber budget for the next year. As budgets continue to rise, the threat landscape continues to get more complex and difficult to navigate. Despite increasing spend, 69% of organisations believe a breach is inevitable. And breaches can be EXPENSIVE! Medibank, in Australia, was breached in (or around) October, 2022. The cost of the breach is expected to reach around USD 52 million when everything is done and dusted – and this does not include the impacts of any potential findings or outcomes from regulatory investigations or litigation.

Recovering Strong

While cybersecurity is still crucially important, the ability to recover from breaches quickly and cost-effectively is also imperative. How you recover from a breach will ultimately determine your organisation’s long-term viability and success. The capabilities needed to recover quickly include:

  • A well-documented and practices incident response plan. The plan should outline the roles and responsibilities of all team members, communication protocols, and steps to be taken in the event of a breach.
  • Backup and Disaster Recovery (DR) solutions. Regular backups of critical data and systems are essential to quickly recover from a breach. Backup solutions should include offsite or cloud-based options that are isolated from the main network. DR solutions ensure that critical systems can be quickly restored and made operational after a breach.
  • Cybersecurity awareness training. Investing in regular training for all employees is crucial to ensure they are aware of the latest threats and know how to respond in the event of a breach.
  • Automated response tools. Automation can help speed up the response time during a breach by automatically blocking malicious IPs, quarantining infected devices, or taking other predefined actions based on the nature of the attack.
  • Threat intelligence. This can help organisations stay ahead of the latest threats and vulnerabilities and frame quicker responses if a breach occurs.

Backup and Disaster Recovery is Evolving

Most organisations already have backup and disaster recovery capabilities in place – but too often they are older systems, designed more as a “just in case” versus a “will keep us in business” capability. Backup and DR systems are evolving and improving – and with the increased likelihood of a breach, it is a good time to consider what a modern Backup and DR system can provide to your organisation. Here are some of the key trends and considerations that technology leaders should be aware of:

  • Cloud-based solutions. More organisations are moving towards cloud-based backup and DR solutions. Cloud solutions offer several advantages, including scalability, cost-effectiveness, and the ability to access data and systems from anywhere. However, technology leaders need to consider data security, compliance requirements, and the reliability of the cloud service provider.
  • Hybrid options. As hybrid cloud becomes the norm for most organisations, hybrid solutions backup and DR that combine on-premises and cloud-based backups are becoming more popular. This approach provides the best of both worlds – the security and control of on-premises backups with the scalability and flexibility of the cloud.
  • Increased use of automation. Automation is becoming more prevalent in backup and DR solutions. Automation helps reduce the time it takes to backup data, restore systems, and test DR plans. It also minimises the risk of human error. Technology leaders should look for solutions that offer automation capabilities while also allowing for manual intervention when necessary.
  • Cybersecurity integration. With the rise of cyberattacks, especially ransomware, it is crucial that backup and DR solutions are integrated with an organisation’s cybersecurity strategy. Backup data should be encrypted and isolated from the main network to prevent attackers from accessing or corrupting it. Regular testing of backup and DR plans should also include scenarios where a cyberattack, such as ransomware, is involved.
  • More frequent backups. Data is becoming more critical to business operations, so there is a trend towards more frequent backups, even continuous backups, to minimise data loss in the event of a disaster. Technology leaders need to balance the need for frequent backups with the cost and complexity involved.
  • Super-fast data recovery. Some data recovery platforms can recover data FAST – in as little as 6 seconds. The ability to recover data faster than the bad actors can delete it makes organisations less vulnerable and buys more time to plug the gaps that the attackers are exploiting to gain access to data and systems.
  • Monitoring and analytics. Modern backup and DR solutions offer advanced monitoring and analytics capabilities. This allows organisations to track the performance of their backups, identify potential issues before they become critical, and optimise their backup and DR processes. Technology leaders should look for solutions that offer comprehensive monitoring and analytics capabilities.
  • Compliance considerations. With the increasing focus on data privacy and protection, organisations need to ensure that backup and DR solutions are compliant with relevant regulations, often dictated at the industry level in each geography. Technology leaders should work with their legal and compliance teams to ensure that their backup and DR solutions meet all necessary requirements.

The sooner you evolve and modernise your backup and disaster recovery capabilities, the more breathing room your cybersecurity team has, to improve the ability to repel threats. New security architectures and postures – such as Zero Trust and SASE are emerging as better ways to build your cybersecurity capabilities – but they won’t happen overnight and require significant investment, training, and business change to implement. 

The Resilient Enterprise
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