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 Many banks still rely on outdated core systems, hindering agility, innovation, and personalised customer experiences.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.
AI has become a business necessity today, catalysing innovation, efficiency, and growth by transforming extensive data into actionable insights, automating tasks, improving decision-making, boosting productivity, and enabling the creation of new products and services.
Generative AI stole the limelight in 2023 given its remarkable advancements and potential to automate various cognitive processes. However, now the real opportunity lies in leveraging this increased focus and attention to shine the AI lens on all business processes and capabilities. As organisations grasp the potential for productivity enhancements, accelerated operations, improved customer outcomes, and enhanced business performance, investment in AI capabilities is expected to surge.
In this eBook, Ecosystm VP Research Tim Sheedy and Vinod Bijlani and Aman Deep from HPE APAC share their insights on why it is crucial to establish tailored AI capabilities within the organisation.
“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
- 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
- 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
- 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
- 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.
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.
The UN’s global stocktake synthesis report underscores the need for significant efforts to meet the ambitious goals of the Paris Agreement to keep the global warming limit to 1.5ºC, compared to pre-industrial levels. Achieving this requires collective action from governments, organisations, and individuals.
While regulators focus on mandates, organisations today are being influenced more by individual responsibility for positive impact. Customers and employees are leading ESG actions – another fast-emerging voice driving ESG initiatives are value chain partners looking to build sustainable supply chains.
Ecosystm research reveals that only 27% of organisations worldwide currently view ESG as a strategic imperative, yet we anticipate rapid change in the landscape.
Click below to find out what Ecosystm analysts Gerald Mackenzie, Kaushik Ghatak, Peter Carr and Sash Mukherjee consider the top 5 ESG trends that will shape organisations’ sustainability roadmaps in 2024.
Click here to download ‘Ecosystm Predicts: Top 5 ESG Trends in 2024’ as a PDF.
#1 Organisations Will Evolve ESG Strategies from Compliance to Customer & Brand Value
Many of the organisations that we talk to have framed their ESG strategy and roadmaps primarily in relation to compliance and regulatory standards that they need to meet, e.g. in relation to emissions reporting and reduction, or in verifying that their supply chains are free from Modern Slavery.
However, organisations that are more mature in their journeys have realised that ESG is quickly becoming a strategic differentiator and compliance is only the start of their sustainability journey.
Customers, employees, and investors are increasingly selective about the brands they want to associate with and expect them to have a purpose and values that are aligned with their own.
#2 Sustainability Will Remain a Stepping-Stone to Full ESG
Heading into 2024, the corporate continues to navigate the nuances between Sustainability and Environmental, Social, and Governance (ESG) initiatives. Sustainability, focused on environmental stewardship, is a common starting point for corporate responsibility, offering measurable goals for a solid foundation.
Yet, the transition to comprehensive ESG, which includes broader social and governance issues alongside environmental concerns, demands broader scope and deeper capabilities, shifting from quantitative to qualitative measures. The trend of merging sustainability with ESG risks is blurring distinct objectives, potentially complicating reporting and compliance, and causing confusion in the market. Nevertheless, this conflation ultimately paves the way for more integrated, holistic corporate strategies.
By aligning sustainability efforts with wider ESG goals, companies will develop more comprehensive solutions that address the entire spectrum of corporate responsibility.
#3 ESG Consulting Will Grow – Till Industry Templates Take Over
At the end of 2022, LinkedIn buzzed with announcements of Chief Sustainability Officer appointments. However, the Global Sustainability Barometer Study reveals that only around one-third of global organisations have a dedicated sustainability lead. What changed?
Organisations have recognised that ESG is intricate, requiring a comprehensive focus and a capable team, not just a sustainability leader.
Each organisation’s path to sustainability is unique, shaped by factors like size, industry, location, stakeholders, culture, and values. Successfully integrating ESG requires a nuanced understanding of an organisation’s barriers, opportunities, and risks, making it challenging to navigate the sustainability journey alone. This is complicated by the absence of clear government/industry mandates and guidelines that frame best practices.
#4 Sustainability Tech Will Finally Gain Traction
Many organisations initiate sustainability journeys with promises and general strategies. While the role of technology in accelerating goals is recognised, alignment has been lacking. In 2024 sustainability tech will gain traction.
Environmental Tech. Improved sensors and analytics will enhance monitoring of air and water quality, carbon footprint, biodiversity, and climate patterns.
Carbon-Neutral Transportation. Advancements in electric and hydrogen vehicles, batteries, and clean mobility infrastructure will persist.
Circular Economy. Innovations like reverse logistics and product lifecycle tracking will help reduce waste and extend product/material life.
Smart Grids and Renewable Energy. Smart grid tech and new solutions for renewable energy integration will improve energy distribution.
#5 Cleantech Innovation Will See Increased Funding
Cleantech is the innovation that is driving our adaptation to climate change. We expect that investments into, and the pace of innovation and adoption of Cleantech will accelerate into 2024.
As companies commit to their net-zero targets, the need to operationalise the technologies required to fuel this transition becomes all the more urgent. BloombergNEF reported that for Europe alone, nearly USD 220 billion was invested in Cleantech in 2022.
But to meet net-zero ambitions, annual investments in Cleantech will need to triple over the rest of this decade and quadruple in the next.
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.
In our previous Ecosystm Insights, Ecosystm Principal Advisor, Gerald Mackenzie, highlighted the key drivers for boosting ESG maturity and the need to transition from standalone ESG projects to integrating ESG goals into organisational strategy and operations.
This shift can be difficult, requiring an alignment of ESG objectives with broader strategic aims and using organisational capabilities effectively. The solution involves prioritising essential goals, knitting them into overall business strategy, quantifying success metrics, and establishing incentives and governance for effective execution.
The benefits are proven and significant. Stronger Customer and Employee Value Propositions, better bottom line, improved risk profile, and more attractive enterprise valuations for investors and lenders.
According to Gerald, here are 5 things to keep in mind when starting on an ESG journey.
Download ‘Embedding Sustainability in Corporate Strategy and Operations’ as a PDF
It is not hyperbole to state that AI is on the cusp of having significant implications on society, business, economies, governments, individuals, cultures, politics, the arts, manufacturing, customer experience… I think you get the idea! We cannot understate the impact that AI will have on society. In times gone by, businesses tested ideas, new products, or services with small customer segments before they went live. But with AI we are all part of this experiment on the impacts of AI on society – its benefits, use cases, weaknesses, and threats.
What seemed preposterous just six months ago is not only possible but EASY! Do you want a virtual version of yourself, a friend, your CEO, or your deceased family member? Sure – just feed the data. Will succession planning be more about recording all conversations and interactions with an executive so their avatar can make the decisions when they leave? Why not? How about you turn the thousands of hours of recorded customer conversations with your contact centre team into a virtual contact centre team? Your head of product can present in multiple countries in multiple languages, tailored to the customer segments, industries, geographies, or business needs at the same moment.
AI has the potential to create digital clones of your employees, it can spread fake news as easily as real news, it can be used for deception as easily as for benefit. Is your organisation prepared for the social, personal, cultural, and emotional impacts of AI? Do you know how AI will evolve in your organisation?
When we focus on the future of AI, we often interview AI leaders, business leaders, futurists, and analysts. I haven’t seen enough focus on psychologists, sociologists, historians, academics, counselors, or even regulators! The Internet and social media changed the world more than we ever imagined – at this stage, it looks like these two were just a rehearsal for the real show – Artificial Intelligence.
Lack of Government or Industry Regulation Means You Need to Self-Regulate
These rapid developments – and the notable silence from governments, lawmakers, and regulators – make the requirement for an AI Ethics Policy for your organisation urgent! Even if you have one, it probably needs updating, as the scenarios that AI can operate within are growing and changing literally every day.
- For example, your customer service team might want to create a virtual customer service agent from a real person. What is the policy on this? How will it impact the person?
- Your marketing team might be using ChatGPT or Bard for content creation. Do you have a policy specifically for the creation and use of content using assets your business does not own?
- What data is acceptable to be ingested by a public Large Language Model (LLM). Are are you governing data at creation and publishing to ensure these policies are met?
- With the impending public launch of Microsoft’s Co-Pilot AI service, what data can be ingested by Co-Pilot? How are you governing the distribution of the insights that come out of that capability?
If policies are not put in place, data tagged, staff trained, before using a tool such as Co-Pilot, your business will be likely to break some privacy or employment laws – on the very first day!
What do the LLMs Say About AI Ethics Policies?
So where do you go when looking for an AI Ethics policy? ChatGPT and Bard of course! I asked the two for a modern AI Ethics policy.
You can read what they generated in the graphic below.
I personally prefer the ChatGPT4 version as it is more prescriptive. At the same time, I would argue that MOST of the AI tools that your business has access to today don’t meet all of these principles. And while they are tools and the ethics should dictate the way the tools are used, with AI you cannot always separate the process and outcome from the tool.
For example, a tool that is inherently designed to learn an employee’s character, style, or mannerisms cannot be unbiased if it is based on a biased opinion (and humans have biases!).
LLMs take data, content, and insights created by others, and give it to their customers to reuse. Are you happy with your website being used as a tool to train a startup on the opportunities in the markets and customers you serve?
By making content public, you acknowledge the risk of others using it. But at least they visited your website or app to consume it. Not anymore…
A Policy is Useless if it Sits on a Shelf
Your AI ethics policy needs to be more than a published document. It should be the beginning of a conversation across the entire organisation about the use of AI. Your employees need to be trained in the policy. It needs to be part of the culture of the business – particularly as low and no-code capabilities push these AI tools, practices, and capabilities into the hands of many of your employees.
Nearly every business leader I interview mentions that their organisation is an “intelligent, data-led, business.” What is the role of AI in driving this intelligent business? If being data-driven and analytical is in the DNA of your organisation, soon AI will also be at the heart of your business. You might think you can delay your investments to get it right – but your competitors may be ahead of you.
So, as you jump head-first into the AI pool, start to create, improve and/or socialise your AI Ethics Policy. It should guide your investments, protect your brand, empower your employees, and keep your business resilient and compliant with legacy and new legislation and regulations.
In my last Ecosystm Insight, I spoke about the 5 strategies that leading CX leaders follow to stay ahead of the curve. Data is at the core of these CX strategies. But a customer data breach can have an enormous financial and reputational impact on a brand.
Here are 12 essential steps to effective governance that will help you unlock the power of customer data.
- Understand data protection laws and regulations
- Create a data governance framework
- Establish data privacy and security policies
- Implement data minimisation
- Ensure data accuracy
- Obtain explicit consent
- Mask, anonymise and pseudonymise data
- Implement strong access controls
- Train employees
- Conduct risk assessments and audits
- Develop a data breach response plan
- Monitor and review
Read on to find out more.
Download ‘A 12-Step Plan for Governance of Customer Data’ as a PDF
