The impact of AI on Customer Experience (CX) has been profound and continues to expand. AI allows a a range of advantages, including improved operational efficiency, cost savings, and enhanced experiences for both customers and employees.

AI-powered solutions have the capability to analyse vast volumes of customer data in real-time, providing organisations with invaluable insights into individual preferences and behaviour. When executed effectively, the ability to capture, analyse, and leverage customer data at scale gives organisations significant competitive edge. Most importantly, AI unlocks opportunities for innovation.

Read on to discover the transformative impact of AI on customer experiences.

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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.  

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. 

It’s been barely one year since we entered the Generative AI Age. On November 30, 2022, OpenAI launched ChatGPT, with no fanfare or promotion. Since then, Generative AI has become arguably the most talked-about tech topic, both in terms of opportunities it may bring and risks that it may carry.

Trust in the Banking, Financial Services, and Insurance (BFSI) industry is critical – and this amplifies the value of stolen data and fuels the motivation of malicious actors. Ransomware attacks continue to escalate, underscoring the need for fortified backup, encryption, and intrusion prevention systems. Similarly, phishing schemes have become increasingly sophisticated, placing a burden on BFSI cyber teams to educate employees, inform customers, deploy multifactor authentication, and implement fraud detection systems. While BFSI organisations work to fortify their defences, intruders continually find new avenues for profit – cyber protection is a high-stakes game of technological cat and mouse!

Some of these challenges inherent to the industry include the rise of cryptojacking – the unauthorised use of a BFSI company’s extensive computational resources for cryptocurrency mining.

Building Trust Amidst Expanding Threat Landscape

BFSI organisations face increasing complexity in their IT landscapes. Amidst initiatives like robo-advisory, point-of-sale lending, and personalised engagements – often facilitated by cloud-based fintech providers – they encounter new intricacies. As guest access extends to bank branches and IoT devices proliferate in public settings, vulnerabilities can emerge unexpectedly. Threats may arise from diverse origins, including misconfigured ATMs, unattended security cameras, or even asset trackers. Ensuring security and maintaining customer trust requires BFSI organisations to deploy automated and intelligent security systems to respond to emerging new threats. 

Ecosystm research finds that nearly 70% of BFSI organisations have the intention of adopting AI and automation for security operations, over the next two years. But the reality is that adoption is still fairly nascent. Their top cyber focus areas remain data security, risk and compliance management, and application security.

Addressing Alert Fatigue and Control Challenges

According to Ecosystm research, 50% of BFSI organisations use more than 50 security tools to secure their infrastructure – and these are only the known tools. Cyber leaders are not only challenged with finding, assessing, and deploying the right tools, they are also challenged with managing them. Management challenges include a lack of centralised control across assets and applications and handling a high volume of security events and false positives.

Software updates and patches within the IT environment are crucial for security operations to identify and address potential vulnerabilities. Management of the IT environment should be paired with greater automation – event correlation, patching, and access management can all be improved through reduced manual processes.

Security operations teams must contend with the thousands of alerts that they receive each day. As a result, security analysts suffer from alert fatigue and struggle to recognise critical issues and novel threats. There is an urgency to deploy solutions that can help to reduce noise. For many organisations, an AI-augmented security team could de-prioritise 90% of alerts and focus on genuine risks. 

Taken a step further, tools like AIOps can not only prioritise alerts but also respond to them. Directing issues to the appropriate people, recommending actions that can be taken by operators directly in a collaboration tool, and rules-based workflows performed automatically are already possible. Additionally, by evaluating past failures and successes, AIOps can learn over time which events are likely to become critical and how to respond to them. This brings us closer to the dream of NoOps, where security operations are completely automated. 

Threat Intelligence and Visibility for a Proactive Cyber Approach

New forms of ransomware, phishing schemes, and unidentified vulnerabilities in cloud are emerging to exploit the growing attack surface of financial services organisations. Security operations teams in the BFSI sector spend most of their resources dealing with incoming alerts, leaving them with little time to proactively investigate new threats. It is evident that organisations require a partner that has the scale to maintain a data lake of threats identified by a broad range of customers even within the same industry. For greater predictive capabilities, threat intelligence should be based on research carried out on the dark web to improve situational awareness. These insights can help security operations teams to prepare for future attacks. Regular reporting to keep CIOs and CISOs informed of the changing threat landscape can also ease the mind of executives.

To ensure services can be delivered securely, BFSI organisations require additional visibility of traffic on their networks. The ability to not only inspect traffic as it passes through the firewall but to see activity within the network is critical in these increasingly complex environments. Network traffic anomaly detection uses machine learning to recognise typical traffic patterns and generates alerts for abnormal activity, such as privilege escalation or container escape. The growing acceptance of BYOD has also made device visibility more complex. By employing AI and adopting a zero-trust approach, devices can be profiled and granted appropriate access automatically. Network operators gain visibility of unknown devices and can easily enforce policies on a segmented network.

Intelligent Cyber Strategies

Here is what BFSI CISOs should prioritise to build a cyber resilient organisation.

Automation. The volume of incoming threats has grown beyond the capability of human operators to investigate manually. Increase the level of automation in your SOC to minimise the routine burden on the security operations team and allow them to focus on high-risk threats. 

Cyberattack simulation exercises. Many security teams are too busy dealing with day-to-day operations to perform simulation exercises. However, they are a vital component of response planning. Organisation-wide exercises – that include security, IT operations, and communications teams – should be conducted regularly. 

An AIOps topology map. Identify where you have reliable data sources that could be analysed by AIOps. Then select a domain by assessing the present level of observability and automation, IT skills gap, frequency of threats, and business criticality. As you add additional domains and the system learns, the value you realise from AIOps will grow. 

A trusted intelligence partner. Extend your security operations team by working with a partner that can provide threat intelligence unattainable to most individual organisations. Threat intelligence providers can pool insights gathered from a diversity of client engagements and dedicated researchers. By leveraging the experience of a partner, BFSI organisations can better plan for how they will respond to inevitable breaches. 

Conclusion

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.

The Banking, Financial Services, and Insurance (BFSI) industry, known for its cautious stance on technology, is swiftly undergoing a transformational modernisation journey. Areas such as digital customer experiences, automated fraud detection, and real-time risk assessment are all part of a technology-led roadmap. This shift is transforming the cybersecurity stance of BFSI organisations, which have conventionally favoured centralising everything within a data centre behind a firewall. 

Ecosystm research finds that 75% of BFSI technology leaders believe that a data breach is inevitable. This requires taking a new cyber approach to detect threats early, reduce the impact of an attack, and avoid lateral movement across the network.  

BFSI organisations will boost investments in two main areas over the next year: updating infrastructure and software, and exploring innovative domains like digital workplaces and automation. Cybersecurity investments are crucial in both of these areas.

As a regulated industry, breaches come with significant cost implications, underscoring the need to prioritise cybersecurity. BFSI cybersecurity and risk teams need to constantly reassess their strategies for safeguarding data and fulfilling compliance obligations, as they explore ways to facilitate new services for customers, partners, and employees.  

The primary concerns of BFSI CISOs can be categorised into two distinct groups:

1. Expanding Technology Use. This includes the proliferation of applications and devices, as well as data access beyond the network perimeter.
2. Employee-Related Vulnerabilities. This involves responses to phishing and malware attempts, as well as intentional and unintentional misuse of technology.

Vulnerabilities Arising from Employee Actions

Security vulnerabilities arising from employee actions and unawareness represent a significant and ongoing concern for businesses of all sizes and industries – the risks are just much bigger for BFSI. These vulnerabilities can lead to data breaches, financial losses, damage to reputation, and legal ramifications. A multi-pronged approach is needed that combines technology, training, policies, and a culture of security consciousness. 

Training and Culture. BFSI organisations prioritise comprehensive training and awareness programs, educating employees about common threats like phishing and best practices for safeguarding sensitive data. While these programs are often ongoing and adaptable to new threats, they can sometimes become mere compliance checklists, raising questions about their true effectiveness. Conducting simulated phishing attacks and security quizzes to assess employee awareness and identify areas where further training is required, can be effective.  

To truly educate employees on risks, it’s essential to move beyond compliance and build a cybersecurity culture throughout the organisation. This can involve setting organisation-wide security KPIs that cascade from the CEO down to every employee, promoting accountability and transparency. Creating an environment where employees feel comfortable reporting security concerns is critical for early threat detection and mitigation. 

Policies. Clear security policies and enforcement are essential for ensuring that employees understand their roles within the broader security framework, including responsibilities on strong password use, secure data handling, and prompt incident reporting. Implementing the principle of least privilege, which restricts access based on specific roles, mitigates potential harm from insider threats and inadvertent data exposure. Policies should evolve through routine security audits, including technical assessments and evaluations of employee protocol adherence, which will help organisations with a swifter identification of vulnerabilities and to take the necessary corrective actions.  

However, despite the best efforts, breaches do happen – and this is where a well-defined incident response plan, that is regularly tested and updated, is crucial to minimise the damage. This requires every employee to know their roles and responsibilities during a security incident. 

Tech Expansion Leading to Cyber Complexity

Cloud. Initially hesitant to transition essential workloads to the cloud, the BFSI industry has experienced a shift in perspective due to the rise of inventive SaaS-based Fintech tools and hybrid cloud solutions, that have created new impetus for change. This new distributed architecture requires a fresh look at cyber measures. Secure Access Service Edge (SASE) providers are integrating a range of cloud-delivered safeguards, such as FWaaS, CASB, and ZTNA with SD-WAN to ensure organisations can securely access the cloud without compromising on performance.   

Data & AI. Data holds paramount importance in the BFSI industry for informed decision-making, personalised customer experiences, risk assessment, fraud prevention, and regulatory compliance. AI applications are being used to tailor products and services, optimise operational efficiency, and stay competitive in an evolving market. As part of their technology modernisation efforts, 47% of BFSI institutions are refining their data and AI strategies. They also acknowledge the challenges associated – and satisfying risk, regulatory, and compliance requirements is one of the biggest challenges facing BFSI organisations in the AI deployments.  

Traditional network architectures are inherently fragile, often relying on a single transport type to connect branches, production facilities, and data centres. The imperative for networks to maintain resilience has grown significantly, particularly due to the delivery of customer-facing services at branches and the increasing reliance on interconnected machines in operational environments. The cost of network downtime can now be quantified in terms of both lost customers and reduced production.  

Distributed Enterprises Face New Challenges 

As the importance of maintaining resiliency grows, so does the complexity of network management.  Distributed enterprises must provide connectivity under challenging conditions, such as:  

• Remote access for employees using video conferencing 
• Local breakout for cloud services to avoid backhauling 
• IoT devices left unattended in public places 
• Customers accessing digital services at the branch or home 
• Sites in remote areas requiring the same quality of service 

Network managers require intelligent tools to remain in control without adding any unnecessary burden to end users. The number of endpoints and speed of change has made it impossible for human operators to manage without assistance from AI.  

AI-Enhanced Network Management 

Modern network operations centres are enhancing their visibility by aggregating data from diverse systems and consolidating them within a unified management platform. Machine learning (ML) and AI are employed to analyse data originating from enterprise networks, telecom Points of Presence (PoPs), IoT devices, cloud service providers, and user experience monitoring. These technologies enable the early identification of network issues before they reach critical levels. Intelligent networks can suggest strategies to enhance network resilience, forecast how modifications may impact performance, and are increasingly capable of autonomous responses to evolving conditions.  

Here are some critical ways that AI/ML can help build resilient networks.  

• Alert Noise Reduction. Network operations centres face thousands of alerts each day. As a result, operators battle with alert fatigue and are challenged to identify critical issues. Through the application of ML, contemporary monitoring tools can mitigate false positives, categorise interconnected alerts, and assist operators in prioritising the most pressing concerns. An operations team, augmented with AI capabilities could potentially de-prioritise up to 90% of alerts, allowing a concentrated focus on factors that impact network performance and resilience.  
• Data Lakes. Networking vendors are building their own proprietary data lakes built upon telemetry data generated by the infrastructure they have deployed at customer sites. This vast volume of data allows them to use ML to create a tailored baseline for each customer and to recommend actions to optimise the environment.   
• Root Cause Analysis. To assist network operators in diagnosing an issue, AIOps can sift through thousands of data points and correlate them to identify a root cause. Through the integration of alerts with change feeds, operators can understand the underlying causes of network problems or outages. By using ML to understand the customer’s unique environment, AIOps can progressively accelerate time to resolution.  
• Proactive Response. As management layers become capable of recommending corrective action, proactive response also becomes possible, leading to self-healing networks. With early identification of sub-optimal conditions, intelligent systems can conduct load balancing, redirect traffic to higher performing SaaS regions, auto-scale cloud instances, or terminate selected connections.  
• Device Profiling. In a BYOD environment, network managers require enhanced visibility to discover devices and enforce appropriate policies on them. Automated profiling against a validated database ensures guest access can be granted without adding friction to the onboarding process. With deep packet inspection, devices can be precisely classified based on behaviour patterns.  
• Dynamic Bandwidth Aggregation. A key feature of an SD-WAN is that it can incorporate diverse transport types, such as fibre, 5G, and low earth orbit (LEO) satellite connectivity. Rather than using a simple primary and redundant architecture, bandwidth aggregation allows all circuits to be used simultaneously. By infusing intelligence into the SD-WAN layer, the process of path selection can dynamically prioritise traffic by directing it over higher quality or across multiple links. This approach guarantees optimal performance, even in the face of network degradation. 
• Generative AI for Process Efficiency. Every tech company is trying to understand how they can leverage the power of Generative AI, and networking providers are no different. The most immediate use case will be to improve satisfaction and scalability for level 1 and level 2 support. A Generative AI-enabled service desk could provide uninterrupted support during high-volume periods, such as during network outages, or during off-peak hours.  

Initiating an AI-Driven Network Management Journey 

Network managers who take advantage of AI can build highly resilient networks that maximise uptime, deliver consistently high performance, and remain secure. Some important considerations when getting started include:  

• Data Catalogue. Take stock of the data sources that are available to you, whether they come from network equipment telemetry, applications, or the data lake of a managed services provider. Understand how they can be integrated into an AIOps solution.  
• Start Small. Begin with a pilot in an area where good data sources are available. This will help you assess the impact that AI could have on reducing alerts, improving mean time to repair (MTTR), increasing uptime, or addressing the skills gap.  
• Develop an SD-WAN/SASE Roadmap. Many advanced AI benefits are built into an SD-WAN or SASE. Most organisations already have or will soon adopt SD-WAN but begin assessing the SASE framework to decide if it is suitable for your organisation.  

Cyber threats are growing in volume, intensity, and complexity and are here to stay. Basic endpoint attacks are becoming intricate, multi-stage operations. Cybercriminals are launching highly coordinated and advanced attacks. This evolving threat landscape affects businesses of all sizes, jeopardising data, operations, and finances.​

In the face of massive data leaks, costly ransomware payments, and an ever-expanding and complex threat landscape, the need to strengthen digital defences has driven significant advancements in cybersecurity.​

Read on to find out how organisations, governments, industry associations and technology providers are evolving ways to combat cybercrime.​

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