Meeting Market Trends and Customer Demands​: Analyst Guidance for Tech Providers

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2024 has started cautiously for organisations, with many choosing to continue with tech projects that have already initiated, while waiting for clearer market conditions before starting newer transformation projects. This means that tech providers must continue to refine their market messaging and enhance their service/product offerings to strengthen their market presence in the latter part of the year. Ecosystm analysts present five key considerations for tech providers as they navigate evolving market and customer trends, this year.

Navigating Market Dynamics

As organisations refine their AI approaches, tech providers must adjust their market strategies - Sash Mukherjee

Continuing Economic Uncertainties​. Organisations will focus on ongoing projects and consider expanding initiatives in the latter part of the year.​ This means that tech providers should maintain visibility and trust with existing clients. They also need to help their customers meet multiple KPIs. 

Popularity of Generative AI​. For organisations, this will be the time to go beyond the novelty factor and assess practical business outcomes, allied costs, and change management.​ Tech providers need to include ROI discussions for short-term and mid-term perspectives as organisations move beyond pilots.​

Infrastructure Market Disruption​. Tech leaders will keep an eye out for advancements and disruptions in the market (likely to originate from the semiconductor sector)​. The disruptions might require tech vendors to re-assess the infrastructure partner ecosystem.

Need for New Tech Skills. Tech leaders will evaluate Generative AI’s impact on AIOps and IT Architecture; invest in upskilling for talent retention.​ Tech providers must prioritise creating user-friendly experiences to make technology accessible to business users. Training and partner enablement will also need a higher focus.

​Increased Focus on Governance​. Tech leaders will consult tech vendors on how to implement safeguards for data usage, sharing, and cybersecurity.​ This opens up opportunities in offering governance-related services.​

5 Key Considerations for Tech Vendors

Click here to download ‘Meeting Market Trends and Customer Demands​: Analyst Guidance for Tech Providers’ as a PDF.

#1 Get Ready for the Year of the AI Startup

Get Ready for the Year of the AI Startup - Tim Sheedy

While many AI companies have been around for years, this will be the year that many of them make a significant play into enterprises in Asia Pacific. This comes at a time when many organisations are attempting to reduce tech debt and simplify their tech architecture. ​

For these AI startups to succeed, they will need to create watertight business cases, and do a lot of the hard work in pre-integrating their solutions with the larger platforms to reduce the time to value and simplify the systems integration work.​

To respond to these emerging threats, existing tech providers will need to not only accelerate their own use of AI in their platforms, but also ramp up the education and promotion of these capabilities. 

#2 Lead With Data, Not AI Capabilities 

Lead With Data, Not AI Capabilities - Darian Bird

Organisations recognise the need for AI to enhance their workforce, improve customer experience, and automate processes. However, the initial challenge lies in improving data quality, as trust in early AI models hinges on high-quality training data for long-term success.​

Tech vendors that can help with data source discovery, metadata analysis, and seamless data pipeline creation will emerge as trusted AI partners. Transformation tools that automate deduplication and quality assurance tasks empower data scientists to focus on high-value work. Automation models like Segment Anything enhance unstructured data labeling, particularly for images. Finally synthetic data will gain importance as quality sources become scarce.​

Tech vendors will be tempted to capitalise on the Generative AI hype but for sake of positive early experiences, they should begin with data quality.​

​​#3 Prepare Thoroughly for AI-driven Business Demand 

Prepare Thoroughly for AI-driven Business Demand - Achim Granzen

Besides pureplay AI opportunities, AI will drive a renewed and increased interest in data and data management. Tech and service providers can capitalise on this by understanding the larger picture around their clients’ data maturity and governance. Initial conversations around AI can be door openers to bigger, transformational engagements.​

Tech vendors should avoid the pitfall of downplaying AI risks. Instead, they should make all efforts to own and drive the conversation with their clients. They need to be forthcoming about their in-house responsible AI guidelines and understand what is happening in AI legislation world-wide (hint: a lot!) ​

Tech providers must establish strong client partnerships for AI initiatives to succeed. They must address risk and benefit equally to reap the benefits of larger AI-driven transformation engagements. ​

#4 Converge Network & Security Capabilities 

Converge Network & Security Capabilities- Darian Bird

Networking and security vendors will need to develop converged offerings as these two technologies increasingly overlap in the hybrid working era. Organisations are now entering a new phase of maturity as they evolve their remote working policies and invest in tools to regain control. They will require simplified management, increased visibility, and to provide a consistent user experience, wherever employees are located.​

There has already been a widespread adoption of SD-WAN and now organisations are starting to explore next generation SSE technologies. Procuring these capabilities from a single provider will help to remove complexity from networks as the number of endpoints continue to grow. ​

Tech providers should take a land and expand approach, getting a foothold with SASE modules that offer rapid ROI. They should focus on SWG and ZTNA deals with an eye to expanding in CASB and FWaaSas customers gain experience.

#5 Double Down on Your Partner Ecosystem

Double Down on Your Partner Ecosystem - Tim Sheedy

The IT services market, particularly in Asia Pacific, is poised for significant growth. Factors, including the imperative to cut IT operational costs, the growing complexity of cloud migrations and transformations, change management for Generative AI capabilities, and rising security and data governance needs, will drive increased spending on IT services.​

Tech services providers – consultants, SIs, managed services providers, and VARs – will help drive organisations’ tech spend and strategy. This is a good time to review partners, evaluating whether they can take the business forward, or whether there is a need to expand or change the partner mix.​

Partner reviews should start with an evaluation of processes and incentives to ensure they foster desired behaviour from customers and partners. Tech vendors should develop a 21st century partner program to improve chances of success.  ​

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Anticipating Tech Advances and Disruptions​: Strategic Guidance for Technology Leaders

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2024 will be another crucial year for tech leaders – through the continuing economic uncertainties, they will have to embrace transformative technologies and keep an eye on market disruptors such as infrastructure providers and AI startups. Ecosystm analysts outline the key considerations for leaders shaping their organisations’ tech landscape in 2024.​

Navigating Market Dynamics

Market Trends that will impact organisations' tech investments and roadmap in 2024 - Sash Mukherjee

Continuing Economic Uncertainties​. Organisations will focus on ongoing projects and consider expanding initiatives in the latter part of the year.​

Popularity of Generative AI​. This will be the time to go beyond the novelty factor and assess practical business outcomes, allied costs, and change management.​

Infrastructure Market Disruption​. Keeping an eye out for advancements and disruptions in the market (likely to originate from the semiconductor sector)​ will define vendor conversations.

Need for New Tech Skills​. Generative AI will influence multiple tech roles, including AIOps and IT Architecture. Retaining talent will depend on upskilling and reskilling. ​

Increased Focus on Governance​. Tech vendors are guide tech leaders on how to implement safeguards for data usage, sharing, and cybersecurity.​

5 Key Considerations for Tech Leaders​

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Click here to download ‘Anticipating ​ Tech Advances and Disruptions​: Strategic Guidance for Technology Leaders’ as a PDF.

#1 Accelerate and Adapt: Streamline IT with a DevOps Culture 

Over the next 12-18 months, advancements in AI, machine learning, automation, and cloud-native technologies will be vital in leveraging scalability and efficiency. Modernisation is imperative to boost responsiveness, efficiency, and competitiveness in today’s dynamic business landscape.​

The continued pace of disruption demands that organisations modernise their applications portfolios with agility and purpose. Legacy systems constrained by technical debt drag down velocity, impairing the ability to deliver new innovative offerings and experiences customers have grown to expect. ​

Prioritising modernisation initiatives that align with key value drivers is critical. Technology leaders should empower development teams to move beyond outdated constraints and swiftly deploy enhanced applications, microservices, and platforms. ​

Accelerate and Adapt: Streamline IT with a DevOps Culture - Clay Miller

#2 Empowering Tomorrow: Spring Clean Your Tech Legacy for New Leaders

Modernising legacy systems is a strategic and inter-generational shift that goes beyond simple technical upgrades. It requires transformation through the process of decomposing and replatforming systems – developed by previous generations – into contemporary services and signifies a fundamental realignment of your business with the evolving digital landscape of the 21st century.​

The essence of this modernisation effort is multifaceted. It not only facilitates the integration of advanced technologies but also significantly enhances business agility and drives innovation. It is an approach that prepares your organisation for impending skill gaps, particularly as the older workforce begins to retire over the next decade. Additionally, it provides a valuable opportunity to thoroughly document, reevaluate, and improve business processes. This ensures that operations are not only efficient but also aligned with current market demands, contemporary regulatory standards, and the changing expectations of customers.​

Empowering Tomorrow: Spring Clean Your Tech Legacy for New Leaders - Peter Carr

#3 Employee Retention: Consider the Strategic Role of Skills Acquisition

The agile, resilient organisation needs to be able to respond at pace to any threat or opportunity it faces. Some of this ability to respond will be related to technology platforms and architectures, but it will be the skills of employees that will dictate the pace of reform. While employee attrition rates will continue to decline in 2024 – but it will be driven by skills acquisition, not location of work.  ​

Organisations who offer ongoing staff training – recognising that their business needs new skills to become a 21st century organisation – are the ones who will see increasing rates of employee retention and happier employees. They will also be the ones who offer better customer experiences, driven by motivated employees who are committed to their personal success, knowing that the organisation values their performance and achievements. ​

Employee Retention: Consider the Strategic Role of Skills Acquisition - Tim Sheedy

#4 Next-Gen IT Operations: Explore Gen AI for Incident Avoidance and Predictive Analysis

The integration of Generative AI in IT Operations signifies a transformative shift from the automation of basic tasks, to advanced functions like incident avoidance and predictive analysis. Initially automating routine tasks, Generative AI has evolved to proactively avoiding incidents by analysing historical data and current metrics. This shift from proactive to reactive management will be crucial for maintaining uninterrupted business operations and enhancing application reliability. ​

Predictive analysis provides insight into system performance and user interaction patterns, empowering IT teams to optimise applications pre-emptively, enhancing efficiency and user experience. This also helps organisations meet sustainability goals through accurate capacity planning and resource allocation, also ensuring effective scaling of business applications to meet demands. ​

Next-Gen IT Operations: Explore Gen AI for Incident Avoidance and Predictive Analysis - Richard Wilkins

#5 Expanding Possibilities: Incorporate AI Startups into Your Portfolio

While many of the AI startups have been around for over five years, this will be the year they come into your consciousness and emerge as legitimate solutions providers to your organisation. And it comes at a difficult time for you! ​

Most tech leaders are looking to reduce technical debt – looking to consolidate their suppliers and simplify their tech architecture. Considering AI startups will mean a shift back to more rather than fewer tech suppliers; a different sourcing strategy; more focus on integration and ongoing management of the solutions; and a more complex tech architecture. ​

To meet business requirements will mean that business cases will need to be watertight – often the value will need to be delivered before a contract has been signed. ​

Expanding Possibilities: Incorporate AI Startups into Your Portfolio - Tim Sheedy
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Transformative Integration: HPE’s Acquisition of Juniper Networks

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Hewlett Packard Enterprise (HPE) has entered into a definitive agreement to acquire Juniper Networks for USD 40 per share, totaling an equity value of about USD 14 Billion. This strategic move is aimed to enhance HPE’s portfolio by focusing on higher-growth solutions and reinforcing their high-margin networking business. HPE expects to double their networking business, positioning the combined entity as a leader in networking solutions. With the growing demand for secure, unified technology driven by AI and hybrid cloud trends, HPE aims to offer comprehensive, disruptive solutions that connect, protect, and analyse data from edge to cloud.

This would also be the organisation’s largest deal since becoming an independent company in 2015. The acquisition is expected to be completed by late 2024 or early 2025.

Ecosystm analysts Darian Bird and Richard Wilkins provide their insights on the HPE acquisition and its implications for the tech market.

Converging Networking and Security

One of the big drawcards for HPE is Juniper’s Mist AI. The networking vendors have been racing to catch up – both in capabilities and in marketing. The acquisition though will give HPE a leadership position in network visibility and manageability. With GreenLake and soon Mist AI, HPE will have a solid AIOps story across the entire infrastructure.

HPE has been working steadily towards becoming a player in the converged networking-security space. They integrated Silver Peak well to make a name for themselves in SD-WAN and last year acquiring Axis Security gave them the Zero Trust Network Access (ZTNA), Secure Web Gateway (SWG), and Cloud Access Security Broker (CASB) modules in the Secure Service Edge (SSE) stack. Bringing all of this to the market with Juniper’s networking prowess positions HPE as a formidable player, especially as the Secure Access Service Edge (SASE) market gains momentum.

As the market shifts towards converged SASE, there will only be more interest in the SD-WAN and SSE vendors. In just over one year, Cato Networks and Netskope have raised funds, Check Point acquired Perimeter 81, and Versa Networks has made noises about an IPO. The networking and security players are all figuring out how they can deliver a single-vendor SASE.

Although HPE’s strategic initiatives signal a robust market position, potential challenges arise from the overlap between Aruba and Juniper. However, the distinct focus on the edge and data center, respectively, may help alleviate these concerns. The acquisition also marks HPE’s foray into the telecom space, leveraging its earlier acquisition of Athonet and establishing a significant presence among service providers. This expansion enhances HPE’s overall market influence, posing a challenge to the long-standing dominance of Cisco.

The strategic acquisition of Juniper Networks by HPE can make a transformative leap in AIOps and Software-Defined Networking (SDN). There is a potential for this to establish a new benchmark in IT management.

AI in IT Operations Transformation

The integration of Mist’s AI-driven wireless solutions and HPE’s SDN is a paradigm shift in IT operations management and will help organisations transition from a reactive to a predictive and proactive model. Mist’s predictive analytics, coupled with HPE’s SDN capabilities, empower networks to dynamically adjust to user demands and environmental changes, ensuring optimal performance and user experience. Marvis, Mist’s Virtual Network Assistant (VNA), adds conversational troubleshooting capabilities, enhancing HPE’s network solutions. The integration envisions an IT ecosystem where Juniper’s AI augments HPE’s InfoSight, providing deeper insights into network behaviour, preemptive security measures, and more autonomous IT operations.

Transforming Cloud and Edge Computing

The incorporation of Juniper’s AI into HPE’s cloud and edge computing solutions promises a significant improvement in data processing and management. AI-driven load balancing and resource allocation mechanisms will significantly enhance multi-cloud environment efficiency, ensuring robust and seamless cloud services, particularly vital in IoT applications where real-time data processing is critical. This integration not only optimises cloud operations but also has the potential to align with HPE’s commitment to sustainability, showcasing how AI advancements can contribute to energy conservation.

In summary, HPE’s acquisition of Juniper Networks, and specifically the integration of the Mist AI platform, is a pivotal step towards an AI-driven, efficient, and predictive IT infrastructure. This can redefine the standards in AIOps and SDN, creating a future where IT systems are not only reactive but also intuitively adaptive to the evolving demands of the digital landscape.

Ecosystm-Snapshot

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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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Meeting Emerging Threats with Intelligent Strategies in BFSI

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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.

What Keeps BFSI Technology Leaders awake at night?

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.

Areas that BFSI organisations are not prioritising enough today

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

An effective cybersecurity strategy demands a comprehensive approach that incorporates technology, education, and policies while nurturing a culture of security awareness throughout the organisation. CISOs face the daunting task of safeguarding their organisations against relentless cyber intrusion attempts by cybercriminals, who often leverage cutting-edge automated intrusion technologies.

To maintain an advantage over these threats, cybersecurity teams must have access to continuous threat intelligence; automation will be essential in addressing the shortage of security expertise and managing the overwhelming volume and frequency of security events. Collaborating with a specialised partner possessing both scale and experience is often the answer for organisations that want to augment their cybersecurity teams with intelligent, automated agents capable of swiftly

The Resilient Enterprise
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Redefining Network Resilience with AI

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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.  

Biggest Challenges of Running a Distributed Organisation

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.  
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The Top 5 Cloud Trends for 2023 & Beyond

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Organisations in Asia Pacific are no longer only focused on employing a cloud-first strategy – they want to host the infrastructure and workloads where it makes the most sense; and expect a seamless integration across multiple cloud environments.

While cloud can provide the agile infrastructure that underpins application modernisation, innovative leaders recognise that it is only the first step on the path towards developing AI-powered organisations. The true value of cloud is in the data layer, unifying data around the network, making it securely available wherever it is needed, and infusing AI throughout the organisation.

Cloud provides a dynamic and powerful platform on which organisations can build AI. Pre-trained foundational models, pay-as-you-go graphics superclusters, and automated ML tools for citizen data scientists are now all accessible from the cloud even to start-ups.

Organisations should assess the data and AI capabilities of their cloud providers rather than just considering it an infrastructure replacement. Cloud providers should use native services or integrations to manage the data lifecycle from labelling to model development, and deployment.

In this Ecosystm Byte, sponsored by Oracle, Ecosystm Principal Advisor, Darian Bird presents the top 5 trends for Cloud in 2023 and beyond. Read on to find out more.

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AI in Traditional Organisations: Today’s Realities

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In this Insight, guest author Anirban Mukherjee lists out the key challenges of AI adoption in traditional organisations – and how best to mitigate these challenges. “I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.”

Anirban Mukherjee, Associate Partner, Ernst & Young

After years of evangelising digital adoption, I have more of a nuanced stance today – supporting a prudent strategy, especially where the organisation’s internal capabilities/technology maturity is in question. I still see many traditional organisations burning budgets in AI adoption programs with low success rates, simply because of poor choices driven by misplaced expectations. Without going into the obvious reasons for over-exuberance (media-hype, mis-selling, FOMO, irrational valuations – the list goes on), here are few patterns that can be detected in those organisations that have succeeded getting value – and gloriously so!

Data-driven decision-making is a cultural change. Most traditional organisations have a point person/role accountable for any important decision, whose “neck is on the line”. For these organisations to change over to trusting AI decisions (with its characteristic opacity, and stochastic nature of recommendations) is often a leap too far.

Work on your change management, but more crucially, strategically choose business/process decision points (aka use-cases) to acceptably AI-enable.

Technical choice of ML modeling needs business judgement too. The more flexible non-linear models that increase prediction accuracy, invariably suffer from lower interpretability – and may be a poor choice in many business contexts. Depending upon business data volumes and accuracy, model bias-variance tradeoffs need to be made. Assessing model accuracy and its thresholds (false-positive-false-negative trade-offs) are similarly nuanced. All this implies that organisation’s domain knowledge needs to merge well with data science design. A pragmatic approach would be to not try to be cutting-edge.

Look to use proven foundational model-platforms such as those for NLP, visual analytics for first use cases. Also note that not every problem needs AI; a lot can be sorted through traditional programming (“if-then automation”) and should be. The dirty secret of the industry is that the power of a lot of products marketed as “AI-powered” is mostly traditional logic, under the hood!

In getting results from AI, most often “better data trumps better models”. Practically, this means that organisations need to spend more on data engineering effort, than on data science effort. The CDO/CIO organisation needs to build the right balance of data competencies and tools.

Get the data readiness programs started – yesterday! While the focus of data scientists is often on training an AI model, deployment of the trained model online is a whole other level of technical challenge (particularly when it comes to IT-OT and real-time integrations).

It takes time to adopt AI in traditional organisations. Building up training data and model accuracy is a slow process. Organisational changes take time – and then you have to add considerations such as data standardisation; hygiene and integration programs; and the new attention required to build capabilities in AIOps, AI adoption and governance.

Typically plan for 3 years – monitor progress and steer every 6 months. Be ready to kill “zombie” projects along the way. Train the executive team – not to code, but to understand the technology’s capabilities and limitations. This will ensure better informed buyers/consumers and help drive adoption within the organisation.

I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.

These opinions are personal (and may change with time), but definitely informed through a decade of involvement in such journeys. It is not too early for any organisation to start – results are beginning to show for those who started earlier, and we know what they got right (and wrong).

I would love to hear your views, or even engage with you on your journey!

The views and opinions mentioned in the article are personal.

Anirban Mukherjee has more than 25 years of experience in operations excellence and technology consulting across the globe, having led transformations in Energy, Engineering, and Automotive majors. Over the last decade, he has focused on Smart Manufacturing/Industry 4.0 solutions that integrate cutting-edge digital into existing operations.

The Future of AI
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