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AI is redefining HR – are you ready?
Here’s some data-backed guide to advancing AI in HR for 2025. It’s no longer just about recruitment; AI is reshaping everything from employee experience to strategic decision-making.
To stay ahead, focus on:
• Setting clear goals with HR-specific KPIs
• Prioritising ethics & transparency in AI use
• Embedding Human-AI collaboration into HR practices
• Taking a seat at the table in AI strategy discussions
AI has broken free from the IT department. It’s no longer a futuristic concept but a present-day reality transforming every facet of business. Departments across the enterprise are now empowered to harness AI directly, fuelling innovation and efficiency without waiting for IT’s stamp of approval. The result? A more agile, data-driven organisation where AI unlocks value and drives competitive advantage.
Ecosystm’s research over the past two years, including surveys and in-depth conversations with business and technology leaders, confirms this trend: AI is the dominant theme. And while the potential is clear, the journey is just beginning.
Here are key AI insights for HR Leaders from our research.
Click here to download “AI Stakeholders: The HR Perspective” as a PDF.
Our research reveals a fascinating dynamic in HR. While 54% of HR leaders currently use AI for recruitment (scanning resumes, etc.), their vision extends far beyond. A striking majority plan to expand AI’s reach into crucial areas: 74% for workforce planning, 68% for talent development and training, and 62% for streamlining employee onboarding.
The impact is tangible, with organisations already seeing significant benefits. GenAI has streamlined presentation creation for bank employees, allowing them to focus on content rather than formatting and improving efficiency. Integrating GenAI into knowledge bases has simplified access to internal information, making it quicker and easier for employees to find answers. AI-driven recruitment screening is accelerating hiring in the insurance sector by analysing resumes and applications to identify top candidates efficiently. Meanwhile, AI-powered workforce management systems are transforming field worker management by optimising job assignments, enabling real-time tracking, and ensuring quick responses to changes.
Despite this promising outlook, HR leaders face significant hurdles. Limited exploration of use cases, the absence of a unified organisational AI strategy, and ethical concerns are among the key barriers to wider AI deployments.
Perhaps most concerning is the limited role HR plays in shaping AI strategy. While 57% of tech and business leaders cite increased productivity as the main driver for AI investments, HR’s influence is surprisingly weak. Only 20% of HR leaders define AI use cases, manage implementation, or are involved in governance and ownership. A mere 8% primarily manage AI solutions.
This disconnect represents a massive opportunity.
Despite these challenges, our research indicates HR leaders are prioritising AI for 2025. Increased productivity is the top expected outcome, while three in ten will focus on identifying better HR use cases as part of a broader data-centric approach.
The message is clear: HR needs to step up and claim its seat at the AI table. By proactively defining use cases, championing ethical considerations, and collaborating closely with tech teams, HR can transform itself into a strategic driver of AI adoption, unlocking the full potential of this transformative technology for the entire organisation. The future of HR is intelligent, and it’s time for HR leaders to embrace it.
The Customer Experience (CX) space is set to witness significant advancements in 2025, particularly with the rise of Agentic AI.
Unlike GenAI, which despite enormous promise, has struggled to deliver scalable solutions, Agentic AI offers dynamic, scalable improvements for brands.
With AI agents and an expanding digital AI workforce, front and back-office automation is becoming more independent.
These AI-driven systems will enable precise information retrieval, intelligent, human-like conversations, autonomous decision-making, and seamless customer interactions without constant intervention from CX teams.
Click here to download “An Agentic AI Perspective” as a PDF.
Traditional Conversational AI has faced persistent challenges that have hindered its widespread adoption. Many solutions lack contextual awareness, limiting their ability to engage proactively. Siloed back-end data often restricts these systems from making autonomous decisions, while predefined conversational boundaries prevent seamless, natural interactions.
Despite advancements, organisations deploying Conversational AI continue to encounter significant issues:
These limitations have slowed adoption, particularly in the Asia Pacific region, where enterprises remain cautious, opting for pilots and tests over large-scale deployments.
Adding to the complexity is the challenge of handling local languages like Thai, Bahasa, Chinese, and Indian languages, as well as nuanced regional English dialects, which AI often struggles to interpret accurately.
Agentic AI is poised to revolutionise Conversational AI by addressing these longstanding challenges. Unlike traditional systems, Agentic AI offers the ability to retrieve precise information, engage in intelligent, human-like conversations, and make autonomous decisions based on vast amounts of customer metadata.
Agentic AI empowers enterprises to create conversational flows that are not only seamless but also adaptive to context and behaviour.
It enables CX systems to overcome language barriers, handle unstructured data dynamically, and deliver faster, more personalised responses. By doing so, Agentic AI enhances customer satisfaction, drives efficiency, and unlocks the potential for proactive, intelligent engagement at scale.
Simpler use cases like balance checks, order confirmations, and structured dialogues have garnered positive feedback. Improvements have been achieved through better conversational design and integrating diverse data into unified repositories.
Agent Assist solutions have seen strong adoption in 2024. New developments in AI agents as a digital workforce are unlocking remarkable outcomes. These agents can analyse unstructured CX data, enabling faster, context-rich conversations.
In 2025, AI agents with agentic capabilities will make independent decisions, learn from context, solve complex problems, and adapt dynamically based on customer interactions.
CX solution buyers and decision-makers must prepare for the transformative potential of Agentic AI.
Undersea cables form the invisible backbone of the modern internet, carrying vast amounts of data across continents and connecting billions of people. These vital arteries of global communication are, however, surprisingly vulnerable.
Recent incidents have highlighted the vulnerability of undersea infrastructure, particularly in the Baltic Sea. In the latest case, a fibre optic cable between Latvia and Sweden was reportedly severed by the dragging anchor of the cargo ship Vezhen, originating from Russia’s Ust-Luga port. Swedish authorities boarded and seized the vessel.
In December, the Eagle S Panamax oil tanker, sailing from St. Petersburg, allegedly damaged a power cable and three fibre optic cables between Estonia and Finland, as well as another connection between Finland and Germany. Finnish authorities seized the ship for investigation. A similar incident occurred in November when the Yi Peng 3, also from Ust-Luga, was linked to cable ruptures connecting Sweden to Lithuania and Finland to Germany. Although shadowed by the Royal Danish Navy, the vessel was ultimately allowed to continue its voyage.
The suspected sabotage of 11 undersea cables in 15 months has alarmed NATO countries, prompting increased surveillance around Europe. Patrols will focus on protecting critical assets like fibre optic cables, power lines, gas pipelines, and environmental sensors. Dubbed Baltic Sentry, the mission will deploy frigates, patrol aircraft, and unmanned naval drones, supported by NATO’s Maritime Centre for the Security of Critical Undersea Infrastructure. An AI system will monitor unusual shipping activity, such as loitering near cables or erratic course changes, aiming to cut response times to 30-60 minutes. Meanwhile, Operation Nordic Warden will analyse satellite imagery, patrol data, and Automatic Identification System (AIS) signals to assess risks in 22 key areas.
The primary concern is damage to infrastructure in the shallow waters of the Baltic Sea, but suspicious activity elsewhere has caught the attention of tech giants. Ireland, a critical hub for Europe’s cloud data centres, hosts undersea cables owned by companies like Google, Microsoft, and Amazon, linking it to the US and UK. As a non-NATO country, Ireland faces the challenge of monitoring over 3,000km of coastline. Recently, both the Irish Defence Forces and Royal Navy shadowed a Russian spy ship in the Irish Sea and English Channel. While cable damage is often immediately evident, the risk of communication taps is more alarming and harder to detect.
There are about 400 undersea cables spanning over 1.3 million kms globally. According to the International Cable Protection Committee, around 200 incidents of cable damage occur annually, mostly caused by dragged anchors or trawling. Only about 10% result from natural causes like weather or wildlife. Near shorelines, cables are heavily protected and often buried under several metres of sand in shallow waters. However, in deeper seas, they are harder to monitor and safeguard.
Highly developed regions, such as the Baltic Sea, North Sea, and Irish Sea, rely on multiple redundant cables to maintain connections between countries. While severing a single link may reduce capacity and cause inconvenience, major disruptions are rare, even for remote European islands served by multiple cables.
Fibre optic cable repairs typically take days to weeks, faster than the lengthy timelines for fixing power cables or gas pipelines. Repair costs range from USD 1-3 million depending on the damage. Faults are located using test pulses, and specialised ships lift the damaged sections to the surface for splicing. However, with only 22 repair-designated cable ships worldwide, simultaneous outages could significantly delay restoration.
In regions with less cooperative neighbours, obtaining permissions can further slow repairs. For instance, cables crossing the South China Sea face increasing challenges in deployment and maintenance, complicating connections between ASEAN nations. Routing cables along longer coastal paths raises costs and impacts latency, adding further strain to the network.
Plausible deniability and the opaque nature of maritime operations make attributing these events challenging. Nonetheless, NATO countries view them as part of Russia’s broader hybrid warfare strategy, which avoids direct confrontation while instilling fear and uncertainty by showcasing an adversary’s reach. Attacks on undersea cables undermine public trust in a government’s ability to protect critical infrastructure.
European governments initially downplayed the impact of these attacks, likely to minimise psychological effects and avoid escalation. While this cautious approach, coupled with rapid repairs, proved effective in the short term, it may have emboldened adversaries, leading to further incidents. In response, Sweden and Finland are now more willing to seize vessels in their territorial waters to deter both intentional and negligent actions.
While enterprises cannot prevent damage to undersea infrastructure, they can mitigate risks and build resilient networks:
Welcome to 2025, the Year of the Snake – now enhanced, of course, with AI-powered features! While 2023 and 2024 saw a surprising global consensus on the potential risks of AI and the need for careful management (think AI legislation), the opening weeks of 2025 have thrown a new, and perhaps more pressing, concern into the spotlight: cost.
The recent unveiling of Project Stargate sent ripples throughout the tech world, not just for its ambitious goals, but for its staggering price tag: a cool USD 500B over four years. Let that sink in. That’s roughly the equivalent of Singapore’s entire GDP in 2023. For context, that kind of money could fund the entire Apollo program and build two International Space Stations, with some spending money left over. It’s a figure that underscores the sheer scale of investment required to push the boundaries of AI.
But then, the plot thickened. A relatively unknown Chinese company, DeepSeek, seemingly out of nowhere, launched its R1 large language model (LLM). Not only does R1 appear to be a direct competitor to OpenAI’s latest offerings, but DeepSeek also claims to have achieved this feat at a fraction of the cost, and using fewer (and potentially less powerful) GPUs. This announcement sent shockwaves through the stock market on January 27th, impacting nearly every stock associated with AI chip manufacturing. Nvidia (NVDA), a key player in the AI hardware space, suffered one of the biggest single-day losses in US stock market history, with nearly USD 600B wiped off its market capitalisation. Ironically, that’s more than Project Stargate’s entire budget plus the cost of an ISS.
This dramatic market reaction highlights several critical trends emerging in 2025. The previously observed consensus on AI risks and legislation is already beginning to fracture (witness the recent back-and-forth on AI regulation). Meanwhile, the exorbitant cost of AI development is becoming increasingly apparent. We’re also seeing a renewed West versus (Far) East rivalry playing out in the AI arena, extending beyond just technological competition. And finally, the age-old debate between open-source and proprietary software is back, with some LLMs, like DeepSeek’s R1, leaning more towards open access than others.
For organisations considering investing in AI, and indeed for all of us whose lives are increasingly touched by AI developments, it’s crucial to keep a close watch on these powerful trends. The risks, the investments, and the potential benefits of AI must be carefully scrutinised and potentially reassessed. The recent stock market correction suggests a necessary pushback against the over-confidence and over-spending that has characterised some areas of AI development. As DeepSeek’s R1 has shown, sometimes it doesn’t take much to disrupt the party.
The question now is: how will the landscape shift, and who will emerge as the true leaders in this expensive, yet potentially transformative, race?
The Australian financial services industry is experiencing a period of rapid transformation driven by technological advancements and shifting consumer expectations. To remain competitive, financial organisations must balance emerging technologies with security, customer experience, and regulatory compliance.
AI adoption offers the potential to revolutionise operations, from automating fraud prevention and streamlining processes to enhancing customer interactions through chatbots. However, to fully harness AI’s potential, organisations must integrate these technologies within a strong cybersecurity framework, ensuring data integrity and security.
This whitepaper delves into the strategic approach required for effectively leveraging AI in the financial services sector. It outlines five key strategies for tech leaders, focusing on data management, integration, cloud optimisation, and cybersecurity. By aligning AI initiatives with robust data frameworks, organisations can overcome challenges and drive superior outcomes.
Download the whitepaper to uncover best practices and strategies to guide your AI journey in Australia’s BFSI sector.
Download Whitepaper – Data-Driven Success: Best Practices For Australia’s Banking, Financial Services, & Insurance Organisations
(Clicking on this link will take you to the Nexon website where you can download the whitepaper)
AI is reshaping the tech infrastructure landscape, demanding a fundamental rethinking of organisational infrastructure strategies. Traditional infrastructure, once sufficient, now struggles to keep pace with the immense scale and complexity of AI workloads. To meet these demands, organisations are turning to high-performance computing (HPC) solutions, leveraging powerful GPUs and specialised accelerators to handle the computationally intensive nature of AI algorithms.
Real-time AI applications, from fraud detection to autonomous vehicles, require lightning-fast processing speeds and low latency. This is driving the adoption of high-speed networks and edge computing, enabling data processing closer to the source and reducing response times. AI-driven automation is also streamlining infrastructure management, automating tasks like network provisioning, security monitoring, and capacity planning. This not only reduces operational overhead but also improves efficiency and frees up valuable resources.
Ecosystm analysts Darian Bird, Peter Carr, Simona Dimovski, and Tim Sheedy present the key trends shaping the tech infrastructure market in 2025.
Click here to download ‘Building the AI Future: Top 5 Infra Trends for 2025’ as a PDF
In 2025, the race for AI dominance will intensify, with Nvidia emerging as the big winner despite an impending AI crash. Many over-invested companies will fold, flooding the market with high-quality gear at bargain prices. Meanwhile, surging demand for AI infrastructure – spanning storage, servers, GPUs, networking, and software like observability, hybrid cloud tools, and cybersecurity – will make it a strong year for the tech infrastructure sector.
Ironically, China’s exclusion from US tech deals has spurred its rise as a global tech giant. Forced to develop its own solutions, China is now exporting its technologies to friendly nations worldwide.
By 2025, Chinese chipmakers are expected to rival international peers, with some reaching parity.
AI-optimised cloud platforms will become the go-to infrastructure for organisations, enabling seamless integration of machine learning capabilities, scalable compute power, and efficient deployment tools.
As regulatory demands grow and AI workloads become more complex, these platforms will provide localised, compliant solutions that meet data privacy laws while delivering superior performance.
This shift will allow businesses to overcome the limitations of traditional infrastructure, democratising access to high-performance AI resources and lowering entry barriers for smaller organisations. AI-optimised cloud platforms will drive operational efficiencies, foster innovation, and help businesses maintain compliance, particularly in highly regulated industries.
By 2025, as AI adoption reaches new heights, organisations will face an urgent need for AI-ready data, spurring significant investments in data infrastructure. However, the approach taken will be pivotal.
A stark divide will arise between businesses fixated on isolated data-cleaning initiatives and those embracing a Platform-as-a-Service (PaaS) architecture.
The former will struggle, often unintentionally creating more fragmented systems that increase complexity and cybersecurity risks. While data cleansing is important, focusing exclusively on it without a broader architectural vision leads to diminishing returns. On the other hand, organisations adopting PaaS architectures from the start will gain a distinct advantage through seamless integration, centralised data management, and large-scale automation, all critical for AI.
While LLMs have captured most of the headlines, small language models (SLMs) will soon help to drive AI use at the edge. These compact but powerful models are designed to operate efficiently on limited hardware, like AI PCs, wearables, vehicles, and robots. Their small size translates into energy efficiency, making them particularly useful in mobile applications. They also help to mitigate the alarming electricity consumption forecasts that could make widespread AI adoption unsustainable.
Self-contained SMLs can function independently of the cloud, allowing them to perform tasks that require low latency or without Internet access.
Connected machines in factories, warehouses, and other industrial environments will have the benefit of AI without the burden of a continuous link to the cloud.
AI PCs have been a key trend in 2024, with most brands launching AI-enabled laptops. However, enterprise feedback has been tepid as user experiences remain unchanged. Most AI use cases still rely on the public cloud, and applications have yet to be re-architected to fully leverage NPUs. Where optimisation exists, it mainly improves graphics efficiency, not smarter capabilities. Currently, the main benefit is extended battery life, explaining the absence of AI in desktop PCs, which don’t rely on batteries.
The market for AI PCs will grow as organisations and consumers adopt them, creating incentives for developers to re-architect software to leverage NPUs.
This evolution will enable better data access, storage, security, and new user-centric capabilities. However, meaningful AI benefits from these devices are still several years away.
Ecosystm research shows that cybersecurity is the most discussed technology at the Board and Management level, driven by the increasing sophistication of cyber threats and the rapid adoption of AI. While AI enhances security, it also introduces new vulnerabilities. As organisations face an evolving threat landscape, they are adopting a more holistic approach to cybersecurity, covering prevention, detection, response, and recovery.
In 2025, cybersecurity leaders will continue to navigate a complex mix of technological advancements, regulatory pressures, and changing business needs. To stay ahead, organisations will prioritise robust security solutions, skilled professionals, and strategic partnerships.
Ecosystm analysts Darian Bird, Sash Mukherjee, and Simona Dimovski present the key cybersecurity trends for 2025.
Click here to download ‘Securing the AI Frontier: Top 5 Cyber Trends for 2025’ as a PDF
The convergence of geopolitical instability, cyber weaponisation, and an interconnected digital economy will make cybersecurity a cornerstone of corporate strategy. State-sponsored cyberattacks targeting critical infrastructure, supply chains, and sensitive data have turned cyber warfare into an operational reality, forcing businesses to prioritise security.
Regulatory pressures are driving this shift, mandating breach reporting, data sovereignty, and significant penalties, while international cybersecurity norms compel companies to align with evolving standards to remain competitive.
The stakes are high. Stakeholders now see cybersecurity as a proxy for trust and resilience, scrutinising both internal measures and ecosystem vulnerabilities.
The future of cybersecurity lies in never trusting, always verifying – especially where AI is involved.
In 2025, the rise of AI-driven systems will make Zero Trust architectures vital for cybersecurity. Unlike traditional networks with implicit trust, AI environments demand stricter scrutiny due to their reliance on sensitive data, autonomous decisions, and interconnected systems. The growing threat of adversarial attacks – data poisoning, model inversion, and algorithmic manipulation – highlights the urgency of continuous verification.
Global forces are driving this shift. Regulatory mandates like the EU’s DORA, the US Cybersecurity Executive Order, and the NIST Zero Trust framework call for robust safeguards for critical systems. These measures align with the growing reliance on AI in high-stakes sectors like Finance, Healthcare, and National Security.
Organisations are caught between excitement and uncertainty regarding AI. While the benefits are immense, businesses struggle with the complexities of governing AI. The EU AI Act looms large, pushing global organisations to brace for stricter regulations, while a rise in shadow IT sees business units bypassing traditional IT to deploy AI independently.
In this environment of regulatory ambiguity and organisational flux, CISOs and CIOs will prioritise data privacy and governance, proactively securing organisations with strong data frameworks and advanced security solutions to stay ahead of emerging regulations.
Recognising that AI will be multi-modal, multi-vendor, and hybrid, organisations will invest in model orchestration and integration platforms to simplify management and ensure smoother compliance.
This shift stems from the need for unified management, cost efficiency, and the recognition that standardisation enhances security posture.
Tech providers are racing to deliver comprehensive network and security platforms.
Recent M&A moves by HPE (Juniper), Palo Alto Networks (QRadar SaaS), Fortinet (Lacework), and LogRhythm (Exabeam) highlight this trend. Rising player Cato Networks is capitalising on mid-market demand for single-provider solutions, with many customers planning to consolidate vendors in their favour. Meanwhile, telecoms are expanding their SASE offerings to support organisations adapting to remote work and growing cloud adoption.
By 2025, the rise of AI-powered cyber threats will demand equally advanced AI-driven defences.
Threat actors are using AI to launch adaptive attacks like deepfake fraud, automated phishing, and adversarial machine learning, operating at a speed and scale beyond traditional defences.
Real-time AI solutions will be essential for detection and response.
Nation-state-backed advanced persistent threat (APT) groups and GenAI misuse are intensifying these challenges, exploiting vulnerabilities in critical infrastructure and supply chains. Mandatory reporting and threat intelligence sharing will strengthen AI defences, enabling real-time adaptation to emerging threats.
In 2024, technology vendors have heavily invested in AI Agents, recognising their potential to drive significant value. These tools leverage well-governed, small datasets to integrate seamlessly with applications like Workday, Salesforce, ServiceNow, and Dayforce, enhancing processes and outcomes.
2025 is poised to be the year of AI Agent adoption. Designed to automate specific tasks within existing workflows, AI Agents will transform customer experiences, streamline operations, and boost efficiency. Unlike traditional AI deployments, they offer a gradual, non-disruptive approach, augmenting human capabilities without overhauling processes. As organisations adopt new software versions with embedded AI capabilities, 2025 will mark a pivotal shift in customer experience delivery.
Ecosystm analysts Audrey William, Melanie Disse, and Tim Sheedy present the top 5 trends shaping customer experience in 2025.
Click here to download ‘AI-Powered Customer Experience: Top 5 Trends for 2025’ as a PDF
The data is in – the real focus of AI over the next few years will be on productivity and cost savings.
Senior management and boards of directors want to achieve more with less – so even when AI is being used to serve customers, it will be focused on reducing back-end and human costs.
There will be exceptions, such as the adoption of AI agents in contact centres. However, AI agents must match or exceed human performance to see broad adoption.
However, the primary focus in contact centres will be on reducing Average Handling Time (AHT), increasing call volume per agent, accelerating agent onboarding, and automating customer follow-ups.
As CX programs mature, 2025 will highlight the need to break down not only data and technology siloes but also organisational and cultural barriers to achieve AI-powered CX and business success.
AI and GenAI have unlocked new sources of customer data, prompting leaders to reorganise and adopt a mindset shift about CX. This involves redefining CX as a collective effort, engaging the entire organisation in the journey.
Technologies and KPIs must be aligned to drive customer AND business needs, not purely driving success in siloed areas.
AI Agents are set to explode in 2025, but even more disruptive developments in AI are on the horizon.
As conversational computing gains traction, fuelled by advances in GenAI and progress toward AGI, “Complex AI Agents” will emerge.
These “AGI Agents” will mimic certain human-like capabilities, though not fully replicating human cognition, earning their “Agent” designation.
The first use cases will likely be in software development, where these agents will act as intelligent platforms capable of transforming a described digital process or service into reality. They may include design, inbuilt testing, quality assurance, and the ability to learn from existing IP (e.g., “create an app with the same capabilities as X”).
The often-overlooked aspect of CX is the “operational side”, where Operations Managers face significant challenges in maintaining a real-time pulse on contact centre activities.
For most organisations, this remains a highly manual and reactive process. Intelligent workflow bots can revolutionise this by acting as gatekeepers, instantly identifying issues and triggering real-time corrective actions. These bots can even halt processes causing customer dissatisfaction, ensuring problems are addressed proactively.
Operational inefficiencies, such as back-office delays, unanswered emails, and slow issue containment, create constant headaches. Integrating bots into contact centre operations will significantly reduce time wasted on these inefficiencies, enhancing both employee and customer experiences.
Employee experience (EX) has traditionally lagged behind CX in focus and technology investment. However, AI-powered technologies are now enabling organisations to apply CX use cases to EX efforts, using advanced data analysis, summaries, and recommendations.
AI and GenAI tools will enhance understanding of employee satisfaction and engagement while predicting churn and retention drivers.
HR teams and leaders will leverage these tools to optimise performance management and improve hiring and retention outcomes.
Additionally, organisations will begin to connect EX with financial performance, identifying key drivers of engagement and linking them to business success. This shift will position EX as a strategic priority, integral to achieving organisational goals.
