AI is transforming customer experience, but is it delivering real value to Sales, Marketing, and Customer teams?
To drive impact and align AI with organisational strategy, Customer Success leaders must:
- Break down data silos to unify customer insights
- Define what success and innovation mean for their teams
- Ensure adoption with intuitive AI and effective change management
- Seamlessly integrate AI into existing systems for maximum value
Customer Success leaders, how are you driving proactive engagement with AI?
Customer Success leaders are keenly aware of AI’s burgeoning potential, and our latest research confirms it. AI is no longer a futuristic concept; it’s a present-day reality, already shaping content strategies for 55% of organisations and poised to expand its influence across a multitude of use cases.
Over the past two years, Ecosystm’s research – including surveys and deep dives with business and tech leaders – has consistently pointed to AI as the dominant theme.
Here are some insights for Customer Success Leaders from our research.
Click here to download “AI Stakeholders: The Customer Success Perspective” as a PDF.
AI in Action: Real-World Applications
The data speaks for itself. We’re seeing a significant uptake of AI in automating sales processes (69%), location-based marketing (63%), and delivering personalised product/service recommendations (61%). But beyond the numbers, what does this look like in practice?
In Marketing, AI tailors campaigns in real time based on customer behaviour, ensuring content and offers resonate. For e.g. in the Travel industry, AI analyses customer preferences to create customised itineraries, boosting satisfaction and repeat bookings. In Sales, AI-driven analysis of buying patterns helps teams stay ahead of trends, equipping them with the right products to meet demand. In Customer Experience, AI-powered feedback analysis identifies pain points before they escalate, leading to proactive problem-solving. We have already seen organisations using conversational AI to enable 24/7 customer engagement, instantly resolving issues while reducing team workload and enhancing CX.
Challenges and Opportunities: Navigating the AI Landscape
However, the path to AI adoption isn’t without its hurdles. Customer Success leaders face significant challenges, including the lack of an organisation-wide AI strategy, data complexity and access issues, and the cost of implementation.
Despite these challenges, the focus on AI to enhance Customer Success is evident, with nearly 40% of AI initiatives geared towards this goal. This requires a more active role for these leaders in shaping AI strategies and roadmaps.
Our research reveals that there lies a critical gap: Customer Success leaders have limited involvement in AI initiatives. Only 19% are involved in identifying and prioritising use cases, and a mere 10% have input into data ownership and governance. This lack of participation is a missed opportunity.
The 2025 Vision: AI-Driven Customer Success
Looking ahead, Customer Success leaders expect AI to deliver significant benefits, including improved customer experience (56%), increased productivity (50%), and enhanced innovation (44%). These expectations underscore AI’s pivotal role in shaping the future of customer success.
To fully harness AI’s potential and advancements like Agentic AI, leaders must take a more active role. This means driving a clear AI strategy, tackling data challenges, and working closely with IT and data science teams to ensure AI solutions address real customer pain points and business gaps.
AI is Transforming Operations – Are You Ready to Scale?
The future of operations is AI-driven, ensuring efficiency, agility, and smarter decision-making. But scaling AI isn’t just about technology – it’s about strategy.
To stay ahead, Leaders must focus on:
- Balancing cost savings with business priorities
- Aligning stakeholders for cross-functional success
- Looking beyond surface-level ROI
- Breaking down data silos for smarter decisions
Operations leaders are on the front lines of the AI revolution. They see the transformative potential of AI and are actively driving its adoption to streamline processes, boost efficiency, and unlock new levels of performance. The value is clear: AI is no longer a futuristic concept, but a present-day necessity.
Over the past two years, Ecosystm’s research – including surveys and deep dives with business and tech leaders has confirmed this: AI is the dominant theme.
Here are some insights for Operations Leaders from our research.
Click here to download “AI Stakeholders: The Operations Perspective” as a PDF
From Streamlined Workflows to Smarter Decisions
AI is already making a tangible difference in operations. A significant 60% of operations leaders are currently leveraging AI for intelligent document processing, freeing up valuable time and resources. But this is just the beginning. The vision extends far beyond, with plans to expand AI’s reach into crucial areas like workflow analysis, fraud detection, and streamlining risk and compliance processes. Imagine AI optimising transportation routes in real-time, predicting equipment maintenance needs before they arise, or automating complex scheduling tasks. This is the operational reality AI is creating.
Real-World Impact, Real-World Examples
The impact of AI is not just theoretical. Operations leaders are witnessing firsthand how AI is driving tangible improvements. “With AI-powered vision and sensors, we’ve boosted efficiency, accuracy, and safety in our manufacturing processes,” shares one leader. Others highlight the security benefits: “From fraud detection to claims processing, AI is safeguarding our transactions and improving trust in our services.” Even complex logistical challenges are being conquered: “Our AI-driven logistics solution has cut costs, saved time, and turned complex operations into seamless processes.” These real-world examples showcase the power of AI to deliver concrete results across diverse operational functions.
Operations Takes a Seat at the AI Strategy Table (But Faces Challenges)
With 54% of organisations prioritising cost savings from AI, operations leaders are rightfully taking a seat at the AI strategy table, shaping use cases and driving adoption. A remarkable 56% of operations leaders are actively involved in defining high-value AI applications. However, a disconnect exists. Despite their influence on AI strategy, only a small fraction (7%) of operations leaders have direct data governance responsibilities. This lack of control over the very fuel that powers AI – data – creates a significant hurdle.
Further challenges include data access across siloed systems, limiting the ability to gain a holistic view, difficulty in identifying and prioritising the most impactful AI use cases, and persistent skills shortages. These barriers, while significant, are not deterring operations leaders.
The Future is AI-Driven
Despite these challenges, operations leaders are doubling down on AI. A striking 7 out of 10 plan to prioritise AI investments in 2025, driven by the pursuit of greater cost savings. And the biggest data effort on the horizon? Identifying and prioritising better use cases for AI. This focus on practical applications demonstrates a clear understanding: the future of operations is inextricably linked to the power of AI. By addressing the challenges they face and focusing on strategic implementation, operations leaders are poised to unlock the full potential of AI and transform their organisations.
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.
HR: Leading the Charge (or Should Be)
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.
The Roadblocks and the Opportunity
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.
2025 and Beyond: A Call to Action for HR
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 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
1. The AI Buildout Will Accelerate; China Will Emerge as a Winner
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.
2. AI-Optimised Cloud Platforms Will Dominate Infrastructure Investments
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.
3. PaaS Architecture, Not Data Cleanup, Will Define AI Success
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.
4. Small Language Models Will Push AI to the Edge
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.
5. The Impact of AI PCs Will Remain Limited
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
1. Cybersecurity Will Be a Critical Differentiator in Corporate Strategy
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.
2. Zero Trust Architectures Will Anchor AI-Driven Environments
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.
3. Organisations Will Proactively Focus on AI Governance & Data Privacy
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.
4. Network & Security Stacks Will Streamline Through Converged Platforms
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.
5. AI Will Be Widely Used to Combat AI-Powered Threats in Real-time
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.
