AI is set to transform the workplace – enhancing productivity and reshaping roles. HR isn’t just a bystander; it’s key to ensuring employees benefit while managing AI’s uncertainties. It’s time HR had a bigger seat at the table.
Here’s where AI adoption in HR stands today.
Barely weeks into 2025, the Consumer Electronics Show (CES) announced a wave of AI-powered innovations – from Nvidia’s latest RTX 50-series graphics chip with AI-powered rendering to Halliday’s futuristic augmented reality smart glasses. AI has firmly emerged from the “fringe” technology to become the foundation of industry transformation. According to MIT, 95% of businesses are already using AI in some capacity, and more than half are aiming for full-scale integration by 2026.
But as AI adoption increases, the real challenge isn’t just about developing smarter models – it’s about whether the underlying infrastructure can keep up.
The AI-Driven Cloud: Strategic Growth
Cloud providers are at the heart of the AI revolution, but in 2025, it is not just about raw computing power anymore. It’s about smarter, more strategic expansion.
Microsoft is expanding its AI infrastructure footprint beyond traditional tech hubs, investing USD 300M in South Africa to build AI-ready data centres in an emerging market. Similarly, AWS is doubling down on another emerging market with an investment of USD 8B to develop next-generation cloud infrastructure in Maharashtra, India.
This focus on AI is not limited to the top hyperscalers; Oracle, for instance, is seeing rapid cloud growth, with 15% revenue growth expected in 2026 and 20% in 2027. This growth is driven by deep AI integration and investments in semiconductor technology. Oracle is also a key player in OpenAI and SoftBank’s Stargate AI initiative, showcasing its commitment to AI innovation.
Emerging players and disruptors are also making their mark. For instance, CoreWeave, a former crypto mining company, has pivoted to AI cloud services. They recently secured a USD 12B contract with OpenAI to provide computing power for training and running AI models over the next five years.
The signs are clear – the demand for AI is reshaping the cloud industry faster than anyone expected.
Strategic Investments In Data Centres Powering Growth
Enterprises are increasingly investing in AI-optimised data centres, driven by the need to reduce reliance on traditional data centres, lower latency, achieve cost savings, and gain better control over data.
Reliance Industries is set to build the world’s largest AI data centre in Jamnagar, India, with a 3-gigawatt capacity. This ambitious project aims to accelerate AI adoption by reducing inferencing costs and enabling large-scale AI workloads through its ‘Jio Brain’ platform. Similarly, in the US, a group of banks has committed USD 2B to fund a 100-acre AI data centre in Utah, underscoring the financial sector’s confidence in AI’s future and the increasing demand for high-performance computing infrastructure.
These large-scale investments are part of a broader trend – AI is becoming a key driver of economic and industrial transformation. As AI adoption accelerates, the need for advanced data centres capable of handling vast computational workloads is growing. The enterprise sector’s support for AI infrastructure highlights AI’s pivotal role in shaping digital economies and driving long-term growth.
AI Hardware Reimagined: Beyond the GPU
While cloud providers are racing to scale up, semiconductor companies are rethinking AI hardware from the ground up – and they are adapting fast.
Nvidia is no longer just focused on cloud GPUs – it is now working directly with enterprises to deploy H200-powered private AI clusters. AMD’s MI300X chips are being integrated into financial services for high-frequency trading and fraud detection, offering a more energy-efficient alternative to traditional AI hardware.
Another major trend is chiplet architectures, where AI models run across multiple smaller chips instead of a single, power-hungry processor. Meta’s latest AI accelerator and Google’s custom TPU designs are early adopters of this modular approach, making AI computing more scalable and cost-effective.
The AI hardware race is no longer just about bigger chips – it’s about smarter, more efficient designs that optimise performance while keeping energy costs in check.
Collaborative AI: Sharing The Infrastructure Burden
As AI infrastructure investments increase, so do costs. Training and deploying LLMs requires billions in high-performance chips, cloud storage, and data centres. To manage these costs, companies are increasingly teaming up to share infrastructure and expertise.
SoftBank and OpenAI formed a joint venture in Japan to accelerate AI adoption across enterprises. Meanwhile, Telstra and Accenture are partnering on a global scale to pool their AI infrastructure resources, ensuring businesses have access to scalable AI solutions.
In financial services, Palantir and TWG Global have joined forces to deploy AI models for risk assessment, fraud detection, and customer automation – leveraging shared infrastructure to reduce costs and increase efficiency.
And with tech giants spending over USD 315 billion on AI infrastructure this year alone – plus OpenAI’s USD 500 billion commitment – the need for collaboration will only grow.
These joint ventures are more than just cost-sharing arrangements; they are strategic plays to accelerate AI adoption while managing the massive infrastructure bill.
The AI Infrastructure Power Shift
The AI infrastructure race in 2025 isn’t just about bigger investments or faster chips – it’s about reshaping the tech landscape. Leaders aren’t just building AI infrastructure; they’re determining who controls AI’s future. Cloud providers are shaping where and how AI is deployed, while semiconductor companies focus on energy efficiency and sustainability. Joint ventures highlight that AI is too big for any single player.
But rapid growth comes with challenges: Will smaller enterprises be locked out? Can regulations keep pace? As investments concentrate among a few, how will competition and innovation evolve?
One thing is clear: Those who control AI infrastructure today will shape tomorrow’s AI-driven economy.
2025 is already shaping up to be a battleground for cybersecurity. With global cybercrime costs projected to reach USD 10.5T, by year’s end, the stakes have never been higher. Cybercriminals are getting smarter, using AI-driven tactics and large-scale exploits to target critical sectors. From government breaches to hospital data leaks and a surge in phishing scams, recent attacks highlight the growing financial and operational toll of cyber threats.
As cyber threats intensify, the demand for stronger defences, top-tier cybersecurity talent, and global collaboration has never been more urgent.
Here’s a look at the recent cyber developments that are shaping 2025.
Click here to download “Cyber Lessons from the Frontlines” as a PDF.
Major Security Breaches: A Costly Wake-Up Call
Cyberattacks are becoming more targeted, disruptive, and costly – impacting governments and organisations worldwide.
In Singapore, mobile wallet fraud is surging, with phishing tactics causing USD 8.9K in losses – 80% linked to Apple Pay. In the UK, security flaws in government IT systems have exposed sensitive data and infrastructure. South Africa’s government-run weather service (SAWS) was also forced offline, disrupting a critical resource for airlines, farmers, and emergency responders. Across the Atlantic, a data breach at a Georgia hospital compromised 120,000 patient records, while BayMark Health Services, the largest addiction treatment provider in the US, alerted patients to a similar breach.
What steps are governments, tech providers, and enterprises taking to protect themselves, critical infrastructure, and individuals?
Protecting Critical Infrastructure: The Digital Backbone
As global connectivity expands, securing critical infrastructure is paramount to sustaining growth, stability, and public trust.
Undersea cables, which carry much of the world’s internet traffic, are a major focus. While tech giants like Amazon, Meta, and Google are expanding these networks to boost global data speed and reliability, the need for protection is just as urgent – prompting the EU to invest nearly a billion dollars in securing them against emerging threats.
Governments and tech providers alike are stepping up. The European Commission has introduced a cybersecurity blueprint to strengthen crisis coordination, rapid response, and information sharing. Meanwhile, Microsoft is investing USD 700M in Poland’s cloud and AI infrastructure, working with the Polish National Defense to enhance cybersecurity through AI-driven strategies.
Quantifying Cyber Risk: Standardised Threat Assessment
As cyber threats grow more sophisticated, so must our ability to detect, measure, and respond to them.
A major shift in cybersecurity is underway – one that prioritises standardised threat assessment and coordinated defense.
The UK is leading the charge with a new cyber monitoring centre that will introduce a “Richter Scale” for cyberattacks, ranking threats much like earthquake magnitudes. Emerging countries are also joining in; Vietnam is strengthening its cyber defences with a new intelligence-sharing platform designed to improve coordination between the government and private sector.
By quantifying cyber risks and enhancing intelligence-sharing, these efforts are shaping global cybersecurity norms, improving response times, and building a more resilient digital ecosystem.
Beyond Defence: Proactive Measures to Combat AI-Driven Cybercrime
Cyber threats evolve faster than defences can keep up – a single click on a malicious email can lead to a breach in just 72 minutes.
With AI making cyberattacks more sophisticated, governments are taking an active role in cyber law enforcement.
Indonesia set up a cyber patrol to monitor and regulate harmful online content while also working to create a safer digital space for children. Thailand, Cambodia, and Laos are cooperating to curb cross-border scams through intelligence sharing and joint enforcement efforts.
Building Trust Online: Digital Identity Solutions
Governments are moving beyond enforcement to strengthen security with digital identity frameworks.
The EU is leading this shift with large-scale pilots for digital identity wallets, designed to offer citizens a secure, seamless way to verify credentials for services, transactions, and age-restricted content. By 2026, each EU member state will issue its own wallet, built on unified technical standards to ensure cross-border interoperability and stronger cybersecurity.
Digital identity wallets mark a major shift in data security, giving citizens greater control over their information while strengthening online trust. By securing identity verification, governments are reducing fraud and identity theft, creating a safer digital landscape.
Closing the Gap: Global Cyber Education Push
Cybersecurity education is no longer just for IT teams – it’s essential at every level, from executives to employees, to build long-term resilience.
Again, governments and tech giants alike are stepping up to bridge the skills gap and enhance cyber awareness.
Singapore is leading by example with a cyber-resilience training program for board directors, ensuring corporate leaders understand cyber risk management. AWS is investing USD 6.35M to support cybersecurity education in the UK, and Microsoft is expanding its global training efforts. The company has partnered with Kazakhstan to strengthen public sector cybersecurity and has committed to training one million South Africans in AI and cybersecurity by 2026.
The Path Forward: A Collective Responsibility
The cybersecurity landscape underscores a crucial truth: resilience can’t be built in isolation. Governments, businesses, and individuals must move past reactive measures and adopt a collective, intelligence-driven approach. As threats grow more sophisticated, so must our commitment to collaboration, vigilance, and proactive defence.
In an increasingly interconnected world, securing the digital landscape is not just necessary – it’s a shared responsibility.
Tech leaders, to create business impact with AI, stop viewing it as just a tool – it demands the right strategy and foundation.
Maximising AI requires focus on:
• Identifying capability gaps & setting realistic goals
• Building skilled AI teams
• Enabling data ownership for business units
• Embedding AI governance early
• Forming strategic partnerships & managing AI holistically
Discover how AI can be the cornerstone of your transformation journey.
AI has rapidly transitioned from a theoretical concept to a strategic imperative, reshaping core business functions and fundamentally altering the operational landscape of technology teams. By empowering teams with increased autonomy and data-driven capabilities, organisations are positioned to realise substantial value and achieve a decisive competitive advantage.
The most profound impact of AI can be observed within tech teams. AI-driven automation of routine tasks and streamlined operations are enabling technology professionals to refocus their efforts on strategic initiatives. This shift transforms the technology function from a reactive system maintenance role to a proactive developer of intelligent infrastructure and future-oriented systems.
Ecosystm research reveals key findings that Tech Leaders need to know.
Click here to download “AI Stakeholders: The Tech Leader’s Perspective” as a PDF.
Strategic AI Deployment
Ecosystm research reveals a clear trend: technology leaders are strategically investing in the immense potential of AI. While 61% currently leverage AI for IT support and helpdesk automation, there is a clear aspiration for broader deployment across infrastructure, development, and security. 80% are prioritising cloud resource allocation and optimisation, followed by 76% focusing on network optimisation and performance monitoring, along with significant interest in software development and testing, and cyber threat detection.
One Infrastructure Leader shared that the organisation uses AI to dynamically scale infrastructure while automating maintenance to prevent outages. This approach has led to unprecedented efficiency and freed up their teams for more strategic work. The leader emphasised that AI is helping to tackle complex infrastructure challenges and is key to achieving operational excellence.
A Cyber Leader discussed the role of AI in enhancing their defense capabilities. While not a “silver bullet,” it is a powerful tool in the fight against cyber threats. AI significantly enhances threat intelligence and fraud analysis, complementing, rather than replacing, security team efforts. This integration has helped streamline security operations and improve the ability to respond to emerging risks.
AI is also making waves in software development. A Data Science Leader explained how AI quality control tools have reduced bug counts by 30%, enabling faster release cycles and a 10% improvement in internal customer satisfaction.
Collaborative AI Implementation: A Cross-Functional Approach
The successful implementation of AI requires a collaborative, cross-functional approach. The responsibility for identifying viable use cases, developing and maintaining systems, and ensuring robust data governance is distributed among various technology leadership roles. CIOs, in collaboration with business stakeholders, define strategic use cases, considering infrastructure requirements. Data Science Leaders bridge the gap between AI’s technical capabilities and practical business applications. CISOs safeguard data, while CIOs manage the systems that store and organise it.
Navigating Challenges, Prioritising Strategic AI Initiatives
Despite the acknowledged potential of AI, technology leaders must address several critical challenges, including use case prioritisation, skill gaps, and the development of comprehensive AI strategies. Nevertheless, the strategic importance of AI will continue to drive its prioritisation in 2025. Key anticipated outcomes include increased technology team productivity (56%) and technology cost optimisation (53%).
AI is no longer a supplementary tool but a core strategic asset. By strategically integrating AI, technology teams are transitioning from operational support to strategic innovation, building the intelligent systems that will define the future of business.
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.