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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.

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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.

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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.

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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.

In my previous Ecosystm Insight, I explored the Automation Paradox – how AI shifts human roles from routine tasks to more complex, high-pressure responsibilities. Now, let’s look at its impact on entry-level roles and what it means for those starting their careers.

AI is reshaping the skills mix in enterprises, automating many repetitive, lower-complexity tasks that traditionally serve as stepping stones for new professionals. Roles like Level 1 IT support or paralegal work – once common entry points – are increasingly being automated or significantly reduced.

The question now is: how will the next generation gain the experience needed to advance?

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Click here to download “AI’s Unintended Consequences: Redefining Employee Skill Pathways” as a PDF

Why Are Entry-Level Roles Changing?

• Automation of Routine Tasks. AI-driven tools are taking over routine tasks. AI-driven tools and chatbots now handle common helpdesk issues instantly, eliminating the need for human intervention. Contract review software scans and analyses legal documents, cutting the workload of junior paralegals.
• Demand for Specialised Knowledge. As AI handles grunt work, remaining roles demand higher-level skills – technical, analytical, and interpersonal. For e.g., IT support shifts from password resets to configuring complex systems, interpreting AI diagnostics, and crafting custom solutions.

With routine tasks automated and remaining work more complex, traditional career entry points may shrink – or vanish entirely.

If an organisation no longer has a roster of junior positions, where will young professionals gain the foundational experience and institutional knowledge needed to excel?

The Ripple Effect on Talent & Development

Reduced Traditional Apprenticeships. Entry-level roles have historically provided new hires with an informal apprenticeship – learning basic skills, building relationships, and understanding organisational nuances. Without these roles, new talent may miss out on crucial developmental opportunities.

Potential Skills Gap. By removing the “lower rungs” of the career ladder, we risk ending up with professionals who lack broad foundational knowledge. A fully automated helpdesk, for example, might produce mid-level analysts who understand theory but have never troubleshot a live system under pressure.

Pressure to Upskill Quickly. New recruits may have to jump directly into more complex responsibilities. While this can accelerate learning, it may also create undue stress if the proper structures for training, mentoring, and support are not in place.

Strategies to Create New Skill Pathways

1. Reimagined Entry Pathways for New Employees

• Rotational Programs. One way to fill the void left by disappearing junior roles is through rotational programs. Over the course of a year, new hires cycle through different departments or projects, picking up hands-on experience even if traditional entry-level tasks are automated.
• Apprenticeship-Style Training. Instead of “on-the-job” experience tied to low-level tasks, companies can establish apprenticeship models where junior employees shadow experienced mentors on live projects. This allows them to observe complex work up close and gradually take on real responsibilities.

2. Blended Learning & Simulation

• AI-Driven Training. Ironically, AI can help solve the gap it creates. AI simulations and virtual labs can approximate real-world scenarios, giving novices a taste of troubleshooting or document review tasks.
• Certification & Micro-Credentials. More specialised skill sets may be delivered through structured learning, using platforms that provide bite-sized, verifiable credentials in areas like cybersecurity, analytics, or advanced software configuration.
• Knowledge Sharing Communities. Team chat channels, internal wikis, and regular “lunch and learn” sessions can help new employees gain the cultural and historical context they’d otherwise accumulate in junior roles.

3. Redefining Career Progression

• Competency-Based Pathways. Instead of relying on job titles (e.g. Level 1 Support), organisations can define career progression through skill mastery. Employees progress once they demonstrate competencies – through  projects, assessments, or peer review – rather than simply ticking time-based boxes.
• Continuous Upskilling. Given the rapid evolution of AI, companies should encourage a culture of lifelong learning. Subsidised courses, conference attendance, and online platforms help maintain an agile, future-ready workforce.

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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.

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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.

The Asia Pacific region is rapidly emerging as a global economic powerhouse, with AI playing a key role in driving this growth. The AI market in the region is projected to reach USD 244B by 2025, and organisations must adapt and scale AI effectively to thrive. The question is no longer whether to adopt AI, but how to do so responsibly and effectively for long-term success.

The APAC AI Outlook 2025 highlights how Asia Pacific enterprises are moving beyond experimentation to maximise the impact of their AI investments.

Here are 5 key trends that will impact the AI landscape in 2025.

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Click here to download “The Future of AI-Powered Business: 5 Trends to Watch” as a PDF.

1. Strategic AI Deployment

AI is no longer a buzzword, but Asia Pacific’s transformation engine.  It’s reshaping industries and fuelling growth. Initially, high costs and complex ROI pushed leaders toward quick wins. Now, the game has changed. As AI adoption matures, the focus is shifting from short-term gains to long-term, innovation-driven strategies.

GenAI is is at the heart of this shift, moving beyond the periphery to power core business functions and deliver competitive advantage.  

Organisations are rethinking AI investments, looking beyond pure financials to consider the impact on jobs, governance, and data readiness. The AI journey is about balancing ambition with practicality.

2. Optimising AI: Tailored Open-Source Models

Smaller, open-source, and specialised AI models will gain momentum as organisations seek efficiency, flexibility, and sustainability in their AI strategies.

Unlike LLMs, which require high computational power, smaller, task-specific models offer comparable performance while being more resource-efficient. This makes them ideal for organisations working with proprietary data or limited computational resources.

Beyond cost and performance, these models are more energy-efficient, addressing growing concerns about AI’s environmental impact.

3. Centralised Tools for Responsible Innovation

Navigating the increasingly complex AI landscape demands unified management and governance.  Organisations will prioritise centralised frameworks to tame the chaos of diverse AI solutions, ensuring compliance (think EU AI Act) while boosting transparency and security.

Automated AI lifecycle management tools will streamline oversight, providing real-time tracking of model performance, usage, and issues like drift.

By using flexible developer toolkits and vendor-agnostic strategies, organisations can accelerate innovation while maintaining adaptability, as the technology evolves.

4. Supercharging Workflows With Agentic AI

Organisations will embrace Agentic AI to automate complex workflows and drive business value. Traditional automation tools struggle with real-world dynamism, but AI-powered agents offer a flexible solution. They empower autonomous task execution, intelligent decision-making, and adaptability to changing circumstances.

These agents, often using GenAI, understand complex instructions and learn from experience. They collaborate with humans, boosting efficiency, and adapt to disruptions, unlike rigid traditional automation.

Agentic workflows are key to redefining work, enabling agility and innovation.

5. From Productivity to People

The focus of AI conversations will shift from simply boosting productivity to using AI for human-centric innovation that transforms both employee roles and customer experiences.

For employees, AI will handle routine tasks, enabling them to focus on creativity and innovation. Education and training will be crucial for a smooth transition to AI-powered workflows.

For customers, AI is evolving to offer more empathetic, personalised interactions by understanding individual emotions, motivations, and preferences. Organisations are recognising the need for transparent, explainable AI to build trust, tailor solutions, and deepen engagement.

Hit or miss AI experiments have leaders demanding results.  In this breakneck AI landscape, strategy and realism are your survival tools.  A pragmatic approach?  High-impact, achievable goals. Know your capabilities, prioritise manageable projects, and stay flexible. The AI winners will be those who champion human-AI collaboration, bake in ethics, and never stop researching.