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?
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.
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 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.
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.
