Lenovo’s Fan Ho on AI adoption and the changing world of work

The conversation around artificial intelligence over the past two years has consistently revolved around a few defining themes: governance, performance, people, and trust. Yet, despite rapid adoption and growing investment, there is still no single blueprint for organisations trying to scale AI successfully. 

In this conversation, Fan Ho, Vice President and General Manager, Asia Pacific, Solutions and Services Group at Lenovo, shares her perspective on how businesses can navigate this evolving landscape, balancing innovation with accountability, building trust into AI systems, and preparing workplaces for a future shaped by human and AI collaboration. 

From hybrid AI infrastructure and workforce transformation to AI literacy and governance, Ho explains why the next phase of AI adoption will depend as much on people and processes as it does on technology.

Read the edited excerpts from the interview.

Now in its fourth year, Lenovo’s CIO Playbook 2026 – AP Edition draws on insights from CXOs across regions, and the rise of generative and agentic AI has brought a few priorities sharply into focus.

In the 2025 edition, governance emerged as the biggest challenge in realising AI value, spanning data access, accountability, risk management, and ultimately the human side of AI adoption. By 2026, the focus shifts even further toward talent, as organisations recognize that effective governance depends on having the right skills in place.

Importantly, “AI talent” is no longer limited to technical expertise such as prompting or engineering. Organisations also need people who understand business processes and can measure ROI.

A key finding from this year’s research is that 96% of organisations plan to increase AI investments, yet governance and talent continue to be the biggest barriers. This reinforces three critical pillars for AI success: people, process, and platform.

Organisations need skilled talent, strong process understanding, and secure technology platforms that enable governance, traceability, explainability, and observability. Most CXOs understand these priorities, but the real challenge lies in execution, as there is no one-size-fits-all approach. Each organisation’s path depends on its business model, market dynamics, and talent ecosystem.

To help organisations move beyond technology adoption alone, Lenovo introduced its Hybrid AI Advantage framework, combining AI factory capabilities, validated AI use cases with measurable ROI, and end-to-end AI services covering advisory, implementation, execution, and change management.

The broader goal is to help organisations tackle the governance and talent concerns now dominating boardroom conversations and translate AI investments into measurable business value.

AI’s capabilities have evolved rapidly, from being the “brain” that generates insights to systems that can increasingly execute tasks through tools and automation. But organisations still operate within essential guardrails around accountability, explainability, governance, and validation.

Because of that, decision-making continues to rest largely with humans. Accountability and responsibility must remain with the right experts, while AI serves as a support system rather than a complete replacement. Even as AI becomes more capable, there needs to be a clear separation between recommendations, advisory functions, and final decisions.

This challenge has become more pronounced with the shift from traditional rule-based AI to generative AI. Earlier AI systems were designed around predefined workflows with built-in validation checks. Generative AI, however, creates new outputs by synthesizing structured and unstructured data, raising concerns around fabricated or partially inaccurate content. That is why mechanisms such as watermarking and traceability have become increasingly important.

The conversation has now evolved further with agentic AI, where AI systems not only generate insights but can also take actions. As this develops, the industry is working to bring back stronger guardrails similar to those used in earlier rule-based systems. This includes metadata, trace markers, human validation layers, certifications, and verification mechanisms that distinguish trusted outputs from purely AI-generated ones.

While generative and agentic AI are driving major gains in automation and efficiency, the broader focus across the industry remains clear: ensuring traceability, accountability, and explainability as AI becomes more deeply embedded into business operations.

One-off workshops are not enough to build meaningful AI literacy. There are really two sides to AI capability building: creating AI systems and effectively using them.

On the creation side, organisations need expertise across tools, technologies, prompting, model design, governance, and management. The complexity can quickly become overwhelming, which is why the industry is increasingly focused on simplification. That’s where platforms like Lenovo xIQ come in.

Today, enterprises have access to multiple large language models, including sovereign AI initiatives, but adoption requires significant adaptation. Organisations need to tailor models to specific use cases, combine public and private data securely, and ensure governance and accountability throughout the process.

Lenovo xIQ introduces a middle layer between enterprises and AI models, allowing organisations to test different models, securely blend private and public data, and simplify agent creation. Instead of extensive coding, users can build agents through templates and drag-and-drop interfaces, making AI development more accessible to process experts who may not have deep technical expertise.

This creates an environment for continuous, role-specific capability building rather than isolated training sessions. Employees can create, manage, and monitor AI agents with far less complexity and training overhead.

On the user side, enablement requires structured and ongoing support. This includes helping employees understand tools like Copilot, improve prompting skills, and integrate AI into daily workflows. But the larger impact comes through organisational change management.

AI and agentic systems are fundamentally reshaping how work flows across businesses. Processes that once depended on multiple departmental handoffs can now be streamlined through AI-driven integration and automation, enabling faster decision-making and more direct customer engagement.

The real value of AI lies not just in augmenting tasks, but in driving broader business transformation. And as organisations move toward that transformation, trust becomes the critical foundation.

Trust and transparency in AI cannot remain abstract concepts owned only by governance boards or security leaders. They need to be embedded across the entire AI value chain.

A key principle Lenovo emphasizes is building governance and transparency into AI systems from the design stage itself. 

Explainability and auditability are equally important. If the same inputs are used, organisations should be able to reproduce similar outputs consistently. That repeatability is what enables AI workflows to be trusted, audited, and scaled confidently.

Lenovo approaches this challenge through both people and platforms. On the people side, its AI services teams help organisations implement governance frameworks by applying lessons from Lenovo’s own internal AI practices and operational experience.

On the platform side, the XIQ agent platform is designed with three core trust capabilities: governance, explainability, and continuous improvement. 

The broader message is that trust and transparency are not standalone features. They emerge when people, processes, and platforms work together to create AI systems that are secure, explainable, and accountable at scale.

Governance is a critical element not only for scaling AI successfully, but for the adoption of any disruptive technology. Historically, before new technologies emerge, the associated risks often remain invisible because the capabilities themselves do not yet exist. As innovation advances, governance naturally becomes more important.

AI is following a similar path. With increasingly open and autonomous systems, concerns around access to public information, automated actions, and the possibility of AI overriding instructions have become more prominent. These anxieties are part of the broader evolution of disruptive technologies.

What is encouraging, however, is that as these challenges surface, the industry is placing far greater emphasis on governance, safeguards, and accountability. Organisations are recognizing that innovation and responsible oversight must evolve together.

In this context, Lenovo is working with NVIDIA on initiatives such as NeMo Core, which focuses on creating more governed AI environments with stronger guardrails, checks, and controls. The goal is to ensure AI systems operate within defined boundaries and remain aligned with intended instructions.

The broader priority is to make AI more responsible, accessible, and governed as adoption accelerates. While not every organisation may yet place governance at the center of its AI strategy, it is becoming increasingly clear that strong oversight and safeguards will be essential to ensuring AI develops in a safe and controlled way for society at large.

One key belief is that – AI is not about disrupting or eliminating jobs for the sake of it. Decision-making and accountability still fundamentally rest with people, based on their expertise, experience, and responsibilities. That core dynamic is unlikely to change.

What is more likely is that, as AI matures, people who know how to work effectively with AI will gain access to greater opportunities than those who do not. The shift is less about AI replacing humans and more about how humans adapt and collaborate with AI.

While certain tasks will become automated, entirely new roles, industries, and business models are also expected to emerge. A decade ago, for example, building a career as a full-time content creator or YouTuber was not widely seen as viable. Today, it represents a significant and sustainable profession for many people.

The same kind of evolution is likely to happen with AI. Rather than creating a future where jobs disappear entirely, AI is expected to reshape how work is done, leading to new skills, new job categories, and new opportunities across industries.

The biggest differentiator in AI adoption is leadership. If leaders believe their business model will remain unaffected and don’t invest time in understanding AI, the organisation may see little change in the short term, but over time it risks becoming marginalized as competitors move faster.

AI creates major opportunities for efficiency and transformation, but it also requires leaders to rethink how their industry operates, the value they deliver to customers, and how they differentiate themselves in the market. The core business fundamentals remain the same, but AI can turn existing disadvantages into competitive advantages when leaders actively engage with it.

The second major differentiator is culture. The gap between laggards and forward-looking organisations often comes down to whether employees are encouraged to learn, experiment with, and embrace AI in their day-to-day work. Organisations that create excitement around AI adoption are more likely to unlock stronger returns from their investments.

Internally, at Lenovo, initiatives such as hackathons and Lenovo Plus are designed to encourage experimentation, learning, and broader AI adoption across teams. These efforts help build a culture where AI acts as a multiplier for innovation and productivity.

At top level, leadership and culture remain more critical to successful AI adoption than technology alone.

There are really two sides to how organisations approach AI investments today.

Many companies still deploy technology in isolated blocks, upgrading infrastructure such as connectivity or Wi-Fi without first clearly defining the business objective. This can lead to overinvestment in “gold-plated” infrastructure that may not actually drive meaningful growth or scale effectively.

At the other extreme, some organisations move heavily toward public cloud and pay-per-use models, assuming that is the simplest path to scale. As a result, businesses often end up choosing between large upfront investments or complete dependence on cloud platforms.

Lenovo’s approach is to find the right balance through a hybrid AI strategy. The starting point is always clarity around the business problem being solved. AI transformation needs to be broken down into specific priorities – whether in sales, operations, front office, or back office functions – so organisations can identify where to start and how to scale gradually.

This is where Lenovo’s AI library and AI services come into play. The AI library helps validate use cases based on proven outcomes, while AI services help organisations define the minimum viable setup, test solutions, and scale them over time. 

Through the TruScale model, customers can also avoid large upfront investments and instead scale consumption as adoption grows.

Insights from the Lenovo CIO Playbook 2026 further reinforce this direction, with more than 80% of CIOs believing AI will ultimately run on private or hybrid infrastructure. 

Key concerns include vendor lock-in in public cloud environments and the unpredictability of AI consumption costs, especially with token-based pricing models.

At the same time, returning entirely to heavy on-premise infrastructure is not seen as practical either. That is why the focus is increasingly shifting toward hybrid AI, combining scalable infrastructure, platforms like XIQ for simplified adoption, validated use cases, and services that guide implementation and governance.

Ultimately, successful AI adoption is not only about technology. It is about finding the right partner, one that brings the expertise, experience, and capability to help organisations navigate complexity and realise measurable value from AI.

The future workplace will increasingly be shaped by collaboration between humans and AI. 

First, it will no longer be limited to human-to-human interactions, but will evolve into a mix of human-to-AI, AI-to-AI, and AI-to-human collaboration. 

Second, at the center of this evolution will be trust, transparency, collaboration, and continuous learning.

Alongside this visible shift, a quieter but equally important transformation is happening in the background through hybrid architecture. Intelligent systems and machines will operate seamlessly behind the scenes to improve productivity and efficiency, making infrastructure, data governance, and storage strategy far more critical than before.

This trend is already reflected in the Lenovo CIO Playbook 2026, where more than 80% of CIOs believe AI will ultimately run on private or hybrid infrastructure. That shift is expected to accelerate rapidly, with hybrid AI environments likely to become the dominant model in the near future.