For Paolo Inga, Chief Organization and People Officer at Agoda, technology is a means to help people work, learn and grow. His international HR experience across Asia, India, the Middle East and Europe has shaped his people-first approach to leadership, grounded in continuous learning and an appreciation of diverse work cultures.
As artificial intelligence reshapes the workplace, this people-first approach is becoming increasingly relevant. The challenge for HR leaders is no longer simply to drive AI adoption, but to prepare employees to adapt to changing roles, develop new skills and apply critical thinking as technology takes on more routine tasks.
In this edition of CHRO Perspectives, Inga discusses how Agoda is encouraging employees to experiment with AI, using workforce data to anticipate reskilling needs and creating an environment where people can learn without fear of failure. He also explores how AI could accelerate career progression and why curiosity, critical thinking and the willingness to unlearn and relearn will remain essential to individual and organisational growth.
For Inga, building an AI-ready workforce means combining technological capabilities with the human skills that enable people to navigate change and make better decisions.
Read the edited excerpts:
AI adoption is often measured through usage rates, but that tells only part of the story. For Agoda’s large workforce, how do you assess whether employees are genuinely becoming AI-ready?
When it comes to a workforce of around 8,000 people at Agoda, our leadership team takes an inside-out approach to AI adoption. We give employees the opportunity to learn and experiment with AI first to improve their personal productivity.
Today, more than 90% of our employees use Gen AI, and we have developed over 120 use cases across software development, content generation, product, finance and HR. These applications help employees work more efficiently and improve productivity.
In late October, my team brought together more than 300 people from the People function in Bangkok for a hackathon focused on productivity. Tasks such as creating presentations, formatting spreadsheets and aligning boxes, which once took up considerable time, can now be automated with a simple prompt.
The objective is to free up employees to focus on more meaningful work and, importantly, use AI to improve decision-making rather than simply save time on tasks.
Take for example, in talent acquisition, AI has two key roles: supporting the recruitment process and helping organisations assess candidates’ suitability for an AI-driven workplace.
Curiosity and critical thinking will become even more important as AI generates increasing volumes of output. People need critical thinking skills to assess whether that output is accurate and useful.
On the recruitment side, AI can automate tasks such as matching CVs with job descriptions and scheduling interviews based on candidates’ availability. These tools simplify routine processes, allowing recruiters to focus on higher-value activities.
AI is a tool, not an end in itself. Curiosity and critical thinking will continue to differentiate individual performance. AI can support us, but we remain responsible for our outcomes, careers and performance. It is there to assist people, not replace their responsibility.
How should organisations redesign early-career learning so that AI removes unnecessary work without removing the experiences through which people actually build expertise?
Anxiety about AI is a natural response to the changes taking place. However, alongside traditional technical skills in areas such as marketing, finance and programming, people will need to learn how to use AI effectively and critically assess its output.
Schools and universities can play an important role by giving students more opportunities to experiment with AI and develop critical thinking. When recruiting junior employees, I see curiosity and critical thinking as two essential qualities for future success.
When I started working 25 years ago, preparing presentations and gathering information from senior colleagues was an important way to learn. Today, AI can generate that content almost instantly. The focus therefore shifts from creating the initial output to evaluating it, improving it and tailoring the communication to the needs of its intended audience.
At Agoda, our summer internship programme brings in more than 80 people each year for 10 weeks, giving them an opportunity to learn and gain practical work experience. The more they experiment and gain workplace experience, the better they understand the professional context needed to guide AI and generate the outputs they want.
How can organisations build curiosity, critical thinking and AI judgement at scale, and how do you know when employees have genuinely developed those capabilities?
A core measure of learning is behavioural change. When people learn something new, they begin to behave differently, and that remains a key indicator regardless of AI or future technological advances.
AI can support this by eliminating routine, time-consuming tasks. For example, I recently received a PDF from my child’s school listing 15 parent-teacher meetings. Instead of spending an hour manually adding them to my Outlook calendar, I asked Claude AI to update my agenda, and it did so in seconds.
In simple words, that is the value of AI: removing repetitive work to free up time for more meaningful activities, whether that means coaching your team, engaging with colleagues or spending time with your family.
What role do managers play in creating the conditions for employees to experiment, learn and fail safely with AI, and how can organisations build that adaptability consistently across a large workforce rather than in isolated pockets?
AI may change how people work and learn, but the fundamentals of learning remain the same: psychological safety, permission to experiment, feedback and practice. Organisations need to create an environment where people feel encouraged to learn without being afraid of failure. This is the responsibility of managers, HR and the leadership team.
At Agoda, employees receive AI tokens to experiment with the technology. When a team member used up her allocation while working on a meaningful project, I congratulated her. It showed she was actively using AI to improve her work.
By contrast, I once received an eight-page document outlining objectives that had clearly not been reviewed or proofread. Generating large volumes of AI content without applying critical thinking is not effective AI adoption.
I tell my team not to be afraid to say they have used AI to prepare a presentation. Using AI does not make you lazy; it makes you more efficient, provided you critically assess and refine the output. Managers must reinforce this distinction, encouraging experimentation and efficiency while giving feedback when AI is used to generate unfiltered content rather than meaningful work.
How can HR use workforce data, business signals and emerging AI capabilities to anticipate where skills will become obsolete or newly important, and translate that insight into learning pathways before the gap becomes a business problem?
Using workforce data might sound like HR or companies are monitoring what employees do every day, checking whether they send two more or two fewer emails. But that is not what we are doing.
In reality, workforce data can help organisations prepare for future reskilling needs. Reskilling often involves technical skills, such as moving from recruitment to rewards within HR, from accounting to business control in finance, or from coding to database administration.
Companies can teach people these technical skills.
In my opinion, the more important factor is whether people are willing to learn new things and unlearn what they already know. How people respond to new learning is what really matters, and that comes down to curiosity and critical thinking. These qualities are essential both for people looking to join an organisation and for those already within it who want to progress in their careers.
As AI changes both the nature of work and how people build expertise, what does this mean for the future employee experience at Agoda, from career progression and internal mobility to learning and workforce planning?
The goal is to prepare our workforce for what lies ahead while ensuring the business benefits from better productivity, stronger capabilities and sustainable growth.
AI could accelerate aspects of early-career development as tasks, particularly in the early stages, as tasks that once took considerable time to master become easier to learn and perform. The overall career ladder may not change dramatically, but people could progress through it more quickly.
At Agoda, we support this through development programmes such as Agoda GROW, which helps employees at different career stages understand what they need to learn to progress. Across our technology function, which includes more than 2,000 engineers, data scientists and other technology professionals, we provide structured learning paths for different career stages.
These paths allow employees to deepen their expertise as individual contributors or develop the skills needed to manage teams.
The aim is to help people identify the right learning opportunities to advance their careers and support their broader personal development.