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June 3, 2026
Originally published in ACTIO E-Magazine Edition 29 (June 2026). Law as of June 2026.
Expert: Edwin Djakaria, S.E., M.Sc. — Director, Stevland Bridge
AI readiness should not be defined by technology alone, because AI is a tool and it is still about the people inside the organisation and the leadership who drive where the organisation is heading. Without the right mindset and direction, the tools will not be optimally helpful; they may even be contradictory or counterproductive. The human factor comes first.
Because at the end of the day, AI is a tool, and it is still about the people inside the organisation and the leadership who actually drive where the organisation is heading and what direction we want to go. Therefore, it is very critical for leadership to understand what we are going to use AI for.
This includes how it affects ways of working, the culture of the organisation, and how we will use the tools more efficiently. It also involves how we manage and anticipate improvements in both people and tools, as well as the capability of people using these tools.
So, it is always the human factor first to ensure that we are ready to use these tools. Without the right mindset and direction, the tools will not be optimally helpful; they may even be contradictory or counterproductive, making things more complex and not helping the organisation achieve its goals.
Because, again, artificial intelligence is a tool. It has happened before in human history that we use technology to replace human capabilities, such as typing, calculating, and organising, which are now largely done by computers. However, with artificial intelligence, some roles will shift.
Anything that is overdone is always dangerous, especially over-reliance on AI. In several areas, there are still many discussions showing that results are not always accurate; some analytical outputs are not very precise and still require significant human intervention to ensure they meet expectations.
Over-reliance is definitely risky, especially if there is no proper control or human oversight to ensure that the results from the tools are as expected. This becomes even more critical when managing high-risk programmes or projects within an organisation.
So, this is a real concern in AI adoption. We need to use it step by step while ensuring sufficient controls are in place, especially for critical processes within the organisation.
Whatever tools we make or use, they can be like a double-edged sword: you can use them for good or for bad. Therefore, we need to ensure that we are using AI in ways that are ethically correct; otherwise, the trust and integrity of the work we have done can be in jeopardy.
The trust given to us as individuals who need to deliver results must always be maintained. We need to be clear on what we can and cannot do, because if tools are used unethically, they can create dishonest or untrustworthy work. It is dangerous to produce those kinds of deliverables or results. At the end of the day, it is still humans who receive and are impacted by our products and technology. If the process is unethical, we risk losing confidence and trust from the people who rely on us and the systems we use.
Human judgment also needs checks and balances, just like AI, because both can have biases. It is very critical for us to define the core values that should guide the judgments we make.
If everything is automated, we rely on the judgment of the machine, which also has its own biases based on the perspective of the developers and the data it is trained on. So, the system has its own bias as well. As humans, we need to ensure proper control is in place. We need both approaches: using tools or machines to help us become less biased, while also ensuring that we can control and evaluate the system to reduce its bias.
The main reason is efficiency, because some of the work that was previously done by people can now be supported by AI. Roles and workflows can be shortened, made more efficient, and simplified, making daily operations easier for people.
As with any other tool, people’s capability development is very necessary. For example, with the growth of AI, more and more people are sharing how to use these tools effectively, such as writing the right prompts and giving the right instructions. That is one key capability.
So, it is important to build capability in using the tools correctly, understanding the context of their use, and knowing how to strategise and connect the tools, the results, and the people within the organisation. This remains a critical human responsibility. Therefore, continuous capability development is essential to ensure everything is used correctly and delivers results as expected.
Organisational culture plays a very critical role in building and using AI. Culture is a set of values, so how we use these tools should align with those values. For example, if one of the values is integrity, then we need to use these tools honestly and ethically. This is critical to have as a foundation, because without it, the tools may not be used productively or aligned with the organisation’s ambition and purpose.
This is part of the change management element within the organisation. Change always triggers different responses from people, because those who experience or are impacted by the change will have their own thoughts and reactions.
Organisations that succeed usually manage the change well. They align and engage people from the beginning, understand their concerns, and communicate the “why” behind the change. As a result, people can align their ways of working with the desired outcomes.
On the other hand, organisations that face resistance often lack this engagement. They do not fully address the concerns of the people who will use or be impacted by the change. So, rather than being a technology issue, this is more about change management, the process of change, and the involvement of people within the organisation, which are critical for successfully integrating AI into their people strategy.
Whatever the organisation does, trust is always critical. People only want to do business with those they can trust. So, when we adopt AI technology, we still need to maintain this critical element of trust within the organisation.
For example, if people trust that we have the capability in a certain area, even when we use AI, we need to ensure that the results are reliable and can be held accountable. At the end of the day, it is still people who will be held accountable for the outcomes produced. If we lose trust due to a lack of accountability, then the organisation will not succeed. Therefore, maintaining trust is a key challenge in adopting AI technologies.
Is AI readiness mainly a technology question?
No. According to the expert, it is a people and leadership question first: leadership must understand what AI will be used for, how it affects ways of working and culture, and how people and tools improve together.
Which decisions should stay human?
Critical decision-making, managerial processes, and strategic decisions are still primarily conducted and performed by humans, even as administrative work shifts to technology.
What is automation bias, and why does it matter?
Automation bias is over-reliance on AI outputs without proper control or human oversight. It is risky because AI results are not always accurate, especially for high-risk programmes and projects.
Why does ethics matter if the team is technically skilled?
Because AI is a double-edged sword: it can be used for good or for bad. Unethical use can produce dishonest or untrustworthy work and destroy the trust of the people who rely on the organisation’s products and systems.
What makes AI integration succeed or fail?
Change management. Successful organisations align and engage people from the beginning, understand their concerns, and communicate the “why” behind the change; organisations facing resistance often do not.
AI readiness is built the same way trust is built: through people, values, and leadership. Tools can make work faster and more comprehensive, but they cannot supply the judgment, ethics, and accountability that make the results worth trusting. Organisations that invest in capability, culture, and change management, and that keep humans accountable for outcomes, are the ones most likely to make AI work for them rather than against them.
