AI Readiness, Business Performance, and the Role of HR

I was recently speaking to senior HR colleagues here in Singapore, both advanced and incipient in their efforts to implement AI in their organizations, and the conversation inspired me to read up about the latest thinking about this topic.

Academic research is already suggesting that there is a direct link between AI readiness in the workforce and stronger business performance. The organizations that have prepared their employees to effectively use AI tools are better able to achieve better outcomes in growth, profitability, and customer satisfaction.

However, the improvements are more likely  related to increased employee capabilities, and not to the tools being used, or where in the organization they are applied. What sets these organizations apart is that they are building the skills to apply it in ways that matter, and not whether they have access to AI.

AI maturity will track with performance

Companies that move beyond pilots and are embedding AI into workflows, redesigning tasks, and training employees are already seeing better results. These organizations tend to be clear on where in the organization AI can improve performance, and they are acting accordingly.

Those that are still exploring or hesitating, are also falling behind. Many of these organizations perhaps feel that their workforce is not ready to use GenAI. This is where HR has a critical role to play.

HR must lead the shift in capability

When HR gets actively involved, the organization moves faster. If HR takes part in governance, leads training, and supports change management, AI adoption is prone to increase, the workforce will be better prepared, and leaders will be more confident in driving transformation.

Some effective HR teams are encouraging business leaders to model the use of new tools, and involving employees in identifying the work that can be improved, either by doing it faster or better, with AI. This way, development goes beyond learning about how to make the technology useful and safe to use, but also about data management, evidence-based decisions, and business processes improvement.

It stands to reason that, if training goes beyond awareness of tools and into use cases that can improve quality and productivity, there will be more application of AI tasks and better results will follow.

The most common content today still centres on ethics, data protection, and general literacy. That is a start, but it is not enough. People need to learn how to use AI in their roles, how to assess the output, and how to apply it to improve outcomes.

Perhaps ironically, the people that should worry the most about being replaced by AI are those in companies that are scaling its use. The more employees engage with these tools, the more likely it is that they will understand their potential. Without clear communication and credible plans for upskilling and redeployment, that understanding becomes anxiety.

HR needs to be proactive here. People want to know how their roles may change and what they can do to stay relevant. We will to reassure, guide and equip, where we can.

AI readiness, while valuable, is only part of the equation. Having AI skills is not the destination, but the means to ensure employees and organizations are more future-ready, can plan more effectively, can identify and close skill gaps more quickly, can make better use of internal talent and can adapt faster when things change.

This is the standard HR should aim for. Help people use today’s tools well, but also prepare them to adjust and grow as work continues to evolve.

What HR can do next

• Audit current readiness. Map where AI tools are available, how they are being used, and what training has already been provided. Focus on actual usage, not policy.

• Integrate AI into learning, not beside it. Use existing leadership and development platforms to introduce practical AI training, aligned with real tasks and workflows.

• Make leaders the first learners. Equip executives and managers to use AI themselves. If they do not use it, they will not lead it.

• Redesign roles before reskilling. Break jobs into tasks and identify which ones are likely to change. Build training around the work, not the job title.

• Track both adoption and outcomes. Measure where AI is being used and what results it is delivering. Use that data to shape future workforce plans.

• Link AI capability to career opportunity. Make it clear how new skills connect to progression and mobility. That will drive motivation.

Achieving this kind of organizational readiness will require a sustained effort to match changing work with capability needs. This is where the next wave of business performance will come from, and HR needs to be at the front of the change required to get there.