7 formas de inovolucrar a tus empleados en el uso de la IA generativa

7 ways to engage your employees in using generative AI

The effective deployment of generative AI will depend on human adaptability rather than technological capability. As with any other aspect of digital transformation.

The human factor, people and culture, will drive AI adoption – or not. Therefore, companies will need to invest resources and time in identifying how to leverage their cultural strengths. In addition, it will be necessary to implement processes that compensate for their cultural weaknesses to encourage AI adoption.

If a culture is passive-aggressive or resistant to risk-taking, incentives must be put in place to reward adoption. Conversely, if a culture is entrepreneurial and takes advantage of any new opportunity that appears in the market, these incentives should be refocused to reward discipline and focus.

How to boost employee engagement with generative AI?

Seven generalisable points can be detailed to improve the business case for adopting generative AI. We elaborate on them below.

1. Understand where resistance comes from

Resistance to change exists in all companies and organisations. To remain competitive, they must be able to move forward and progress.
Generative AI is no exception. There are many companies that resist on the grounds that they have already tried different ways of doing things. Also, fear of the unknown overshadows any desire for change. Change will effectively strengthen an organisation if it can be implemented throughout its systems. But change agents are needed within the organisation to combat the pockets of resistance in the system. It is these pockets of resistance that become enraged by any change, as they see it as a threat.

It is important to understand where this resistance comes from and the logic behind it. Sometimes it may be explicit, as there are organisations that see fit to ban the use of generative AI. Other times, the informal resistance that needs to be addressed is that of employees who are reluctant to change. The best way to combat this fear is to explain the ways in which the technology can help the organisation. Also, change this negative opinion into a positive, or at least neutral, one.

2. Focus on the problem to be solved

Generative AI is a very versatile technology and while this can be seen as a positive, it sometimes acts as a disadvantage. Sometimes the problem it can solve is not seen in an obvious way, which can dilute its importance. To eliminate this drawback, organisations must identify the most critical challenges to be addressed. Once the objective has been unpacked, AI needs to be tested alongside other solutions.

For example: one hospital was experiencing constant scheduling problems in its operating theatres. A drawback that led to long waiting lists and underutilisation of resources. Instead of implementing generative AI without clear direction, the hospital first defined its specific problem: optimising the scheduling of surgeries. After identifying this critical challenge, they explored various solutions, including traditional optimisation algorithms and management software. They then integrated generative AI to analyse patterns in historical surgery data, predict operating times more accurately and suggest optimal schedules.


Small improvements will represent a better approach to implementing technological innovations. Start using AI with an open mind and experimental mentality. Learn from even the first failed attempts to create the right conditions for long-term success.

For example, an e-commerce company wanted to improve the customer experience on its website by using generative AI. Instead of implementing a complex solution all at once, they chose to start with a small project: personalising product recommendations on the homepage. Using a simple generative AI model, they launched a first version that generated recommendations based on the user’s recent browsing history. During the first few weeks, the company observed and analysed the results, making incremental adjustments to the algorithm based on actual user behaviour and data collected. This experimental phase allowed them to quickly identify what worked and what didn’t, without compromising the overall user experience.

Over time, incremental improvements to the recommendation algorithm led to a 15% increase in sales conversions. The company continued to adopt this “less is more” approach to other areas of its platform. That way, it ensures that every small improvement will be based on previous data and learnings. This ultimately resulted in a continuous and sustainable optimisation of the customer experience.

4. Intuition is the common enemy

AI, which produces “human-like” activity, is often seen as a threat to people’s control, power and autonomy. It is true that it often reduces human freedom and improvisation. Workers fear being displaced by the very technology they are training. It is important to focus on the fact that some control over minor decisions can be relinquished so that people can concentrate on performing tasks that add more value.

For example, in one financial services company, analysts spent a great deal of time processing data and performing routine calculations to generate financial reports. These analysts feared that implementing AI to automate these tasks could jeopardise their jobs. However, the company’s management decided to address this fear proactively. They implemented an AI system to handle repetitive tasks and automate basic reporting.

Rather than seeing this as a loss of control, the company stressed that this automation would free analysts from monotonous tasks. They could then focus on more complex and strategic analyses that require human judgement and expertise. Analysts were able to spend more time interpreting data, identifying trends and advising clients on personalised financial strategies. This not only improved service quality, but also increased job satisfaction among employees, who now felt more valued and challenged in their roles. The company experienced an increase in operational efficiency and higher customer satisfaction. Thus, it was demonstrated that relinquishing some control over minor decisions can produce more meaningful and value-added work.

5. Everyone wants to change until they have to change

Change is an attractive idea until we realise the effort and persistence it requires to execute it. The idea of having an organisation that has already gone through all the stages of experimentation is tempting. However, the real work is the time spent in trial and error, navigating these stages and learning from them.

A supermarket chain decided to implement an AI system to optimise inventory management and reduce food waste. At first, they faced many challenges: errors in predictions, employee resistance and the need for continuous adjustments. But, management persevered, providing training and fostering a culture of learning. After several months of trial and error, the system began to work effectively. Waste was reduced by 20% and the availability of fresh produce improved. Employees finally recognised the value of change. It was demonstrated that success requires time and effort dedicated to continuous improvement.

6. Overcoming cultural resistance

We must see cultural resistance as an established parameter that exists in companies. Just as we treat the climate when we choose the clothes we wear: not as something you can change, but as something you have to take into account.

The key is to put in place new systems and processes that counteract the effects of culture, such as incentives, that revise the established dynamics. Especially when they are established from a middle position within the company. Since the behaviours of these people tend to inculcate new habits in the rest of the people.

7. Be proactive about ethical concerns

Generally, media coverage of generative AI is quite sensationalist. It can lead to moral dilemmas, legal and ethical concerns. Organisations need to be transparent with users and allow people to opt in and show how the application of AI can be an improvement over existing processes. Not only will it keep the company out of real trouble, but it will convince sceptics that generative AI can be an improvement on their day-to-day work and lives.

One human resources company implemented generative AI to review resumes and shortlist candidates. This company was proactive in addressing ethical concerns. In addition, they were transparent with applicants about the use of their data. They offered candidates the option to opt out and explained how AI improved fairness by eliminating unconscious bias in the selection process. This transparency and focus on improving the process not only mitigated legal and ethical concerns, but also increased candidates’ trust in the company and improved the quality of hires.


Progress is not just about adopting technological innovations as they emerge. It is also about leveraging these tools to advance strategically and improve organisational effectiveness and efficiency over time.

If companies can find ways to embed AI into their culture, it is likely to result in a competitive advantage over their rivals.

Marina Albacar Subirats – Product Owner en Itequia