Generative artificial intelligence has transformed the way we work in many organisations, but we still find some companies that are light years away from embracing AI.
A few months ago we came across this headline: “BBVA achieves an explosion of innovation among its employees with the deployment of ChatGPT Enterprise“. The data presented in the article is quite compelling. Only five months after distributing 3,000 licenses among its employees:
- 83% have incorporated the tool into their work routine and use it on a weekly basis.
- 3,000 GPTs have been created, one per user. Of these, 700 are available in the company’s own GPT Store, open to all employees who use the tool.
This initiative boosted learning in the use of generative AI and, above all, led to an improvement in skills in record time. The key was the distribution of the tool to a large volume of employees, which generated a ripple effect: everyone wanted to be part of this transformation, and the adoption curve grew exponentially.
The “Anti-Adoption” Companies
Many companies have not yet taken this step, and AI adoption is at a standstill.
In these companies, the corporate AI project is usually in the hands of the IT department, which does not release the tools to the rest of the organisation for one of the following reasons:
- High licensing costs (ChatGPT or Copilot). At first glance, the price of licensing tools such as ChatGPT or Copilot may seem like a significant expense. However, if each employee using them manages to increase their productivity by 15-20%, the return on investment changes the equation completely. Rather than a high cost, it could be seen as an investment in efficiency and optimisation of daily work.
- Fear of loss of control of corporate information. Many organisations fear that the use of AI tools involves risks in data management and security. Concerns about the possible leakage of sensitive information or third party access to critical data can slow down the adoption of these technologies. However, today’s solutions offer configuration and security options that enable tight control over the use of AI within the enterprise, minimising the risks without sacrificing the benefits.
- Lengthy review and approval processes. The implementation of new technology tools often faces lengthy and bureaucratic validation circuits. The need to align multiple areas, obtain approvals from different levels and ensure regulatory compliance can delay the adoption of solutions that have already proven their effectiveness. Streamlining these processes without compromising rigour can make the difference between an innovation that is successfully integrated and a wasted opportunity.
- Pilot tests that never scale. In many companies, AI projects start with enthusiasm through well-structured pilot tests. However, these tests often remain at an early stage and fail to be implemented on a large scale. The reasons for this can be varied: lack of a clear adoption strategy, resistance to change, or lack of metrics to demonstrate real business impact. Designing pilots with a clear vision of scalability and measurable objectives is key to turning these tests into real transformations.
The result? Teams with high motivation towards the use of AI that cannot benefit from the potential of these technologies, missing the opportunity to improve their productivity and generate greater value for their customers.
AI: the real challenge is not technological, but cultural
I recently heard a phrase in a talk that I thought was very apt: “AI is not going to create a technological problem in companies, but one of adoption”.
Resistance to change, lack of training and internal bureaucracy are the real obstacles preventing AI from becoming a driver of growth and efficiency. It is not just about implementing a tool, but about transforming the organisational culture so that employees integrate it into their daily work.
In some cases, the barrier is directly the implementation of the tool: the paralysis of IT teams is limiting the evolution of their companies.
AI for Upskilling and Reskilling
Adopting AI is not just a question of efficiency, but of talent development. The learning curve for these tools is surprisingly low, but their impact on improving skills is huge.
Generative AI enables employees to learn faster, automate repetitive tasks and spend more time on strategic activities. It also becomes a key ally for upskilling and reskilling programmes, helping teams to develop new skills in an autonomous and agile way.
Moving to action: keys to successful adoption
If an organisation wants to accelerate the adoption of AI and maximise its impact, there are three key fundamentals:
- Open and secure access: technology should be accessible to employees, with appropriate security protocols, but without unnecessary restrictions.
- Tangible use cases per department: working on concrete examples of how AI can improve daily work in each area, builds confidence and motivates teams to use it.
- Training and support: Providing access is not enough. It is key to accompany with practical training so that employees can integrate AI into their day-to-day work.
Companies that understand that AI adoption is a business issue and not just an IT challenge will be better positioned to take advantage of its potential. It is not a question of whether it is time to bring AI into the workplace, but how to do it strategically and effectively. So you decide: do you want to give your teams tools to explore and innovate, or are you going to keep waiting for the ideal tool to arrive while your competitors leave you behind?