AI Readiness Declines in Mexico: Cisco

Enterprises in Mexico have experienced a decline in their readiness to adopt AI technologies, with only 13% of organizations considering themselves fully ready to implement it in their operations, reports Cisco.

"Companies realized they did not know what they did not know, and as their AI strategy matured, they saw they had more needs before they were ready for AI," says Ramon Viñals, Director of Sales Systems Engineering for Latin America, Cisco. He highlights that organizations face a growing challenge in implementing effective AI strategies with expectations and pressure for results on the rise.

The Cisco 2024 AI Readiness Index aims to measure the readiness of enterprises to adopt AI across six pillars: strategy, infrastructure, data, governance, talent, and culture. Despite the growing urgency to implement AI expressed in the report, companies in both countries face significant obstacles in several of these key areas.

"Regardless of where they are on their AI journey, organizations need to be preparing existing data center and cloud strategies for the changing requirements," says Jeetu Patel, Chief Product Officer, Cisco, to Computer Weekly.

AI Adoption in Mexico 

In Mexico, 99% of companies reported an increased urgency to adopt AI in the last year, driven primarily by senior management requirements. However, despite these pressures, only a limited number of organizations have managed to align their IT strategies with the demands imposed by AI.

Technology infrastructure is one of the most critical gaps. According to the report, only 20% of enterprises have the graphics processing units (GPUs) needed to meet current and future AI demands, while 33% of organizations have the capabilities needed to protect data in AI models through end-to-end encryption and security audits.

Over 62% of companies allocate between 10% and 30% of their IT budget to AI projects, focusing primarily on three areas: cybersecurity (43%), IT infrastructure (34%), and data management (46%). Many business leaders reported that the returns on these investments have not met their expectations, with half of the respondents indicating that they have not seen clear benefits in key areas such as process automation or improved operational efficiency.

Another recurring problem is the lack of qualified talent in areas essential to AI implementation, such as infrastructure, data, and governance. Only 27% of companies believe they have adequate staff to carry out AI projects, a decrease of 2% compared to the previous year. This lack of specialized talent represents a critical obstacle to AI initiatives, especially in countries where technical skills shortages remain a significant challenge as Mexico.

The report also found that companies underestimated internal resistance to AI adoption. Eighty-two percent of companies still struggle with data silos, and only 35% believe their organization has a good level of understanding of global data privacy standards. Less than a third (32%) of respondents report high readiness from a data perspective to adapt, deploy, and fully leverage AI technologies. Only one in three respondents (35%) believe there is a high level of understanding across their organization about global data privacy standards.

Despite all these challenges, the report highlights that 65% of companies in Mexico face increasing pressure to succeed with their AI strategies within a year.

"Eventually, there will be only two types of companies: those that are AI companies and those that are irrelevant," Patel says.

Cisco’s Recommendations

Cisco offers  several key recommendations for companies adopting AI, starting with investing in scalable, adaptive, and secure infrastructure. 

“As AI workloads grow, companies need to ensure their IT systems are capable of handling these increased demands,” reads the report. “This means adopting technologies that not only support current needs but also scale to meet future demands, ensuring agility in AI initiatives”. 

Security also plays a critical role; organizations must protect data throughout its lifecycle, from transit to storage, and establish robust defenses against unauthorized access, data tampering, and emerging threats specific to AI, such as model poisoning and prompt injection. 

In parallel, effective data management, integration, and governance are crucial to the success of AI projects. According to the report, organizations should prioritize data quality, ensuring it is consistent, accurate, and accessible, while also implementing comprehensive governance frameworks to ensure compliance with evolving regulations. 

Regularly updating internal policies and protocols is also essential to keep pace with the rapidly changing AI landscape. As the demand for AI talent increases, organizations must focus also on developing and retaining their workforce by creating continuous learning opportunities and fostering cross-functional collaboration. 

“Encouraging skill development within existing teams can help mitigate the talent shortage, expanding the talent pool without the need for costly external hires,” reads the report. “Finally, cultivating a pro-AI organizational culture is key to maximizing the potential of AI technologies, with leadership playing a pivotal role in aligning AI initiatives with long-term business goals and inspiring confidence across the organization.”