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Latest AMD Report Finds, AI Development Outpacing Current IT and Security Readiness

Latest AMD Report Finds, AI Development Outpacing Current IT and Security Readiness

Modern IT organizations have shifted their focus toward innovative AI workloads. New concepts such as generative AI, automated machine learning (AutoML), multi-modal deep learning, facial recognition, AI-based cyber security, and tons of other technologies feature at the top of the “to-do” lists of all major IT companies. IT companies are developing superior, futuristic AI development roadmaps for their organizations by increasing their investments in new platforms, tools, and modernization. Despite the general sense of optimism among the IT leaders, and a fair raise in the budgets, 52% of IT leaders feel their existing IT infrastructure is unprepared to effectively use AI workloads. According to the latest online survey conducted by Edelman Data & Intelligence and commissioned by AMD, IT leaders are concerned their outdated systems, security infrastructure, and inexperienced employees stand in the way of a successful transformation with AI tools and applications.

AMD’s AI Outlook pointed out the various advantages of using AI-enabled technologies in the enterprise and also cautioned leaders about the new range of concerns and challenges that have emerged due to the uncontrolled pace of AI development in recent months, particularly since the launch of ChatGPT and similar generative AI tools.

A key outcome of the survey pointed to the common belief that AI can improve workplace efficiency. Without the adoption of AI, organizations could be missing out on the big opportunities in the market. That’s not all; AI and machine learning are also key to an organization’s pace of innovation and growth. But, to what extent can AI actually make organizations perform better with their existing IT and security infrastructure?

Here are the top key takeaways from the AMD AI Outlook report, highlighting the state of AI development in 2023 and its impact on IT infrastructure and security readiness.

Who’s more Agile: Smaller or Larger Organizations?

Organizations rely on time-tested formulas to stay relevant during turbulent times. However, these strategies may not work during uncertainties that strike businesses without any previous history of managing volatile market scenarios, such as pandemic-driven lockdowns, or geo-economic meltdowns. AI, by that means, centers itself as a potent enabler in helping business leaders become more receptive to changes in the environment. Companies that adopt different AI tools and digitized systems are found to be more receptive and successful in meeting their objectives during tough times. This is achieved through a focused and continued transformation based on agility.

What is agility?

Agility is the inherent or acquired ability of an individual, system, or organization to identify, process, and respond to any unforeseeable change through flexible, fast, and effective processes designed with human-machine relationships in mind. Now, different-sized organizations could have a varied approach to agility and its effectiveness in change management. AI is a potent capability that can shape the approach of organizations toward agility.

For instance, AMD found that larger organizations have a longer timeline for AI adoption, while smaller organizations move quickly by integrating AI systems in less than six months.

While it’s true that agility provides a solid foundation to manage future needs, it is also important to understand that introducing AI into existing digital transformation journeys pivoted on agility-driven processes can introduce a new range of risks for the organization. For IT leaders, the lack of familiarity with new AI techniques such as generative AI, Natural Language Programming, and Facial Recognition systems poses challenges in the current context of AI-led accelerated digital transformation.

Teams that have built agile leadership to tackle the new challenges and risks arising from AI development are likely to foster organizational agility and effectiveness in delivering high-quality projects. 

Security is the biggest barrier to AI adoption

The rising number of AI-powered phishing attacks and AI-enabled automation ransomware incidents have spooked the IT industries around the world. The number of ransomware incidents has more than quadrupled since 2021, forcing IT leaders to take on cybercriminals, using AI tools. But, how do IT leaders look at AI for security?

AMD’s survey found that IT professionals are growing skeptical about the role of AI tools in breaching existing security infrastructures. 67% of IT leaders said that AI tools could introduce new types of risks in the security and data governance domains. These risks could be linked to inexperienced teams that are unfamiliar with the evolving norms of AI data training and storage. It is important for AI leaders in IT organizations to ensure that enterprise data frameworks are 100% secured to prevent any kind of security mishap.

Organizational Development Lacks Focus on AI Skills Training

According to AMD, IT leaders are still unclear about the scale and scope of training required to bring the existing workforce up to speed on AI-related roles and projects. In 2022, IBM published a report identifying the top five barriers hindering the pace of AI development in organizations. According to IBM’s Global AI Adoption Index 2022, 34% of respondents mentioned the lack of AI skills, expertise, or knowledge as the biggest barrier to scaling projects with Artificial Intelligence (AI).

Taking Action on AI Adoption Has Clear Benefits

Organizations that are taking action in AI development and adoption through increased investments are likely to lead the industry and generate visible benefits across their value chain. 75% of AI optimists identified in the AMD report stated that failing to invest in AI in 2023 is a bigger risk for their organization as they fear it could result in falling wayward in the competition. However, 46% of IT leaders state their organization isn’t prepared to implement AI within the next few years. While the use cases of AI technology continue to expand in the era of LLMs and AutoML, it is also worth noting that the lack of AI skills, the lack of IT infrastructure, and unclear security policies present a nuanced picture for the global IT and security leaders in 2023.


AI’s ethical and responsible usage will continue to be at the center of every IT discussion involving data engineers, cloud architects, edge programmers, and security officers. AI’s use across industries would largely depend on how quickly organizations embed their systems with the technology in a secure, fully compliant environment, meeting human potential at scale.

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