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Opinion: AI Adoption in Enterprise and the Fear of Missing Out or Messing Up

Yarob Sakhnini, Vice President, Emerging Markets – EMEA at Juniper Networks says given AI’s capacity to enhance business operations and boost productivity to new heights, it is crucial for enterprises to invest in this technology. 

Amidst the ever-evolving business landscape, artificial intelligence (AI) has been the centre of attention in recent years. But what is it about AI that makes it so compelling? The enormous power it holds to revolutionise entire industries and expand the boundaries of human intelligence beyond the limits of our imagination could offer a reasonable explanation. Research shows that AI has the potential to generate a real value of $150 billion across all sectors of the combined economies of the Gulf Cooperation Council (GCC) countries in the Middle East.

Given AI’s capacity to enhance business operations and boost productivity to new heights, it is crucial for enterprises to invest in this technology in order to stay not just competitive, but also relevant. However, serious concerns still exist regarding bias, governance and security. IT leaders are currently struggling to find a balance between the fear of missing out (FOMO) and the fear of messing up (FOMU).

Juniper Networks partnered with Wakefield Research to carry out a survey of 1,000 global executives engaged in AI and machine learning within their organisation. The results assess AI adoption, growth and integration at companies globally.

The Growing Pressure for AI Technologies Implementation

 One of the most interesting findings of Juniper’s global survey is that 82 percent of participating IT leaders say they feel somewhat or significantly pressured to quickly implement AI solutions. While keeping up with competitive trends is certainly important for businesses, this rush has the risk of leading to a superficial sense of readiness, where the depth of integration and understanding of AI’s full potential and implications may not be as thorough as claimed.

Given AI’s central role in numerous GCC national initiatives and strategic plans, the growing adoption of AI technologies should not be surprising. For instance, the UAE Strategy for Artificial Intelligence aims to establish the UAE as a world leader in AI by 2031, while the Saudi Data & AI Authority (SDAIA) has developed the National Strategy for Data & AI with the ambition to position Saudi Arabia among the top 15 countries in AI. Recently, the Technology Innovation Institute (TII) of Abu Dhabi’s Advanced Technology Research Council (ATRC), has also announced the launch of the ‘Falcon Foundation’, which committed to provide US $300 million to fund the development of open-source Generative AI models and sustainable ecosystems around open-source projects.

The pressure is on, yet enterprises still need to figure out how to proceed carefully so they don’t risk falling behind. Since they need to fine-tune for accuracy and iterate rapidly, starting safe, where scale is manageable, could be a wise choice.

Factors Affecting the Deployment of AI

AI implementation in enterprise is hampered by many different types of challenges. Juniper’s survey reveals that IT leaders rate data quality (47 percent) and privacy concerns (46 percent) as the top two challenges of implementing AI in their organisation. Businesses must make sure their data is well-structured, complete and relevant to the AI tasks while adhering to transparent data practices to build trust and mitigate legal risks.

Furthermore, the integration with existing legacy systems increases the complexity of making AI work smoothly. Limited processing power can also have an impact on AI performance. Notably, 42 percent of IT leaders surveyed agree that the lack of employee expertise is another significant concern.

The Significance of AI-Native Networking

 In addition, enterprises have to deal with the consequences of insufficient AI networking infrastructure. ΙΤ leaders who participated in the Juniper Networks AI survey  have identified that these consequences include the need for external expertise (52 percent), inaccurate data output (50 percent), increased costs (49 percent), delayed implementation (48 percent) and loss of data (37 percent).

Fortunately, all of these potential consequences can be addressed with a modern,  AI-Native Networking Platform, purpose-built to leverage AI to assure the best end-to-end operator and end-user experiences. Moreover, cutting-edge AI Data Centre solutions now offer an extremely fast and flexible way for enterprises to deploy high performing AI training and inference clusters, while also being simple to operate when IT resources are limited.

AI Adoption’s Impact on Workforce

Seventy-eight percent of IT leaders surveyed expect that the implementation of AI will result in more responsibilities for employees. What’s more, 85 percent believe that employees’ ability to use AI will somewhat or significantly impact their opportunity for career advancement. It seems obvious that AI, like any new disruptive technology, will inevitably replace humans in a variety of positions.

On the other hand, by taking over repetitive and mundane tasks, AI allows IT employees to focus on more complex and innovative projects that require human creativity and critical thinking skills. This can lead to increased job satisfaction and productivity as employees are able to engage in tasks that are more stimulating and fulfilling. On top of that, new job requirements and skill sets will emerge as a direct consequence of AI adoption. While generative AI does show up in natural language, it’s still mostly statistics, therefore employees that want to advance in their jobs need to work on their skills in areas where statistics aren’t applicable.

As businesses across industries continue to invest in AI technology and integrate it into their operations, its impact on the global economy and society will become more and more apparent. From automating routine tasks to revolutionising customer support and product development, AI has the potential to unleash unprecedented levels of efficiency and innovation.

However, AI adoption in business operations needs to be approached thoughtfully and strategically. For enterprises starting out, it’s critical to focus on key areas such as networking to build a solid foundation for ongoing AI success. Prioritising the domains where AI can provide its greatest benefits while minimising risks is essential for building trust in the technology and mitigating any possible drawbacks.

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