Artificial intelligence (AI) is emerging as a fundamental pillar of digital transformation. Companies are facing unprecedented challenges regarding data secularism, environmental sustainability, and combating information silos. The non-optimization of resources and infrastructure hinders the growth of AI potential. The centralization of information, necessary for effective analysis, remains crucial in this complex context. Securing AI systems compels leaders to anticipate growing threats. At the same time, the sustainability imperative forces companies to reassess their practices to reduce their ecological footprint. Adopting an integrated approach proves vital to navigate this ever-evolving landscape.
NetApp has highlighted the pressing issues faced by organizations worldwide in their efforts to optimize their artificial intelligence (AI) strategies. The transition to large-scale AI capabilities is a key challenge for 2025, as stated by Gabie Boko, Chief Marketing Officer of NetApp.
Investments for Innovation
Many companies report that their data is largely optimized for AI purposes. Two-thirds of leaders assert that notable improvements have been made in terms of data accessibility, accuracy, and documentation. Unfortunately, maturation towards true artificial intelligence still requires significant investments, particularly from technology executives.
Thus, 40% of technology leaders expect that unprecedented investment will be needed by 2025 to enhance data management and AI capabilities. The pursuit of significant progress calls for increased commitment in financial resources and infrastructure. Leaders who are prudent about investing can expect significant rewards in terms of innovation and efficiency.
Elimination of Data Silos
One of the major obstacles identified by the NetApp study concerns data fragmentation. Nearly 79% of technology leaders believe that data unification, reducing silos, and smooth interconnectivity are essential prerequisites for leveraging the full potential of AI. Companies that adopt unified data storage are better at overcoming these challenges.
By connecting data, regardless of type or location, in multi-cloud hybrid environments, these companies ensure constant accessibility while mitigating fragmentation. Organizations focusing on data unification are significantly more likely to achieve their AI goals by 2025. Nearly 30% of companies that neglect this prioritization fear missing their targets.
The Growing Risks of AI
The rapid adoption of AI comes with increasing challenges, particularly regarding security. More than 41% of executives anticipate a marked increase in security threats by 2025 as AI becomes integrated into various operational aspects. The rise in attack surfaces grants access to new vulnerabilities while presenting unique challenges, including the protection of sensitive AI models.
Leading nations in the AI race, such as India, the United States, and Japan, will face these security concerns more acutely. The efforts made by companies to address these issues are starting to pay off. Indeed, the percentage of leaders placing cybersecurity and protection against ransomware at the top of their priorities has decreased by 17% since 2023.
Sustainability and Environmental Footprint
The growth of AI raises pressing questions about sustainability. Approximately 34% of technology executives anticipate that advances in AI will necessitate radical transformations in corporate sustainability practices. Meanwhile, 33% expect the emergence of new government policies dedicated to managing energy consumption.
The infrastructures required to operate AI, which transform raw data into commercial value, demand considerable energy consumption. Countries with a high concentration of AI are likely to feel these environmental impacts more intensely. A majority of 72% of companies continue to focus on reducing their carbon footprint, although this figure has dropped from 84% since 2023.
Data as the Foundation of Innovation
Krish Vitaldevara, Senior Vice President and General Manager at NetApp, emphasizes that organizations that have succeeded in advanced analytics and AI share certain characteristics. These organizations benefit from unified and well-categorized data, as well as robust security for sensitive information. Confronted with inherent challenges, they manage to combine innovation and responsibility.
For organizations to evolve without causing irreparable harm to the environment while adopting responsible practices, maintaining ecological accountability alongside technological growth is of paramount importance.
The full NetApp report is available here (PDF).
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Another read: Companies struggle to establish governance frameworks for AI.
To explore these issues further, corporate technology events and webinars are available here.
(Photo by Chunli Ju)
Frequently Asked Questions about Key Success Factors in Artificial Intelligence: Security, Sustainability, and Elimination of Silos
What are the best practices to ensure data security in artificial intelligence?
It is essential to implement robust security protocols, such as data encryption, multi-level authentication, and ongoing employee training on security risks related to AI.
How does eliminating data silos contribute to improving the efficiency of AI systems?
Eliminating data silos allows for seamless integration of information, thereby facilitating access and collaboration among various departments, improving the quality of data used by AI systems.
What investments are needed to ensure the sustainability of artificial intelligence initiatives?
Organizations must invest in environmentally friendly infrastructures, energy-efficient technologies, and adopt sustainable practices, such as optimizing energy consumption for data centers.
What are the major challenges related to security in deploying artificial intelligence?
Challenges include protecting AI models from attacks, managing system vulnerabilities, and the need for continuous oversight to detect potential threats in real time.
How can companies measure the maturity of their artificial intelligence strategy in terms of sustainability?
Companies can assess their maturity by analyzing key performance indicators (KPIs) related to sustainability, such as reducing their carbon footprint from AI operations and the environmental impact of their technologies.
What role does data governance play in eliminating silos and securing AI systems?
Good data governance establishes policies and standards to ensure data integrity, quality, and accessibility, thereby reducing silos and enhancing the security of AI systems.
How do current trends in AI affect corporate sustainability initiatives?
Current trends toward increased AI adoption push companies to rethink their environmental practices by integrating AI solutions that optimize resource use and minimize ecological impact.
