Why enterprises still struggle to implement AI organization-wide (and what you can do about it)

As the enthusiasm around artificial intelligence (AI) reaches its peak, it has become clear that AI is no longer just a “nice-to-have” for enterprises. Now a game changer for its efficiency and productivity gains it offers businesses, it’s no wonder that nearly every enterprise has some form of AI in place.

But maximizing their AI potential can be a sizable challenge. That’s because deploying AI across the organization can require significant resources, such as technical skills and access to critical, high quality data. According to Foundry’s AI Priorities Study 2023, half of the companies interviewed are grappling with IT integration, including governance, maintenance and security, with these issues exacerbated by the lack of in-house expertise for design, deployment, which complicates the making of a business case for AI. Moreover, 94 percent of ITDMs have difficulty addressing ethical implications when implementing AI technologies, with data privacy being the number one challenge for businesses at 41 percent.

Obstacles lay ahead in AI deployment

Nonetheless, the challenges of AI deployment can be chalked up to several factors. First is the need to narrow down opportunities into its most impactful use cases, be it crafting chatbots for bettering customer service, or automating the content creation process, such as product descriptions and social media posts. At the same time, businesses need to manage, prepare and ensure the security and governance of critical enterprise data. This includes keeping up to date with the ever-evolving regulatory landscape, such as General Data Protection Regulation (GDPR). This can complicate data management while making it difficult for businesses to remain compliant with changing AI regulations.

Then there’s the increasing workload as demanded by AI applications. The use of large language models (LLMs), as well as multi-modal AI, can place immense strain on the AI infrastructure. That’s why as enterprises are looking to AI to drive increased efficiencies, building a robust AI infrastructure will be foundational to business success. Technical roles associated with AI, too, are also necessary, but this has become a gap that’s difficult to fulfill, which can lead to technical limitations in AI deployment. Finally, ensuring appropropriate and accurate responses is an ethical concern businesses need to tackle urgently. Incomplete data and the lack of multiple data sources can reduce the efficacy of AI strategies, and this can be detrimental for data-driven enterprises. In this case, the key challenge will be to identify and capture the right data for improving their offerings, and using these data to extract business value and exceed customer satisfaction.

Inadequate AI tools in the market

In addition to these challenges, businesses are also encumbered by the limitations of existing AI tools. Take for instance the lack of comprehensive end-to-end tools that will integrate AI strategies across three deployment models: edge, core data center and cloud. Many current solutions in the market are unable to support a growing range of enterprise use cases, such as their inability to process visual data or deliver actionable insights.

Then there’s the inherent complexity in using AI tools, such as AI agents. In fact, Forrester has predicted that three-quarters of organizations will fail when building their in-house AI agents. The lack of AI explainability—that is, the capacity to provide an in-depth understanding of how AI systems reach a particular decision or recommendation—can also erode trust in AI among users. At the same time, it may prevent IT teams from ensuring that their AI system is working as planned.

Behind the pillars of a powerful AI factory

Addressing these challenges is at the heart of AI factories, and a suitable solution can help businesses reap huge bottom-line returns. One trait of such a comprehensive tool is the ability to simplify AI deployment, while supporting multiple deployment options across the enterprise landscape. This translates to a fully integrated solution that offers rigorous testing and validation, while transforming data into truly valuable insights, rather than vague recommendations. Together, these features should enable businesses to fulfill data security and governance standards.

In short, the right AI factory should:

• Support enterprise AI use cases: On top of AI use cases, this should support AI applications, while including end-to-end validation to support the entire generative AI lifecycle from inferencing and retrieval augmented generation (RAG) to model tuning and model development and training.

• Work the way you want with an open ecosystem: Get the flexibility to build the operating environment for any AI operations with a comprehensive partner ecoystem stack, including colocation and hosting providers and silicon vendors.

• Deliver pay-as-you-go flexibility: This allows businesses to quickly adopt AI solutions without needing an extensive, upfront investment. With a subscription model, businesses can pay only for what they use.

• Leverage a consistent framework of solutions: These include hardware, software and strategies that free businesses to create, launch, productize and scale their AI and generative AI work streams across their teams.

• Offer professional services: A team of experts should help businesses accelerate their AI transformation from identifying the right use case to data preparation. Training and certifications, too, should also help organizations address skill gaps.

Find out more about driving your AI transformation with Dell AI Factory with NVIDIA.