The commercialisation of AI is gaining pace. The technology is deeply integrated with numerous internal and customer-facing products and services. Current statistics say AI-powered chatbots will handle 65% of customer service interactions, 30% of routine administration will be AI-managed, and generative AI tools will complete 40% of programming tasks.[1] Pressure is undoubtedly increasing on businesses to incorporate AI more fully into their growth strategies to stay competitive in a rapidly changing business environment.
Driving growth through AI integration can be difficult. Our research shows that 40% of technical discussions among your C-suite peers are focused on accelerating AI integration processes to maintain momentum and improve the bottom line. Based on what’s being said, there’s concern that adopting AI, which may take many months or even years, is too slow and opportunities are being lost.
The critical AI barriers to overcome
Among the challenges of leveraging AI, three key issues to focus on are data quality, inconsistent formats, and data silos. Data quality is responsible for 80% of AI project failures.[2] Issues include data inaccuracies, incompleteness, duplication and inadequate labelling. Data inconsistencies arise when the same types of information are stored differently; for instance, dates may be recorded as strings, characters or numerical timestamps. Additionally, data silos significantly affect the success of AI projects because fragmented data can reduce model accuracy and increase the risk of AI hallucinations.
To improve quality, you want to establish a monitoring process that involves real-time pipeline checks and catching issues as they arise. Data tools on the market use AI to automate data validation, completeness reviews and audits. You also want to implement governance policies without delay that ensure data consistency across your different data sources and systems moving forward.
Data silos have been a well-known issue since the early days of data analysis. To break down silos, you need to centralise your data and should be creating data lakehouses that combine the flexibility of a data lake with the structure of a data warehouse. Furthermore, these architectures make implementing robust data security and governance mechanisms easier.
The areas of infrastructure to address
After tackling those three issues, you should ensure your underlying infrastructure is compatible with AI and delivers the necessary computational power, memory, data storage management and network performance. Addressing these areas will make your initial commercialisation of AI easier, and you’ll be able to scale the use of AI across your organisation faster.
You want to keep on top of data governance and control your security threats from the beginning. AI can increase data risks through privacy violations, data leakage and even model poisoning. With AI becoming more embedded in your growth strategy, you should take these problems in hand. To maximise security, it’s best to follow a multi-step process that covers development pipeline security, model protection, network and infrastructure security, monitoring and incidence reporting, continuous training and compliance oversight.
Think about your skills
Although AI projects may be underway, it’s clear why many organisations are experiencing a slow pace of development. It takes significant time and resources to ensure your data, infrastructure and security are suitable, even before commercialisation begins. Furthermore, while 81% of IT professionals feel confident they can integrate AI into their roles, only 12% have substantial experience working with AI[3] highlighting a skills gap you must consider.
Simplify AI commercialisation from strategy through to scale
To overcome the challenges and maintain momentum for AI commercialisation, you should simplify deployment to increase time to value. An enterprise-grade solution that provides an integrated, end-to-end AI infrastructure will speed up progress and prevent lost opportunities.
Ideally, you want to adopt an AI-ready platform whose open ecosystem provides the tools to create best-in-class data pipelines. In addition, it must ensure access to the cutting-edge AI, machine learning generative AI models and data analytics tools you’ll need. From an infrastructure perspective, the same platform should also offer a data lakehouse architecture and be underpinned by AI-optimised hardware with validated AI designs.
If success depended solely on the technology, it would just be a choice of the infrastructure. But as we’ve suggested, AI experience and the accompanying skill sets are vitally important, and you’ll want to work with a platform provider that can help you strategise, implement, adopt and scale as you commercialise your AI.
The Dell AI Factory
That’s why we’ve created the Dell AI Factory with NVIDIA to help companies extract maximum value from AI and generative AI. The industry’s first and only end-to-end enterprise AI solution,* the Dell AI Factory with NVIDIA is designed to speed AI adoption by delivering integrated Dell and NVIDIA capabilities to accelerate AI-powered use cases, combine data and workflows, and enable you to create your AI journey for repeatable, scalable outcomes.
It delivers a comprehensive portfolio of AI technologies, including validated turnkey solutions and expert services, to help you achieve your AI outcomes faster. From desktop –to data centre, you can start building your organisation’s skills with a clear understanding of data pipelines and business processes, enabling your business to scale as your needs grow.
Discover more about our AI story and learn how the Dell AI Factory with NVIDIA can advance your AI strategy.
* Based on Dell analysis, July 2024. Dell Technologies offers solutions with NVIDIA hardware and software engineered to support AI workloads from PCs with AI-powered features and workstations to servers for high performance computing, data storage, cloud native software-defined infrastructure, networking switches, data protection, HCI and services.
[1][1] https://www.spritle.com/blog/100-game-changing-ai-statistics-for-2025-trends-shaping-our-future/
[2] https://www.ihlservices.com/news/analyst-corner/2024/10/80-of-ai-projects-fail-why-and-what-can-we-do-about-it/
[3] https://www.pluralsight.com/resource-center/ai-skills-report-2024
