Artificial intelligence (AI) is a fantastic opportunity for enterprises to automate workloads and bring order to data chaos. However, integrating it into legacy systems – and even newer infrastructure – isn’t as simple as pressing a button and letting AI do its thing.
Insufficient infrastructure to handle AI workloads, data quality and management issues, a shortage of AI talent, security concerns, unclear use cases, and even power and data centre capacity limitations can stifle your great AI roll-out.
A recent report by Channelnomics and Dell Technologies revealed.
- AI spending will grow from about $40 billion in 2023 to over $140 billion by 2027.
- AI spending will surge by 55% each year up to 2027.
- 64% of businesses will invest in AI systems over the next two years.
- 34% will ‘likely’ invest in AI systems in the same period.
- Only 2% of companies have no plans to invest in AI.
The reason for that widespread adoption is the value of AI for enterprises.
The value of AI for enterprises
The right AI can identify patterns and trends and generate reports to assist IT management in making strategic upgrades that improve operations.
Better predictive analytics, real-time insights, and data-driven decision-making capabilities enable predictive maintenance, automated incident response, intelligent workload optimisation, and cloud resource management.
Giving real intelligent creatures—humans—more time for creativity and exploration can also increase their exposure to innovation.
Dell provides partners with servers and storage devices validated for AI use to power AI workloads and applications. These automatically provision, scale, and optimise resources based on demand, reducing manual effort and IT spending.
AI also offers huge cost benefits to enterprises. It can help cut operating costs and increase productivity, increasing business profits.
Impediments to AI adoption
- Insufficient Infrastructure
Your organisation’s existing data centres, networks, and endpoint infrastructure might not be sufficient to handle the demands of emerging AI workloads.
- Data hygiene and management
If your company struggles to organise, maintain, and access large volumes of data, implementing AI could exacerbate the problem. AI systems require high-quality, accessible data to function effectively.
- Small AI talent pool
There’s a shortage of professionals with AI expertise, and the high demand for AI talent drives up salaries and makes it difficult to acquire the right talent.
- Operational Security Issues
Integrating AI into enterprise systems raises data confidentiality, integrity, and availability concerns. You need specialised expertise to ensure the security and reliability of AI-powered systems and data.
- Unclear use cases
If you lack a clear understanding of applying AI to specific operational needs and challenges, AI adoption will slow as you take more time to plan deployments.
- Insufficient power and data centre capacity
AI-focused data centres can overwhelm the capacity of power systems, leading to potential disruptions in further Artificial intelligence (AI) deployments.
