Private vs public AI: Which should your business use in 2025?

Private vs public AI: Which should your business use in 2025?

On the Eleventh Day of AI, we explore the differences between private and public AI, the key benefits and downsides of private AI, future trends, and how to get started with a hybrid AI approach.

Imagine a Christmas where your business predicts market trends before they happen, streamlines operations effortlessly, and secures sensitive data with elf-like precision. This isn't a far-off dream – it's the reality of artificial intelligence (AI) today. But AI is not a one size fits all solution – there are different types of AI to consider, and steps to take to lay the groundwork for successful AI adoption. In fact, the AI industry value is projected to increase by over 13 times over the next six years due to the ever increasing advancements in this space. Two of these variations are private and public AI – both with their own set of capabilities and drawbacks.

As a business leader, you face a festive decision: should you harness the power of private AI, or leverage the vast resources of public AI?

Private or public AI?

Public AI operates on hyperscale cloud-based platforms and tends to be accessible to multiple users and businesses. These platforms leverage vast amounts of data from various sources, providing powerful, general-purpose AI capabilities. However, this accessibility comes with trade-offs in terms of security and data privacy.

Private AI, on the other hand, is tailored and confined to a specific organisation. It offers bespoke solutions, retrained to meet the unique needs of a business while ensuring data remains secure within the organisation’s cloud or private infrastructure. This approach mitigates the risks associated with public AI, such as unauthorised data sharing and security breaches.

The joys of private AI

1. Security: One of the key advantages of private AI is enhanced security. By operating with a dedicated model and within a private environment, businesses can protect sensitive information and ensure compliance with data privacy regulations. This is particularly crucial for sectors handling confidential data, such as healthcare, fintech, and government agencies.
2. Performance: Private AI can deliver a more tailored performance, customised to specific business requirements. With dedicated hardware, businesses can optimise AI workloads for speed and efficiency, leading to more accurate and timely insights.
3. Control and customisation: Private AI offers greater control over the AI environment. Businesses can customise their AI models to align with their strategic goals and operational needs. This level of control is invaluable for developing bespoke solutions that drive competitive advantage – this also provides a wider choice of customised models that can be deployed.

These benefits might look tempting to business leaders, but it’s also important to consider the downsides.

The frosty side of private AI

1. Costs: Implementing and maintaining private AI infrastructure can be expensive. The costs associated with dedicated hardware, specialised talent, and ongoing maintenance can be a significant barrier for smaller organisations.
2. Complexity: Managing private AI requires a deep understanding of both AI technologies and the specific business context. This complexity can make it challenging to deploy and scale AI solutions effectively without the right technology partner.
3. Scalability: While private AI offers tailored solutions, it may lack the scalability of public AI platforms. Businesses need to carefully plan their AI strategy to ensure they can scale their AI initiatives as needed without compromising performance or security.

Private AI in 2025 – future trends

In 2024, we have seen significant advancements in AI infrastructure, making software more accessible and flexible, though hardware costs remain high. The trend towards making private AI more consumable for smaller players is expected to continue into 2025. Large organisations will continue to lead in adopting private AI, but we anticipate a shift towards more experimental and flexible AI environments, enabling businesses to develop and refine their AI capabilities internally.

The introduction of regulatory frameworks like the General AI Bill will also shape the future of AI deployment. Businesses must ensure their AI models are trained on unbiased data and adhere to ethical standards, avoiding issues like AI hallucinations and misinformation.

The 12 Days of AI

• On the First day of AI, we explore how AI is being used in marketing, the benefits and key use cases, as well as concerns and how marketers can best take advantage of the technology.
• On the Second Day of AI, we look at the importance of truly understanding what AI is to enable true organisational transformation.
• On the Third Day of AI, we explore some of the key trends to keep an eye on, and prepare for, in 2025.
• On the Fourth Day of AI, we discuss the value of adopting AI responsibly, and outlines how businesses can build responsible adoption into their plans.
• On the Fifth Day of AI, we explore how AI is reshaping HR – boosting productivity, addressing concerns, and preparing organisations for the future.
• On the Sixth Day of AI, we explore how leveraging AI and cloud can enhance business performance and share tips for successful implementation.
• On the Seventh Day of AI, we explore the double-edged sword of AI in cybersecurity and how businesses can protect themselves against the cyber grinches.
• On the Eighth Day of AI, we explore the key considerations and strategic frameworks essential for extracting maximum value from AI projects.
• On the Ninth Day of AI, we explore the critical role data plays in AI implementation and the key steps business leaders must take to prepare their data for a successful AI future.
• On the Tenth Day of AI, we explore the evolving role of AI in managed services and what to expect in 2025.

Taking the reins

Adopting a hybrid AI approach, which combines the strengths of both private and public AI is an increasingly attractive proposition for business leaders. Using both ways of implementing AI can be a more accessible way to leverage certain private AI capabilities, while keeping costs and time investments to a minimum by supplementing with public AI. But adopting a hybrid approach to AI is not just a technology choice but a strategic business decision, and business leaders need to consider the following steps:

1. Evaluate your AI needs: Assess the specific requirements of your business and determine where AI can add the most value. Identify the types of data you need to protect and the AI capabilities you require.
2. Find the right partner: Collaborate with partners who understand the AI stack and can provide the necessary expertise and support. Look for partners with a proven track record in AI implementation and security.
3. Focus on security and ethics: Ensure your AI solutions adhere to stringent security protocols and ethical guidelines. Implement secondary AI layers for fact-checking and to prevent AI-generated misinformation/hallucinations.
4. Plan for scalability: Develop a roadmap for scaling your AI initiatives. Consider how you will manage and grow your AI infrastructure as your business needs evolve.

By carefully considering these factors, businesses can effectively leverage AI technologies, harnessing the power of both private and public AI to drive innovation, enhance performance, and maintain a competitive edge. A hybrid approach to AI is not merely a Christmas toy; it is a strategic imperative for businesses aiming to thrive in the AI-driven future.

Chris Folkerd is director of core infrastructure at ANS, a digital transformation provider and Microsoft’s UK Services Partner of the Year 2024. Headquartered in Manchester, it offers public and private cloud, security, business applications, low code, and data services to thousands of customers, from enterprise to SMB and public sector organisations.

Originally published at ECT News