Linking Cloud Maturity to Ignite Today’s AI Adoption Curve

Early adopters of cloud technology valued its speed and flexibility, avoiding lengthy procurement processes and the need to build virtual infrastructure. However, they faced challenges in choosing between IaaS, PaaS, and SaaS, as well as deciding on public, private, or hybrid clouds, with numerous vendors entering the market, each offering unique strengths.

More teams are using AI because it helps them finish tasks faster. However, they face a confusing mix of models, platforms, and vendors, as well as systems that link different AI agents for their work. As with cloud computing, using AI more can lead to unexpected challenges due to its complexity.

Cloud’s early security lessons focused on mistakes with access and setup. Misconfigured S3 buckets and storage blobs exposed sensitive data. As data moved off-premises, organizations struggled with losing visibility and control.

AI is raising important concerns about data sources and information verification. Like cloud computing, sending data outside the organization poses compliance risks. Problems are emerging as employees may unknowingly use public AI tools for sensitive data.

Cloud technology changed spending for organizations. Companies now use subscription pricing rather than paying a large upfront fee for hardware, which lowers barriers to entry but introduces unpredictability. As providers adjust their storage and data transfer fees, companies must frequently reassess their cloud strategies to manage costs.

AI operates on a token-economy model, starting with free or low-cost trials and transitioning to a pay-as-you-go model based on factors such as context, memory, and output length. As models improve, additional charges for advanced usage complicate budgeting, prompting organizations to continuously reassess their AI expenses, like cloud spending.

Cloud adoption slowed due to a lack of skills, concerns about being locked into a single vendor, and the need for stringent security checks. AI adoption faces the same three challenges:

• Skills: Teams must learn to create prompts, train models, and monitor results for reliable outputs and a good return on investment.
• Lock-in: Selecting the right model for each case is important. Creating your own LLMs and agents can be costly to maintain.
• Due diligence: AI security questionnaires verify how and where data is used and protected, like cloud vendor assessments.

Drawing on how cloud maturity played out, NetOps teams serious about scaling AI should:

1. Identify real business use cases first
2. Manage security risk proactively
3. Understand the true cost
4. Keep humans accountable

Organizations that use AI, like successful cloud users, will gain the most benefits by planning carefully and establishing security from the start. Once properly integrated, it will be hard to imagine working without it.

Read the full article or discover how BackBox leverages AI to enable NetOps teams to automate network device lifecycle management and security operations;visit our platform page. Ready to get started? Request a demo to see BackBox in action.