Managed AI vs. Doing it Yourself: Which is Right for You?

Managed AI represents a new approach to implementing artificial intelligence in business. The key lies in how AI solutions are maintained and operated. Managed AI focuses on the optimisation and maintenance of AI systems without requiring clients to build or deploy infrastructure themselves. Instead, experts handle everything behind the scenes, ensuring systems run smoothly. Internal management of AI, by contrast, requires organisations to develop, implement and manage AI systems internally, demanding significant technical expertise and ongoing commitment. Choosing between these approaches depends on your organisation’s resources, technical capabilities, and strategic priorities.

What is the difference between managed AI and taking care of AI internally?

The fundamental difference between managed AI and internal AI is in who handles the technical implementation and ongoing maintenance of AI systems. With managed AI, specialised service providers take care of optimisation and maintenance without building or deploying infrastructure, allowing clients to focus on using AI rather than maintaining it. Internal AI requires organisations to handle all aspects internally, from development to implementation and ongoing management.

In an internal AI approach, your organisation needs to build a team with AI expertise, develop custom solutions, deploy them on your infrastructure, and continuously manage technical aspects. This approach gives you complete control but demands significant resources and technical knowledge.

Managed AI solutions, on the other hand, operate on a service-based model where the technical complexity is abstracted away. The service provider ensures your AI systems run efficiently without your team needing to understand the underlying technical details. They handle optimisation, monitoring, and maintenance, ensuring everything runs smoothly behind the scenes.

This distinction is particularly important for organisations looking to implement AI but lacking specialised AI talent or wanting to focus resources on their core business rather than technical AI management.

How does managed AI work behind the scenes?

Managed AI works by having expert teams handle all the complex optimisation and monitoring processes that keep AI systems running efficiently. These specialists maintain your AI solutions ensuring optimal performance through continuous monitoring, adjustments, and maintenance activities.

Behind the scenes, managed AI providers implement monitoring systems that track key performance metrics such as response times, accuracy rates, and resource utilisation. These systems automatically detect anomalies or performance issues before they impact end users.

Regular optimisation activities include fine-tuning AI models to maintain accuracy, adjusting resource allocation to control costs, and implementing prompt engineering to improve response quality. Providers also conduct ongoing evaluations to ensure outputs remain consistent and reliable over time.

Maintenance activities involve addressing potential issues proactively, applying updates to underlying systems, and scaling resources as needed to accommodate changing demand. All of these processes happen seamlessly without requiring client involvement in the technical details.

Through this comprehensive behind-the-scenes management, organisations can enjoy the benefits of cutting-edge AI without the technical burden of maintaining complex systems themselves.

What are the benefits of choosing managed AI?

Choosing managed AI over internal solution offers several significant advantages, with the most notable being reduced technical burden on your organisation. By eliminating the need to build in-house AI expertise and infrastructure, you can focus resources on core business activities while still leveraging advanced AI capabilities.

Cost-effectiveness is another major benefit. Managed AI typically operates on a predictable subscription model, converting large capital expenditures into manageable operational costs. You avoid expenses related to hiring specialised AI talent, purchasing infrastructure, and handling unexpected technical issues.

Ongoing optimisation ensures your AI systems continuously perform at their best. Expert teams proactively monitor and fine-tune your solutions, maintaining high performance levels without requiring your involvement in technical details.

Scalability becomes significantly easier with Managed AI. As your needs grow, the service can scale accordingly without you needing to redesign systems or add technical staff. This flexibility allows organisations to start small and expand their AI usage as value is proven.

Additionally, Managed AI services often provide access to more advanced AI capabilities than most organisations could develop internally. These services incorporate best practices from working across multiple clients and stay current with the latest AI developments.

Finally, Managed AI leverages proven methodologies and experiences from previous deployments, increasing the likelihood of successful AI adoption.

Who is the ideal candidate for Managed AI solutions?

The ideal candidates for Managed AI solutions are organisations that want to leverage AI capabilities without building extensive technical expertise internally. Resource-constrained businesses that lack specialised AI talent but recognise the potential value of AI in their operations will benefit most from the managed approach.

Companies focused on rapid implementation are excellent candidates. If your priority is quickly integrating AI into business processes rather than developing AI systems from scratch, Managed AI provides a faster path to value with significantly reduced implementation time.

Organisations that prefer to focus on their core business rather than technology management are well-suited for Managed AI. This includes businesses where technology supports operations but isn’t the primary product or service offering.

Enterprises with fluctuating AI needs also benefit from Managed AI’s scalability. If your AI requirements vary based on seasonal demands or growth phases, the flexibility allows you to scale resources up or down without maintaining excess capacity.

Additionally, businesses concerned about staying current with rapidly evolving AI technologies find Managed AI valuable. The service provider handles keeping systems updated with the latest capabilities, relieving you of the burden of continuous research and development.

Companies at any stage of AI maturity can benefit, from those just beginning their AI journey to organisations looking to optimise existing AI implementations for better performance and cost-efficiency.

How do you transition to Managed AI services?

Transitioning to Managed AI services begins with a thorough assessment of your current AI systems and objectives. This evaluation should identify which components are suitable for Managed AI and establish clear goals for the transition, whether focused on cost reduction, performance improvement, or reduced management burden.

The next step involves selecting an appropriate Managed AI service provider with expertise relevant to your specific AI applications. Look for providers with experience in your industry and with similar AI implementations to ensure they understand your unique challenges.

Develop a detailed migration plan that outlines the transition process, including timelines, responsibilities, and potential risks. This plan should minimise disruption to ongoing operations while ensuring a smooth handover of responsibilities.

During implementation, work closely with your chosen provider to transfer knowledge about your existing systems and business requirements. This collaboration ensures the service is properly configured to meet your specific needs.

After migration, establish clear communication channels and performance metrics to monitor the effectiveness of the managed service. Regular reviews help ensure the service continues to meet your evolving business requirements.

Many organisations benefit from a phased approach, transitioning one AI application or component at a time rather than attempting a complete overhaul simultaneously. This approach reduces risk and allows for adjustments based on early experiences.

Throughout the transition, focus on change management for affected teams, ensuring they understand how to work effectively with the new managed service model and can take full advantage of its benefits.