Artificial Intelligence (AI) has now clearly emerged as a transformative force across industries, driving innovation, efficiency, and competitiveness. However, something that’s not being thought about by a lot of businesses (yet) is that the ongoing improvement, optimisation and maintenance of AI technologies can pose significant challenges. This makes Managed AI Services an area of key strategic importance for the future.
In this comprehensive article, we will delve into the reasons why managed AI is vital and explore how managed services enable customers to effectively leverage AI technologies, with a particular focus on bridging the expertise gap. We will also touch on projected growth in demand for managed AI services and introduce the concept of FinOps in relation to cost efficiency.
What is Managed AI?
Managed AI has some similarities, but importantly some differences, with ‘traditional’ infrastructure based managed services.
All the core elements of a traditional infrastructure managed service are still generally included, such as service desk, break / fix support, RTO / RPO, SLAs BCP activities and service management activities. These are still important and probably always will be.
The differentiators in a Managed AI Services are typically the ability to maintain, improve and optimise AI solutions over time. This can require very specialised skills and knowledge to achieve this and a different approach to ‘traditional Managed Services.
It can’t be taken for granted that AI solutions continue to perform as expected and required over long periods when the data and information they ingest changes or evolves alongside customer requirements.
There is a clear need for expertise and support to ensure initial value continues to be delivered and that action is taken to react and adapt as necessary to make improvements and optimisations when potential inefficiencies or opportunities materialise.
Future Demand for Managed AI Services
It’s already well established how quickly AI (both consumer and enterprise) is growing and scaling, but the future of AI adoption is also extremely promising, with the demand for managed AI services poised to grow significantly. Studies have indicated a strong upward trajectory in this market:
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According to a 2023 report by Precedence Research, the global AI market is set to grow exponentially over the coming years. Last year, the market was valued at USD 454.12 billion; this is set to increase by 2032 to an astronomical USD 2,575.16 billion based on current forecasts. While this is certainly impressive, it must be kept in mind that this growth will depend on the appropriate AI expertise growing in parallel to help keep it on track. While this may not be feasible for a lot of companies, this is where Managed AI Services will come into play to ensure that this expertise is accessible for enterprises to take advantage of the AI boom.
- A survey by McKinsey & Company found that 61% of organisations consider AI to be their most significant data initiative. As AI's prominence grows, so does the need for specialised services that can bridge the expertise gap.
How Managed Services Enable Effective AI Leveraging
Expertise and Skill Gap
One of the primary reasons that a managed AI service can add value is the shortage of AI expertise in many organisations. Building and maintaining AI systems requires specialised skills in data science, machine learning, and AI architecture as well as more traditional technical support capabilities. Managed AI Services can provide access to a wide group of experienced professionals who can design, develop, and maintain AI solutions, bridging the expertise gap.
For example, consider a healthcare provider implementing AI for medical image analysis. To maintain the accuracy of the AI model, regular re-training of the model is required to adapt to evolving medical standards. Managed AI services can offer the capability to oversee this process and ensure that the AI remains reliable without the need for recruiting or training costing and in demand resources in house.
Cost Efficiency and FinOps
Managing AI solutions in-house can be cost-prohibitive. This can include investing in high-performance infrastructure, licences, and the manpower required to maintain and troubleshoot AI systems. Managed services can operate on a more flexible model, allowing organisations to control costs while enjoying the benefits of AI.
To further enhance cost efficiency, many organisations are turning to Financial Operations (FinOps) practices in conjunction with managed AI services. FinOps is the practice of bringing financial accountability to cloud usage. It helps businesses optimise their cloud costs and allocate resources efficiently.
Managed AI services are able to leverage proven FinOps principles and runbooks from day one, enabling organisations to monitor, optimise, and predict AI-related costs effectively. This synergy ensures that AI investments remain cost-efficient, and resources are allocated judiciously.
For instance, a manufacturing company using AI for predictive maintenance can benefit from managed services that not only offer cost-effective, cloud-based solutions but also embed FinOps practices to track and control costs proactively and allocate these to the right teams and business units and highlight and calculate ROI and unit cost metrics.
Scalability
AI workloads can be dynamic, and their requirements can change rapidly. Managed AI services offer the flexibility to scale up or down based on the specific needs of the business. This scalability ensures that resources are allocated efficiently, preventing overprovisioning or underutilisation.
A retail business employing AI for demand forecasting may experience seasonal fluctuations. Managed services allow the company to scale its AI resources during peak seasons and scale down during quieter times, optimising costs and performance.
Security and Compliance
AI systems often deal with sensitive data and must adhere to stringent security and compliance standards. Managed AI services can provide access to security experts who can implement robust security protocols and ensure regulatory compliance, reducing the risk of data breaches and potential legal or data protection issues.
For instance, a financial institution using AI for risk assessment activities would rely on the highest levels of data security. Managed services provide the necessary security expertise and compliance measures to protect sensitive customer data.
Performance Optimisation
Underlying code and databases can be monitored and improved either when assessed against agreed KPIs or when an opportunity to improve has been uncovered through proactive service management.
In addition to this, areas such as caching, code profiling and query optimisation can all play a part in this and require the right mix of skills, experience and deep knowledge of the solution in place to be able to deliver this on an ongoing basis.
When these areas are combined with the right service wrap and proactive service management, it can become a very effective way of aligning current and future performance with business strategy.
Model Management
Managing models is a specialised task and can involve training new models, fine tuning existing ones and monitoring behaviour to ensure the outputs created are correct and delivering the desired results.
Understanding the needs, best practice and continual improvement opportunities in this area can have an enormous impact on the outcome of the service for businesses.
Rapid Deployment & Onboarding
Managed AI services that are aligned and integrated with delivery teams can streamline the deployment process. This allows businesses to get up and running with AI applications quickly and be supported and optimised from day one. This speed-to-market advantage can be crucial in gaining a competitive edge.
Consider an e-commerce platform that wishes to implement AI-powered recommendation systems. Managed services can expedite the development and deployment of these systems, ensuring that the platform can offer personalised product suggestions to customers promptly and continually does so.
Continuous Monitoring and Maintenance
AI models require ongoing monitoring and maintenance to ensure optimal performance. Managed services can offer 24/7 monitoring, proactive issue detection, and rapid troubleshooting, minimising downtime and disruptions. Importantly, this extends beyond the infrastructure alone and incorporates the solution, data the ability to combine various diverse metrics to create a view on system performance and user experience
For example, a transportation company using AI for route optimisation might depend on real-time monitoring. Managed services can ensure that any deviations or issues are identified promptly and addressed to keep the fleet operating efficiently as downtime is extremely costly and impacting to the business during core hours.
Customisation
Managed AI services can also be the most effective when tailored to meet the specific needs and objectives of the organisation. This customisation ensures that AI solutions align with business goals, enhancing the value of the technology. Customers using identical cloud providers, infrastructure and designs may still be achieving very different results and requiring very different models of service depending on their specific requirements and strategies.
An example would be a telecommunications company using AI for customer support chatbots might require highly specialised, industry-specific language models. Managed services can provide customisation options to create and improve solutions that excel in the telecom domain.
Integration with Existing Systems
Effective AI leveraging often involves integrating AI solutions with existing systems and workflows. Managed services providers have the expertise to seamlessly integrate AI into the organisation's technology stack, facilitating data flow and interoperability.
For example, an insurance company implementing AI for claims processing benefits from managed services that integrate the AI model into their existing claims management system. This integration ensures a smooth and efficient claims processing workflow.
Training and Enablement
Managed AI services typically offer training and support to help organisations understand and make the most of AI technologies. This support ensures that employees can use AI tools effectively, maximising their benefits.
For instance, an educational institution employing AI for student performance analysis can rely on managed services to provide training sessions for educators on how to interpret and act upon the AI-generated insights. This can align with their longer term strategy and help empower their teams to create new opportunities for the future.
Summary
Managed AI services play a pivotal role in enabling organisations to harness the power of AI technologies. They can bridge the expertise gap, control costs, offer scalability, enhance security and compliance, and facilitate rapid deployment. As organisations continue to recognise the transformative potential of AI, the demand for managed AI services is expected to rise significantly.
By partnering with a strategic managed AI service provider, businesses can fully leverage the potential of AI to drive innovation, efficiency, and competitiveness in a rapidly evolving landscape.