The artificial intelligence (AI) market is growing exponentially. Since 2000, the number of AI start-ups has increased 14-fold, while occupations requiring AI skills have increased nearly five-fold since 2013, according to Stanford University. AI Index. Given the growing importance of this emerging technology, ambitious and visionary companies have already implemented specific AI strategies in their organizations. As a result, many companies see AI as the most important competitive advantage of the 21st century.

While large enterprises have the resources to build a team of AI experts, small and medium-sized enterprises (SMBs) don’t, and that’s where managed service providers (MSPs) come in. ).

AI Implementation Challenges

A recent survey suggests that approximately 87% of global organizations believe AI solutions will give them a competitive advantage. Overall, AI can dramatically increase business productivity and performance. However, implementing AI is a complex process and comes with its own set of challenges, namely lack of skills, data requirements, and high costs.

Lack of skills

Talent shortages in technical skills still exist in companies. For a successful implementation of AI, companies need highly experienced professionals. However, they don’t always have the budget and bandwidth to absorb technical experts.

Data requirements

Successfully implementing an AI model requires consistent access to structured, unbiased data for meaningful results. Unfortunately, companies often struggle with data processing, organizational, compliance and security issues, which complicate the adoption of AI.

High costs

There is no one-size-fits-all AI solution and customization is crucial as business needs and goals vary by organization. Building an AI talent pool for custom solutions and investing in advanced technical infrastructure is expensive for businesses.

How MSPs Can Help AI Adoption

MSPs can help mitigate common challenges by helping businesses properly adopt AI. That said, adopting AI is a complicated and resource-intensive task. It’s no surprise, then, that companies are turning to MSPs to launch their AI models quickly and successfully, whether for every business operation or for part of the development of a specific AI model on the subscription basis. MSPs can build better data solutions, improve customer experience, provide robust data protection, develop business intelligence, develop custom applications, and optimize performance.

Build better data solutions

MSPs can partner with companies and develop measurable quality metrics for AI data refinement and accuracy. They can also identify patterns from available information, help with data structuring and analysis, and troubleshoot data issues that businesses typically face when implementing AI. MSPs set up the data framework and ensure that high-quality, error-free data is constantly available for AI implementation.

Improve customer experience (CX)

According to IBM, chatbots can manage 80% interactions with customer service. As a result, AI-powered chatbots improve response times and increase customer satisfaction. These chatbots also reduce labor costs for companies looking to improve their customer support.

Provide robust data protection

AI can offer solutions to counter cyber threats such as Distributed Denial of Service (DDoS) attacks. One way to prevent attacks is to detect abnormal network activity. Many modern antivirus and anti-malware solutions rely on AI to run lightning-fast scans, identify threats, and eliminate them without human intervention. Many IT security solutions also leverage AI to keep networks secure and robust.

Develop business intelligence

Affordable and effective data analytics provided by MSPs can help businesses make smarter decisions faster, thanks to smart algorithms that turn MSPs into oracles that can turn raw data into valuable material. By taking the service of an MSP that is also an innovative data analytics service provider, companies can take advantage of the exponential growth of Big Data.

Develop custom applications

Few MSPs are able to build custom AI applications for their customers in today’s AI market. However, it is a cost-effective practice that can help businesses achieve their business goals. To build custom applications, MSPs can leverage open source AI and machine learning (ML) platforms such as Tensor Flow. Neural networks created using these platforms can automate business tasks and processes, giving companies better decision-making power while adding an additional service to the MSP portfolio.

Optimize performance

As MSPs have greater market exposure, they have better access to experts and technical platforms. Additionally, through networking, MSPs can help develop better AI solutions aligned with the business operating model. Thus, they also optimize organizational performance, as companies can focus on their core functional processes without worrying about implementing AI.

Key benefits of partnering with MSPs for AI adoption

Managed service providers bring many benefits to a customer enterprise’s adoption of AI. Working with an MSP can improve efficiency, increase velocity, close talent gaps, improve quality, and reduce brand risk.

Improved efficiency

Developing and implementing an internal AI model takes a lot of time, money and effort for companies. They must collect and annotate data; build, test, and deploy the model; and monitor it frequently. MSPs can help companies automate and build pipelines for these processes more efficiently and without needing to spend resources, engage in trial and error, or create a new AI initiative.

Increased speed

Managed service providers have ready-to-use tools and highly trained technicians to get AI models off the ground and up and running easier and faster in no time. Thus, partnering with an MSP helps businesses stay competitive with faster execution and scaling rates.

Talent gap filled

Recent studies have revealed that the talent gap for the technical skills needed for AI development is only growth – partly due to the lack of internal AI development. While retraining or hiring new employees can be helpful, studies show that seeking expertise from an MSP can be an effective and more affordable way to fill the void.

Improved quality

If a company tries to source data on its own, the process can be very difficult and time-consuming, and it can end up creating biased data sets. All of these mistakes will ultimately lead to limited or inefficient AI models that fail to offer valuable insights or automate a process. A managed service provider offers modern quality controls and efficient data management tools, which can bring better accuracy and consistency to AI models.

Reduced brand risk

Sometimes developing AI models carries a risk for a company, especially if it publicly fails in production. A Gartner report indicates that approximately 42% of survey respondents do not fully understand the benefits of AI. In these cases, partnering with an MSP can minimize this risk, as MSPs are more likely to know the pitfalls and have proven processes and tools to implement AI with minimal risk.

Additionally, working with an MSP can help AI startups launch their first products or help existing AI companies maximize operational efficiency. In other words, businesses of any shape and size can benefit from this partnership.

Conclusion: Implementing AI via MSPs

Developing AI models is a complex process that requires high costs, specialized skills, efficient pipelines, and tons of resources. Thus, large enterprises often partner with managed service providers to manage their AI adoption and workflows, which could ultimately result in a higher return on investment (ROI).

Today, MSPs are an inevitable solution for companies competing in the AI ​​space, whether they are struggling to launch AI models or simply looking to improve their operational efficiency. As AI transforms from a fringe element to a core part of modern business, we can expect more diverse services from MSPs across the entire AI value chain.