AI implementation via MSPs

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The artificial intelligence (AI) market is growing at an exponential pace. Since 2000, the number of AI start-ups has grown by 14 times, while professions that need AI skills have grown nearly five times since 2013, according to Stanford University’s AI Index. Considering the rising importance of this emerging technology, ambitious and visionary enterprises have already placed specific AI strategies in their organizations. As a result, many businesses consider AI to be the most significant competitive advantage of the 21st century.

While large enterprises have the resources to set up an expert AI team, small and medium-sized businesses (SMBs) do not, and that’s where managed service providers (MSPs) come in.

AI Implementation Challenges

A recent survey suggests that about 87% of global organizations believe AI solutions will offer them a competitive edge. Overall, AI can significantly increase business productivity and performance for businesses. 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 gaps in technical skills still exist in business. For a successful AI implementation, businesses need highly experienced professionals. However, they do not always have the budget and bandwidth to absorb technical experts.

Data requirements

Successful AI model implementation requires consistent access to structured and bias-free data for meaningful outcomes. Unfortunately, businesses often struggle with data handling, organization, compliance, and security issues, which complicates AI adoption.

High costs

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

How MSPs Can Aid in AI Adoption

MSPs can help alleviate common challenges by helping companies adopt AI properly. That said, AI adoption is a complicated and resource-intensive task. So, it’s no surprise companies are turning to MSPs to launch their AI models quickly and successfully, either for every business operation or for part of a specific AI model development on a subscription basis. MSPs can create better data solutions, enhance customer experience, provide robust data protection, build business intelligence, develop custom applications, and optimize performance.

Create better data solutions

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

Enhance customer experience (CX)

According to IBM, chatbots can handle 80% of customer service interactions. As a result, AI-powered chatbots improve response times and boost customer satisfaction. These chatbots also reduce labor costs for businesses looking to enhance their customer support.

Provide robust data protection

AI can offer solutions to counter cyber threats like distributed denial-of-service (DDoS) attacks. One way it prevents attacks is by detecting abnormal network activity. Many modern antiviruses and anti-malware solutions rely on AI to run ultra-fast scans, identify threats, and eliminate them without human intervention. Many IT security solutions also leverage AI to keep networks safe and robust.

Build business intelligence

The affordable and efficient data analytics MSPs provide can help companies make more thoughtful decisions quickly, thanks to intelligent algorithms that turn MSPs into oracles that can turn raw data into valuable material. By taking up the service of an MSP that is also an innovative data analytics service provider, businesses can cash in on the exponential growth of big data.

Develop custom applications

Few MSPs are able to build custom AI applications for their clients in the current AI market. However, it is a profitable practice that can help companies meet their business goals. For building custom applications, MSPs can leverage open-source AI and machine learning (ML) platforms like TensorFlow. The neural networks created using these platforms can automate business tasks and processes, giving businesses better decision-making power while adding one more service to MSPs’ portfolio.

Optimize performance

As MSPs have greater exposure in the market, they have better access to experts and technical platforms. Besides, with their networking, MSPs can help develop better AI solutions aligned with the business operational model. Thus, they optimize organizational performance, too, as businesses can focus on their core functional processes without stressing about AI implementation.

Top Benefits of Partnering With MSPs for AI Adoption

Managed service providers bring numerous benefits to a corporate customer’s AI adoption. Working with an MSP can lead to improved efficiency, increased speed, filled talent gaps, improved quality, and reduced brand risk.

Improved efficiency

Developing and implementing an in-house AI model takes a large amount of time, money, and effort for businesses. They have to collect and annotate data; build, test, and deploy the model; and monitor it frequently. MSPs can help businesses automate and create pipelines for these processes more efficiently and without the need to expend resources, engage in trial and error, or create a new AI initiative.

Increased speed

Managed service providers possess ready-made tools and highly skilled technicians to take AI models off the ground and let them run smoother and faster in no time. So, partnering with an MSP helps businesses remain competitive with faster turnaround and scaling rates.

Filled talent gaps

Recent studies have revealed that the talent gap for technical skills needed for AI development is only growing — partly due to the lack of in-house AI development. While reskilling or hiring on new staff can help, the studies show that seeking expertise from an MSP can be an effective and more affordable way to fill the gap.

Improved quality

If a company tries to source data on its own, the process can turn out to be highly challenging and time-consuming, and it may end up creating biased datasets. All of these errors will ultimately lead to limited or inefficient AI models that will 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

At times, developing AI models carry a risk for a business, particularly if it fails in production publicly. A Gartner report states that about 42% of survey respondents do not completely understand AI benefits. Partnering with an MSP in these cases can minimize this risk since MSPs are more likely to know the pitfalls and have tried-and-tested processes and tools for implementing AI with minimal risk.

Moreover, working with an MSP can help start-ups in the AI arena launch their first products or help existing AI companies maximize operational efficiencies. In other words, businesses of any shape and size can benefit from this partnership.

Bottom Line: AI Implementation via MSPs

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

Today, MSPs are an inevitable solution to companies competing in the AI arena, whether they’re struggling to launch AI models or simply looking to enhance operational efficiency. As AI transforms itself from a fringe element to a core component of modern business, we can expect more diversified services from MSPs across the entire AI value chain.