MSPs have always been the architects behind clients’ tech ecosystems, balancing reliability and security. But as AI reshapes business expectations, that role is evolving into something more strategic.
Generative AI has quickly gone from a far off aspiration to the centerpiece of the digital transformation revolution. A majority of businesses across industries, both big and small, are eager to infuse the technology into their business processes after hearing about the widely promised productivity gains that will “transform businesses” and drive revenue.
But realizing these benefits doesn’t happen overnight. From on the ground work at Sherweb with MSPs, we’ve discovered that while approximately 70% of small to mid-size businesses are actively seeking to integrate AI, both they and the businesses they serve have foundational work that needs to be done before AI can make any real impact.
Luckily, setting this foundation and becoming AI-ready does not need to take long. Here are four steps that MSPs can follow to make AI aspirations a reality in just 90 days.
1. Bridge the gap between siloed data sources.
While data is hardly in short supply for the majority of businesses, the information is typically fragmented and spread across multiple systems and channels. This makes it difficult for AI models to be trained and optimized on businesses’ specific needs.
Since creating one holistic space where all data will live securely is a larger project, in order to start meeting AI ambitions right away, companies can set up temporary connectors to bridge the gap between data sources. By pulling specific, safe datasets and fusing them together, the AI can quickly access the data points it needs to function properly.
For example, if an AI application is being used to automate customer support, MSPs can tie together external and on-prem data sources related to that capability, such as support request tickets, in order to jumpstart their AI program.
2. Raise security walls to protect data.
For any company, protecting data from security threats should be top of mind whether they are implementing AI or not. And, while AI has troves of benefits, the technology is not always without bugs, which means there is a risk of data leakages.
To protect against this, MSPs’ can limit the access AI has, only introducing the models to the data sets essential to its performance. Restricting the AI from accessing sensitive information is critical, especially before all of a company’s data has been reviewed and cleaned. Higher risk data sources can be vetted properly while the AI is working off of cleared data.
3. Determine where AI will make the biggest impact in business.
Once any and all security concerns have been addressed and AI has access to the data it needs to function, companies can begin identifying where it will make the most immediate impact in their day to day business.
While companies typically have a few upfront AI tasks and use cases in mind, in the rush to deploy AI tools, many businesses overlook the bigger question: how AI changes what’s worth automating in the first place.
Doing an in-depth analysis of the areas where AI could be the most beneficial is essential to actually seeing gains from AI implementation.
The target area will differ from business to business, but AI can be infused to do everything from integrating Copilot to streamlining everyday workflows to building more customized use cases. All of the capabilities can also be tested first with smaller sub groups in the company. If they see success, then the business can roll them out company wide.
4. Break down data barriers for a holistic foundation to run AI on.
Data is the key to AI. In order for a company to become truly AI centric, data from across the entire organization needs to be centralized in one, accessible location.
As companies begin implementing AI in the short term through the above steps, they should simultaneously be working towards building this overarching data infrastructure. Once in place, MSPs can remove the temporary barriers they installed upfront, giving AI access to any data point it may need to execute its given goals.
AI success isn’t solely about technology adoption. It’s about operational readiness and a mindset shift. As AI becomes more and more ingrained in our society, this four step approach will allow MSPs to achieve the upfront speed needed to immediately compete, while also putting them on the path to reap longer term benefits as the technology evolves.
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