Interest in generative artificial intelligence (GenAI) remains high. However, a survey of over 250 executives shows that only 22 percent believe their organization’s data foundation is fully ready to support GenAI applications, while 53 percent think their organization is somewhat ready.
Conducted by MIT Technology Review Insights, the top challenges organizations are encountering are data governance, security, and privacy (59 percent), followed by data quality and timeliness of data (53 percent), costs (49 percent), and data silos (48 percent).
Addressing key data management challenges for MSPs
At the core of those challenges are three distinct issues that create a massive opportunity for managed service providers (MSPs). The first is simply a well-known general lack of data management expertise. Organizations have historically created and stored a lot of data, but few optimally manage much of it.
The second issue is the type of data needed to drive GenAI applications tends to be unstructured. You typically find structured data in a database format that is relatively easy to aggregate. Still, most of the data needed to build or customize a large language model (LLM) is unstructured. It resides in everything from PDF files to spreadsheets that are typically stored everywhere, from the cloud to a laptop. Aggregating all that data and converting it into a set of vectors that an LLM can consume is a major undertaking.
Finally, many organizations are realizing the use case in which they want to apply GenAI to requires instant responses from the LLM. That means data needs to be continuously fed to the LLM. Most IT teams today rely on batch-oriented processes to update applications. Only a limited number of professionals possess the expertise needed to manage near-real-time data flows.
The GenAI gap
Collectively, these issues explain why, despite the current level of enthusiasm, many organizations are finding that it will be 2025 at the earliest before they can build and effectively deploy a custom GenAI application.
In the meantime, business executives increasingly feel that their organizations are falling behind. Overall, the survey identifies the top three drivers for investing in GenAI as increased efficiency and productivity (72 percent), increased market competitiveness (55 percent), and the need to drive better products and services (47 percent), followed distantly by increased revenue (30 percent) and reduced costs (24 percent).
As the close of 2024 approaches, the anxiety level that executives have about GenAI, otherwise known as the fear of missing out (FOMO), is increasing. MSPs with the data management expertise required to ease those fears will find it a lot easier to book a meeting with those executives than they might have this time last year. Just as importantly, many IT leaders expected to drive these initiatives are realizing they’ve taken on more than they can handle.
Like any IT initiative, success always depends on how well organizations manage the data driving it. Even in the age of AI, garbage in is still garbage out. MSPs that can clean up a data management mess will likely discover they can command a premium for that expertise now more than ever.
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