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Recent research from organizations like MIT are casting doubts on how much value enterprises are truly finding in their AI deployments. We spoke with EncompaaS CEO Jesse Todd about why data remains the make-or-break component in the AI era.
Why organizations remain unprepared for AI success
Concerns around whether GenAI “works,” so to speak, have seemed to pick up steam over the last few months as early AI adopters report mixed results on their self-identified outcomes with deployments.
Todd says much of this can be attributed to a lack of planning prior to jumping into the AI rabbit hole that has left some early adopters without the results they wanted to see.
“To deploy AI properly you need to go deep into the inner workings of an organization and understand their workflows and then expose an AI to the business wisdom within the data,” said Todd. “That is actually very difficult to do and I think people are starting to realize that.”
“I think executives largely just assumed their data was ready, and many of us who have worked in this space for a long time knew that wasn’t going to be true,” he continued.
How addressing unstructured data can enable more than just GenAI use
“Unstructured data” refers to the information within an organization that does not follow a format or hierarchical sequence, and typically falls outside of relational rules. It is, therefore, typically difficult for machines (GenAI models included) to read.
This is especially problematic because estimates suggest that most of the data within a business is likely unstructured in some way, and experts like Todd say CEOs and others have long known that massive swaths of data were not organized or otherwise structured within their organizations.
“This the first time, I think, that there’s a really tangible, financial business case for finally addressing data issues,” said Todd. “Many execs are now going to say, ‘I actually cannot transform my business if I don’t fix my unstructured data.’”
As many in the data industry will tell you, concerns about unstructured data are not new. The EU AI Act and other recent legislation, as well as the demand for AI deployments that prove out value, are shifting attention to an area compliance and security leaders have been talking about for years.
“The principles of compliance and security absolutely all apply here, and I think many of those checklist items are going to be ticked basically by default as businesses make changes to their data processes to leverage GenAI,” Todd said.
To actually address the issues at hand, Todd stresses that businesses could take one of a few paths, including:
- Identifying a use case for a GenAI project and then determining where the necessary data inputs for that use case would live
- Determining the lowest-lift type or general location of data within the organization and starting there regardless of potential AI use
- Adopting an ecosystem-specific tool like Copilot, which by default would require M365 data work
No matter how a business starts their data journey, common steps to become “AI ready” include standardizing, classifying, tagging, de-duplicating, and securing data sources.
From compliance to AI: why EncompaaS is seeing more growth than ever with GSIs and Microsoft
Now, with AI dominating customer conversations, channel partners are being tasked with connecting the dots between AI desires and business outcomes. For EncompaaS, that has provided new opportunities to work with systems integrators and tech giants like Microsoft.
“We’re still in the education part of this journey, but our relationships with partners are growing very fast because they are the ones being asked by companies to deploy AI, and the big integrators and consultants know it won’t work unless the data is ready for it,” Todd said, noting that those integrators know they’ll likely take the brunt of the blame if companies don’t see an ROI on costly AI programs.
Todd told us he’s seen the channel build upwards of 70 percent of the company’s pipeline over the last several months.
“We are seeing a much deeper pipeline through Microsoft and through GSI partners because these organizations are able to articulate and sell the AI and they also understand the challenges businesses will face, but they need someone like us to help with the ‘dirty work’ involving the data,” Todd said.
EncompaaS has been in business since 2016, but the latest use case for data management is accelerating demand in new areas moving forward.
