The Intelligence Layer is Here

Data, Documents, and Context

In the current era of artificial intelligence, hype moves fast, but transformation requires patience. For Bob Muglia, former CEO of Snowflake and longtime Microsoft executive, the message to enterprises is clear: don’t race to build, race to understand.

At a time when AI promises to upend how organizations function, from customer engagement to internal knowledge access, Muglia cautions that without quality data, contextual understanding, and structured experimentation, most initiatives will fall short. “AI only knows as much as the data that it is trained on or fed with,” he emphasized. “If you can’t come up with that data in a coherent way, you are not going to be able to use these models very effectively.”

Through his experience guiding transformative tech companies, Muglia has observed the AI shift from both the infrastructure and application layers. His view of the future is grounded in pragmatism: AI can empower business value, but only when built on strong foundations.

Hype Cycle Meets Reality Check

“We are at the top of the hype cycle,” Muglia observed. After an explosive year of AI discourse and pilot deployments, he predicts a near-term cooling period, followed by more grounded, enterprise-specific adoption. “We may not have to wait real long,” he added, predicting the industry will soon enter the “slope of enlightenment.”

This maturity curve means leaders need to resist the pressure to “do AI” for AI’s sake. “There’s a significant likelihood that whatever you build today will get replaced in a few years,” Muglia said, urging CIOs to focus on learning rather than locking themselves into a particular stack. 

Instead of chasing short-term ROI, he encourages organizations to identify key friction points in their workflows where AI can introduce leverage, particularly in document-heavy operations, data access, and information retrieval.

Unstructured Documents to Intelligent Workflows

One such opportunity lies in contracts and documentation, which often reside in unstructured formats rich in content but difficult to mine for insights. Muglia spotlighted a company doing just that: Docugami.

“They’ve built an application that lets people take the contents of a contract and turn it into essentially a semi‑structured document where the data can be accessed directly… they’re reinventing the lifecycle for contract creation.”

The significance of this use case is broad. Every enterprise wrestles with vendor contracts, legal terms, and partnership arrangements, and these documents often require manual oversight, repeated review, and intense administrative lift.

By introducing structure to what was once opaque text, AI helps organizations manage risk, ensure compliance, and even accelerate negotiations. It’s not just about digitizing paper. It’s about turning knowledge into an active asset.

Retrieval-Augmented Generation

But smart contracts are only the beginning. The future of enterprise AI, according to Muglia, hinges on the ability to bring knowledge and models together, bridging the gap between generic intelligence and domain expertise.

“These models are intelligence that can be applied to a business problem,” he said. “But that intelligence has to be combined with knowledge.” Enter Retrieval-Augmented Generation (RAG), a framework where language models pull from enterprise-specific databases or document stores to contextualize their responses.

“You combine a database or a knowledge base or some set of documents with a large language model, and you provide that information into the prompt,” Muglia explained. The result is a system that can answer business questions with precision, not by guessing, but by drawing from trusted internal data.

This is the architecture that makes AI useful inside the enterprise firewall. A model trained on internet-scale data won’t know your pricing tiers, your legal standards, or your internal compliance rules. “You need to take data knowledge that's relevant to your business and combine it with this intelligence,” he said.

Data Readiness Is Everything

Of course, none of this works if the data is disorganized. Muglia was unequivocal: data readiness is the prerequisite for AI success. “It has to be provided with data in order to provide answers,” he emphasized. “If you can’t come up with that data in a coherent way, you are not going to be able to use these models very effectively.”

Most enterprises today face sprawling data landscapes, both structured and unstructured, and siloed across teams and tools. Before LLMs can generate insights, the data must be integrated, governed, and made queryable.

That means bringing rigor to how data is collected, stored, and surfaced. Companies that invest now in cleaning their data pipelines and building interoperable knowledge systems will be poised to unlock the true power of generative AI.

Designing with Future Replacement in Mind

While Muglia is optimistic about the future of AI, he urges a healthy amount of skepticism when it comes to permanence. This is the nature of an ecosystem advancing at exponential speed.

Rather than resisting change, smart CIOs should architect for modularity and swapability. He recommends building things in a way that they can evolve. That includes using open standards, layering internal logic over general-purpose tools, and resisting vendor lock-in.

Strategic Lessons

Across each of these cases, Muglia’s message is consistent: be thoughtful. The technologies are real, but the strategic value comes from careful application.

“The big companies have definitely won to begin with, but I think as AI gets incorporated into everything, one of the attributes of this technology is that it can be built into pretty much any existing application, and it can create new categories of applications, as well,” he said.

In the meantime, Muglia urges restraint. “It’s time to experiment and to learn,” he said. “It’s not necessarily the time to make the giant investment.”

Building the Intelligence Layer

Muglia sees a future where enterprise workflows become deeply intelligent, not because of flashy front ends, but because of aligned context, clean data, and flexible architectures.

And while this journey will require patience and iteration, the outcome is a workplace where information flows as freely as conversation and where AI doesn’t just answer questions, but understands what questions matter most.