The Missing Piece in AI Adoption for Organizations

AI tools are being plugged into more workflows every day, from writing code to analyzing data. But here’s the reality most leaders are starting to see: Adding AI doesn’t magically deliver value. In fact, without the right foundation, it can slow teams down.

Many organizations are trying AI tools, hoping for quick wins, but often, they end up disappointed. That’s because they’re not giving AI what it really needs to help: clear, structured, and meaningful data. Most importantly, it needs the right context, just like a new team member needs time, documents, and explanations to understand their job.

So the real question isn’t whether your employee should use AI in your daily work. It’s how we can set it up to actually make a difference.

The Hidden Problem: Fragmented and Unstructured Data

Knowledge lives everywhere: Slack threads, Confluence pages, Notion docs, Jira tickets, dashboards, spreadsheets, emails, CRMs, and more. This fragmentation includes a large volume of unstructured data such as code, text, images, video, etc, making it difficult to establish relationships between pieces of information.

Even humans struggle to gather context across these fragmented sources. Imagine being a brilliant new hire, how much context would you need to make smart decisions? Now, compare that to what we give LLMs when we ask for help. It’s often vague, minimal, and lacking structure.

Have you ever asked the model if it understands what you’re asking it to do? Or if it has any clarifying questions? Most of us don’t, but we should. Without sufficient context, even the most powerful models are left guessing.

The challenge of fragmented data isn’t just an AI problem. Human employees also face difficulty connecting the dots when information resides across different systems. If humans can’t make sense of the data, AI certainly won’t either. Addressing this gap improves both human and machine understanding.

The Need for Structured, Semantic Data

Some organizations are moving away from SaaS to gain control over their data, structuring it for AI consumption and training proprietary models. This trade-off between control and cost highlights the growing priority of data structure in AI strategies.

Some of the organizations have well-developed data pipelines. But most of them are traditional and focus solely on generating human-readable reports; they focus on aggregating raw data into reports for human consumption. But this often strips out granular details and relationships that AI needs. We need to evolve our pipelines to produce semantic layers, data enriched with meaning, relationships, and context.

To unlock AI’s full potential, organizations must structure their data semantically. This includes implementing metadata, ontologies, and taxonomies that give AI models the context they need to understand and reason. When your AI understands your data deeply, it’s not just faster; it’s smarter. That means better decisions, more automation, and a team that’s no longer stuck in manual work.

Building this semantic layer isn’t just a technical challenge; it’s a strategic shift. It requires investment in data modeling, metadata management, and possibly new storage technologies suited for semantic information.

SaaS is not dead, but it must evolve

SaaS isn’t going away, but its role is changing. SaaS providers must shift to standardize data and expose it semantically through APIs so AI systems can consume and reason with it. To stay in the AI value chain, SaaS providers must define robust data schemas and APIs, ideally aligning to emerging standards. This makes it easier for organizations to use their SaaS data in AI workflows and training pipelines.

We’re also beginning to see early standards, like MCP (Model Context Protocol), introduced by Anthropic, which is an emerging protocol designed to help AI models reliably access external data sources. It offers a client-server architecture for standardizing tool and data interactions, aiming to replace fragmented, one-off integrations. However, it’s still early, with limited industry support.

While current MCP consumers focus on tool integration, what’s still missing is a universal protocol for feeding semantic data to LLMs. This would define not just access but the format and structure of meaningful data. Such a protocol could unlock a new wave of AI-powered systems with deeper reasoning and integration capabilities.

Making Your Org AI-Friendly

If you want to embed AI into your workflows, start by reimagining your organization’s data model:

• From scattered documents to centralized knowledge graphs.
• From static dashboards to explorable stories.
• From human-readable formats to machine-readable semantics.

Ask yourself:

• What data do we have?
• Where is it stored?
• Is it structured or semantic?
• Can an AI access it and reason from it?

The answers will guide your transformation.

Addressing the fundamental challenges of data fragmentation and ensuring AI models have access to rich and relevant context are important. As SaaS providers need to evolve to meet the demands of this AI-driven era by standardizing their data and offering seamless integration capabilities, the emergence of standardized protocols like the Model Context Protocol offers a promising path toward universal data accessibility for AI.

AI’s usefulness is directly tied to the clarity and richness of the input it receives. The more structured, semantic, and contextual the data, the smarter your AI becomes. Embedding AI into workflows isn’t just about tools or APIs; it’s about the organizational intent to make data understandable, not just available.

If AI is already part of your stack or on your roadmap, the question is no longer whether you’re using it but whether you’re giving it the foundation it needs to succeed. Is your organization truly ready?