Companies running AI agents already report more trouble with data silos than companies without them, according to MuleSoft’s 2025 Connectivity Benchmark. This post looks at how team-by-team AI adoption is repeating the mistakes of the SaaS era, and why ownership of AI strategy belongs at the organizational level.
How do companies end up with dozens or even hundreds of applications that don’t talk to each other? For most enterprises, it happened one reasonable purchase at a time. A sales director found a pipeline tool she liked and put it on a corporate card. Marketing signed with an automation platform a few months later, and finance chose its own planning app the next year. Each team solved its own problem, and nobody owned the question of how the pieces fit together.
Too many companies are now repeating that pattern with AI, and the consequences are even more daunting. Teams are choosing their own assistants and building agents on whatever slice of company data they can reach. If AI stays a team-by-team decision, it will reinforce the data silos that enterprises spent the last decade paying to tear down. Ownership of AI has to sit at the organizational level.
What SaaS sprawl has left behind
MuleSoft’s 2025 Connectivity Benchmark Report, based on a survey of 1,050 enterprise IT leaders conducted with Vanson Bourne and Deloitte Digital, found that the average organization runs 897 applications and only 29% of them are connected. Ninety percent of respondents said data silos create business challenges. Zylo’s 2026 SaaS Management Index found that business units control 81% of SaaS spend, while IT directly manages just 15%.
In practice, the same customer likely shows up under different IDs in the CRM, the billing system, and the support desk. Churn signals in one platform fail to reach the teams that can address them. Analysts spend the first week of every quarter reconciling numbers that should match. Integration projects consume IT budget that could have gone to new capabilities.
AI is moving even faster
AI adoption is following the same route, only faster. Microsoft and LinkedIn’s 2024 Work Trend Index, a survey of 31,000 people in 31 countries, found that 75% of knowledge workers use AI at work and 78% of those users bring their own AI tools. Among leaders, 60% said their company lacks a vision and plan to implement AI. Zylo reports that ChatGPT is now the most expensed application in its data, which means AI is entering companies through expense reports instead of procurement.
The sanctioned side is fragmenting too. MuleSoft found organizations doubled the number of AI models they use, from 9 in 2024 to 18 in 2025. Notably, organizations running AI agents reported even more trouble with data silos than those without agents.
Siloed AI does more damage
A siloed SaaS application stores data in an inconvenient place. A siloed AI agent makes decisions from incomplete data, and it can make thousands of them before anyone checks the output. A demand forecasting agent that can’t see the sales pipeline will miss the forecast. A service agent that can’t see order history will give customers answers that contradict what the account team told them.
Team-built AI also bakes local definitions into its logic. When marketing and finance configure their own tools, they each decide what counts as an active customer or a qualified lead, and those definitions become invisible to everyone outside the team. Whatever one team’s agent learns about customers or operations stays with that team, so the next project starts from zero.
In a Gartner survey of 248 data management leaders, 63% said their organizations either lack or aren’t sure they have the right data management practices for AI. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that aren’t supported by AI-ready data.
Who should own AI
Organizational ownership doesn’t require central IT to approve every prompt. Business units understand their own problems, and they should keep choosing use cases and moving quickly. The organization needs to own the foundation those use cases sit on, which includes:
- A shared context layer that connects data across core systems, so every agent works from the same information
- Common definitions for the entities that matter most, such as customer, product, order, and margin
- One governance framework covering data access, security, and model evaluation
- A current inventory of the AI tools, models, and agents in use across the company
Employees without an approved option fill the gap with whatever tool they can find. A company that gives them approved, well-connected options early will see far less shadow AI than one that relies on policy memos.
Building the shared foundation
At RapidCanvas, we use a Hybrid Approach™, combining human experts with a proven agentic AI platform to deliver AI solutions that are customized to your goals, workflows, and tech stack. Our Enterprise Context Engine™ connects the systems a company already runs and gives every AI agent access to the same organizational knowledge. Each new deployment builds on the context from the ones before it, creating Compounding Intelligence.
Most enterprises spend years and a great deal of money reconnecting the systems their teams buy independently. AI gives them the chance to build the connections first, and the payoff grows with every agent they deploy.
If you’d like to discuss your AI opportunities with an expert, RapidCanvas would love to help. We can walk you through how we put a shared foundation under AI for every team in the enterprise. Contact us to schedule a time. You can also visit our website, browse dozens of case studies, and read verified customer reviews on G2.
