Closing the SAP Data Gap: How CEOs Get Clear Business Insight
Sean Antonello
VP, Solution Delivery
Sean Antonello
VP, Solution DeliveryMay 1, 2026 | 4 Minute Read
Almost every CEO has some version of the same frustration. They ask a question about the business and what comes back is a meeting, a deck, or a request for two weeks to compile the information. The data exists. The problem is the layer between the question and the answer.
What Actually Happens When a CEO Asks a Question
The reality in most organizations is this: a CEO asks a question, perhaps about costing or margin. It gets routed to the right team, then to an individual. That individual pulls data sets into Excel, merges the data, builds a deck, and does some manual analysis to understand what’s causing the impact. Three days later, the CEO receives a report by email with some of the information they asked for. That’s today’s reality.
What’s Actually Broken
What’s broken is access to data. The data a CEO needs is rarely just SAP financial data from the ERP. It’s SAP financial data blended with operational data. Very often, those two data sets are siloed, which means someone is exporting data and manually trying to piece it together.
Once the data is assembled, the work is to build a story around what’s driving a change or an anomaly, which is not easy to detect by human nature alone. Organizations have institutional knowledge and preconceived notions about what’s driving a result. But the fundamental question is whether the data actually supports that. Often, no one knows for certain.
What It Takes to Get a Real Answer in Plain Language
For a CEO to ask a question in plain language and get an answer grounded in real SAP data, the data has to be trusted, accessible, and unified. If SAP data and non-SAP data are not unified in a structured way with the right semantics, AI is not going to return an honest result. Without that foundation, you get hallucinations or irrelevant information.
The answer is staging the data and joining it using Business Data Cloud. That makes the data available for AI to query against a specific, structured data set rather than wide-open, unstructured data.
Where AI Actually Fits, and Why This Is Different
There is still a lot of smoke and mirrors on the AI side, especially when it comes to integrations into core applications. AI has a time and place, and what makes the SAP approach different is that Joule, SAP’s AI tool, is trained to point to the data set that is logical and makes sense. It is built for natural language querying against a verifiable, trusted data set. In the SAP world, using Business Data Cloud and Joule together, automation is already established through the semantic layer. That changes what’s actually achievable.
A practical example: a CEO is looking at the P&L and sees a drop in margin for a specific business unit. They can type a question in natural language asking for margin for the past eight quarters for that business unit, hit enter, and get back a visualized chart. From there they can ask follow-up questions and drill deeper into the data. They may still go to the business unit team for further analysis, but the AI narrows the scope of what they’re actually looking for before that conversation even starts.
AI Isn’t the Point. Trusted Data Is.
AI gets a lot of attention, but it is not the starting point. Trusted data is. The real goal is building data products inside the organization that everyone, from the analyst to the CEO, can work from with confidence. Most organizations are not there yet. Different teams are working from different versions of reality, each with their own spreadsheets and their own offline numbers. Getting everyone onto the same trusted dataset is what changes how an organization operates. AI can accelerate that, but it cannot replace it. It all comes back to the data..
How to Start Realistically
The right starting point is smaller than most people think. Pick one use case that matters, something where better data would change a decision. Build the dataset around that use case, structure it cleanly, and get it into the hands of the people who need it. Once the data is trusted and the model is working, that is the right moment to layer in AI and test whether it adds value. If it does, you have your proof point and a foundation to build from. If it does not, you have learned something cheaply. The goal is not to boil the ocean. It is to find the value, prove it, and then expand.
Ready to Close the Gap?
Improving helps organizations build the trusted, unified data foundation that makes real business intelligence possible for CEOs and the teams that support them. If your leadership team is still waiting days for answers that should take minutes, let’s talk about where to start.
