Use Cases
This page is for business leaders who want to understand what’s possible when it comes to getting answers from the organization’s technology architecture: without needing to be an architect, without waiting for a report, and without learning specialized software. The short version: it’s now possible to ask plain-language questions about your organization’s technology and get real answers from live data. Here’s how.
Your organization has an architecture team. They know a great deal about which technology systems the business runs, how those systems connect, what’s aging out, and what the risks are. That knowledge is recorded in a specialized tool: something like a detailed technical map of your technology landscape.
The problem is that the tool was built for architects, not for business leaders. When you need to know which systems support a key business process, or which technology is becoming a risk in the next 18 months, you have two options: schedule a meeting with an architect and wait for a prepared answer, or receive a slide deck with diagrams you weren’t trained to read.
Neither of these gets you what you actually need: a fast, reliable answer when you’re making a decision.
The good news is this has changed. The same technology advances that put a capable AI assistant in your phone have made it possible to connect your organization’s architecture data to tools you already use, so you can ask questions and get answers directly: without going through an intermediary.
There are four ways business leaders can access architecture intelligence today, depending on what tools your organization already uses.
If your organization uses Power BI or Tableau for business reporting, architecture data can be added as a live data source. The same dashboard environment where you look at financial results and operational metrics can include a portfolio health view: showing which technology systems are healthy, which are aging out, and which are critical to the business.
The key word is “live.” When the architecture team updates the technology map, your dashboard updates automatically. You’re not looking at data from last quarter’s report; you’re looking at the current state.
This is the simplest option to use and requires no new tools. If your organization already has Power BI or Tableau, architecture dashboards can be added to what already exists.
If your organization uses Microsoft 365 and Microsoft Copilot, you can ask architecture questions the same way you’d ask Copilot anything else: in plain language, in Teams, in the flow of work. Ask “which applications supporting our customer service process are flagged as high risk?” and receive an answer synthesized from live architecture data.
For organizations in the Salesforce ecosystem, the same capability exists through Salesforce Agentforce.
This option is powerful because it requires no new behavior. Business leaders who are already asking Copilot questions about documents, emails, and meeting summaries can start asking architecture questions the same way. The architecture data is just another source Copilot draws from.
Kernaro AI Hub is a browser-based tool built specifically for making architecture data accessible to people who aren’t architects. You open a browser, type a question in plain language, and receive an answer drawn from your organization’s current architecture data.
No installation. No EA license. No training in how to read architecture diagrams. You type your question and get an answer.
This is a good option when your organization isn’t heavily invested in a single AI assistant (like Microsoft Copilot), or when you want architecture intelligence available to a broad audience across different technology environments.
Prolaborate is a browser-based viewer for architecture content. Instead of asking questions, you browse: you can look at views of the application landscape, see which systems support which business areas, and navigate architecture documentation in a structured format.
This is lower-cost than the AI query options and works well for teams that need to reference and explore architecture documentation rather than ask specific questions. Think of it as a read-only window into the architecture team’s working environment, organized for non-architects.
Here are ten questions a business leader might realistically ask of an AI-connected architecture tool. These are the kinds of questions that today require a meeting with an architect: and tomorrow don’t have to.
The answers to these questions are already in your organization’s architecture data. The question is whether they’re accessible when you need them, or whether they require a meeting and a week’s wait.
The quality of answers from any of these tools depends entirely on the quality of the underlying data: the technology map your architecture team maintains.
Think of it this way: if the map is accurate and up to date, the answers are accurate and up to date. If the map has gaps, the answers will have gaps. If the map is stale, the answers will be stale.
The work that keeps architecture data trustworthy is called governance: it’s the process your architecture team uses to make sure the data stays consistent, that someone owns every entry, and that the map gets updated when things change. Organizations that have invested in this process get reliable answers from the AI tools described above. Organizations that haven’t yet will get unreliable answers: not because the tools don’t work, but because the data going in isn’t ready.
Before your organization deploys any of these stakeholder access tools, the architecture team needs to assess whether the underlying data is in a condition to support them. That assessment is the starting point: and it’s part of what the Discover service is designed to do.
Do I need to learn architecture notation to use these tools?
No. The access tools described on this page: dashboards, Copilot, Kernaro AI Hub, Prolaborate: are all designed for people who are not architects. You ask questions in plain language or look at organized views; the tool handles the translation from architecture data to understandable answers. You don’t need to know what “ArchiMate” is, what a “Block Definition Diagram” looks like, or how to read any specialized notation.
How current is the architecture data?
This depends on how your architecture team manages the data. When the architecture repository is connected to dashboards and AI tools via live data integration, changes the architecture team makes are reflected immediately or on a short refresh cycle: typically within minutes to hours, not weeks. The freshness of the data is a function of how often the architecture team updates the repository, not a limitation of the access tools themselves.
Can I trust AI answers about our architecture?
You can trust them to the extent that the underlying architecture data is trustworthy and current. If your architecture team has a well-maintained, consistently updated repository, AI answers drawn from it will be accurate. If the repository has gaps or outdated entries, AI answers will reflect those gaps. The honest answer is: ask your architecture team whether the data is ready to support AI-powered stakeholder access. If they say yes, the answers will be reliable. If they’re not sure, the Discover service is designed to find out.
What does our IT team need to set up before I can access this?
Three things need to be in place: the architecture data needs to meet a quality threshold (governed, consistently maintained); a data integration layer called EA GraphLink needs to be deployed to connect the repository to the access tools; and the specific access tool (Power BI, Copilot, Kernaro AI Hub, or Prolaborate) needs to be integrated and configured. The Connect service handles the integration and configuration; the Deploy service addresses data quality if needed first.
How is this different from asking the architecture team directly?
Three differences: speed, availability, and scope. Speed: you get an answer in seconds rather than days. Availability: you can ask at any time, including in the middle of a meeting or a decision conversation, without scheduling. Scope: AI tools can traverse the entire architecture dataset simultaneously, surfacing connections and relationships that a human would take hours to trace manually. The architecture team remains valuable for interpretation, judgment, and complex analysis: but routine queries and data lookups no longer require their direct involvement.
What does it cost to set this up?
The integration work that connects architecture data to stakeholder access tools is covered by the Connect service, which ranges from $50K to $185K+ depending on how many access paths are deployed and how complex the integration is. Organizations that need data quality work before integration can expect an additional Deploy engagement ranging from $30K to $130K. A Discover assessment ($25K–$75K) is the right starting point if there’s uncertainty about which access path fits best or whether the data is ready.
Connect: deploys the integration that makes architecture data accessible to business stakeholders through the tools they already use. $50K–$185K+.
Discover: if you’re not sure which access path fits your organization or whether the architecture data is ready to support it. $25K–$75K.
Talk to a Sparx Services architect about where your organization is on the journey and what the next stage looks like.