More and more companies want to use AI-supported analytics directly in their existing dashboards, without having to build their own data science team or completely overhaul the architecture.
Software providers face a clear challenge:
How do I integrate modern AI analytics into my existing software – quickly, securely, and without changing the core of my app?
The good news: It's easier today than ever before. We show how straightforward AI analytics can be embedded – without months of development or complex re-architecture.
In this article, you'll find a practical overview of how to integrate AI analysis step by step into any dashboard or software – whether ERP, MES, CRM, portal, B2B SaaS, or startup.
Why integrate AI analytics into existing dashboards at all?
Customers today expect more than static KPIs. They want:
Answers to complex questions in seconds, not after ticket + BI sprint
Dashboards that remain flexible, rather than needing a developer every time
Suggestions, trends & anomalies, not just tables
Self-service, instead of always involving the BI team
Software providers, in turn, benefit because AI analytics:
significantly enhance their product
reduce support costs
create differentiation in the market
increase retention
open up additional revenue potential (e.g., premium analytics modules)
No wonder that AI analytics are becoming a standard feature – similar to reporting 10 years ago.
1. Architectural question: Do I need to overhaul my system? No.
Many teams initially have concerns:
“For AI, we have to build everything anew.”
“We need a data warehouse.”
“Our data isn’t perfect.”
In reality, it’s much simpler.
Modern solutions (e.g., Scavenger Inside) integrate via:
✔️ SQL databases
(Postgres, MySQL, MS SQL, Oracle, SAP HANA, etc.)
✔️ Data warehouses
(Snowflake, Redshift, BigQuery, Databricks)
✔️ API-based systems
(SAP, Salesforce, Microsoft Dynamics, Custom APIs)
✔️ CSV/Excel ingestion
(if no central system exists yet)
In some of our projects, we were able to connect the entire system without changing the architecture and automatically build a complete semantic layer.
So you don’t have to create any new infrastructure.
The AI sits like an intelligent layer over your existing system.
2. The most important step: A “Semantic Layer” that understands business logic
For an AI to provide real answers, it must understand the data – not just tables, but meaning. This includes:
How is revenue calculated?
What does the column zurt_22_A represent?
What does OEE mean?
How is uptime calculated?
Which join is valid?
The semantic layer:
recognizes entities, relationships, timelines, metrics
prevents incorrect join paths
defines synonyms (“downtime” = “unplanned stop”)
enables 100% transparent & reproducible results
helps avoid hallucinations
“An AI is only as good as its understanding of business logic.”
– Engineering Lead of one of our industrial clients
This is precisely why the semantic layer is today’s gold standard – and our data engineers help to build it.
3. Simple embedding: Here’s how AI lands directly in the dashboard
There are three common integration methods:
A) iframe: The fastest but least secure way to “AI inside”
Ideal for: SaaS, portals, and corporate dashboards that involve no security-sensitive data.
Advantages:
1–2 days integration
immediately functional
minimal development time
B) API / SDK: Seamless, native embedding
Ideal for: Software providers with their own frontend team or existing dashboards.
Advantages:
completely in their own look & feel
Components like “Question Bar”, charts, answer cards can be rendered natively
full UI control
C) Component embedding in context
For example:
“Ask about this” directly on the existing interface
AI cards next to existing KPI tiles
Recommendations
Interactive insights directly in the module
4. Security & Governance: A critical point
When AI runs directly in your dashboard, the following standards must be met:
SSO integration (Azure AD, Okta, Keycloak…)
Role & rights system
Tenant separation
Row-level security
PII masking
complete audit logging
data remains within the business system (no lock-in)
Especially important:
The AI must only see data that the user is already allowed to see.
In our projects, this is always essential – the integration must handle rights & tenant separation, as well as customer data separation.
5. Project duration: How quickly can I go live with AI analytics?
Modern projects proceed in 5 steps:
Define use case
Data integration & semantic layer (2 weeks)
Testing & domain fine-tuning (1 week)
Dashboard embedding (1 week)
Rollout & scaling (ongoing)
Total: 4 weeks until go-live
We use exactly this structure in our projects – with a very fast time-to-value.
6. The business case: Why AI analytics pay off quickly
Most providers report:
✔ Reduction of support tickets by 20–40 %
(Customers find answers themselves)
✔ Higher customer satisfaction
(Quick, precise answers)
✔ Higher retention & differentiation
(The product stands out significantly)
✔ New revenue potentials
(Premium analytics modules, add-ons)
7. Conclusion: AI analytics are no longer a “nice-to-have” – they are becoming the standard
Anyone building ERP, MES, CRM, or B2B software today will have to integrate AI analytics sooner or later.
The market is moving fast – and users expect the same convenience with data that they are used to from ChatGPT.
The good news:
You do not have to build a data science organization, host a complex AI stack, or create a new architecture.
With the right partner, AI can be integrated into any dashboard in a few weeks – cleanly, securely, and scalably.
If you want to integrate AI analytics into your software
We have now integrated over 10 companies that have embedded Scavenger Inside directly into their software – from ERP and MES providers to industrial portals or even startups.
If you're technical, you can check out our API documentation.
If you prefer to talk directly rather than read, you can book an appointment here.