Applied AI & Analytics consulting From scattered data to sound decisions and automated processes in daily operations
ERP, line-of-business systems and spreadsheets rarely fit together. So month-end figures are consolidated by hand and every invoice is checked one by one. On site, we build the data foundation, the KPIs for recurring decisions and the automation of routine work. Your team decides on the exceptions.
Pillar 01 · Applied AI & Analytics
Trusted data first, then sounder decisions, and finally automated processes
In the Applied AI & Analytics pillar, DataClue brings together the offers that turn data from ERP, line-of-business systems and spreadsheets into decisions and workflows. They include the AI Value Scan as the entry point, Decision Intelligence for data-driven decisions and AI Workflow Deployment for AI-supported workflows. A trusted data foundation is part of every offer.
Layer 3
AI-supported workflows
We automate recurring tasks in the process, such as invoice checks, lot release or routing requests. Fixed rules handle the standard cases, AI the cases where rules are not enough. Exceptions reach your team with a reason, for human review and approval (human-in-the-loop). Every step is logged. You decide what may run automatically.
- AI agents
- Process automation
- AI document processing
Layer 2
Data-driven decisions
First we clarify which decisions come up regularly, for example on volumes, prices or priorities. Then we define which figures they rest on. That becomes KPIs, reports and forecasts, for instance in Power BI, that your team maintains itself. Your managers set the threshold at which someone acts.
- KPI systems and dashboards
- Forecasts and scenarios
- AI analysis in natural language
Layer 1Part of every engagement
Data foundation
We bring ERP, specialist systems and spreadsheets together in one data model from which every function reads the same figures. Together with you we set who maintains and approves which data. Reports and automation build on it.
- Data integration
- Data modeling
- Data quality and governance
How data and AI get into your operations
- AI potential analysisEntry pointThe AI Value Scan by DataClue shows where AI pays off in your business and where it does not. We review workflows, data and systems on site. Then we assess the use cases by business value, feasibility and effort. The result is an AI value map with three to five prioritized use cases and a clear recommendation for each one.
- Data-driven decisionsDecision Intelligence is the DataClue offer for business intelligence and analytics built around one concrete, recurring management decision. We combine trusted data, agreed KPI logic, analytics and thresholds into one decision system. It runs on your existing ERP, BI and spreadsheet landscape, for example Power BI. AI is added where it improves the decision.
- AI-supported workflowsAI Workflow Deployment by DataClue takes one clearly defined workflow, such as invoice processing, into productive use. Fixed rules handle the standard cases, AI the cases where rules are not enough. Exceptions and high-impact decisions go to your team with a reason, for human approval (human-in-the-loop). Every decision is logged.
How we work
Measurable impact in daily operations, with a fresh decision after every step
Every step delivers a concrete result. Stopping is a result too. You can also commission steps 01 and 02 on their own, as the AI Value Scan.
Step 01: Understand the challenge
What you walk away withA data map and prioritized use cases, assessed by value and effort.
- What we do
- On-site interviews and a review of ERP, line-of-business systems and file storage. We count which decisions come up how often and how long they take.
- What we agree on together
- Which areas we look at and who works with us on your side.
Step 02: Set target and success criteria
What you walk away withA signed agreement with target, measurement plan and business case. Plus a clear recommendation on whether implementation pays off.
- What we do
- We measure the starting point and propose a target and limits. Then we work out the business case.
- What we agree on together
- Target, stop criterion and the threshold above which a person decides. And on that basis, whether we go ahead with implementation.
Step 03: Implement step by step
What you walk away withA solution that runs on your systems in daily operations. Plus a team that works with it confidently.
- What we do
- We connect your systems, set up the check rules and build an interface for exceptions. Every decision is logged, and a manual fallback is in place.
- What we agree on together
- The check rules, and when they go live for the first cases.
Step 04: Measure and optimize impact
What you walk away withA measurement report against the agreed target, every rule change in the log.
- What we do
- Measurement, analysis of the exceptions, proposals for rule changes.
- What we agree on together
- What happens if the target is missed, up to and including a stop.
Step 05: Embed and scale the solution
What you walk away withYour team runs the solution on its own, with documentation and clear responsibilities.
- What we do
- Training, documentation, handover of the rules and the monitoring to your team.
- What we agree on together
- Acceptance of the handover. Separately, you decide whether the solution is extended to other areas.
Case studies
- 50%less monthly consolidation effortFull margin transparency across three sites and multiple ERP systems
- About 91%of bookings identical to the tax advisors’ final versionAI booking agent: documents coded, booked and handed over automatically every day
- 12KPIs in one cockpit for the entire site networkProgram cockpit: the status of all ramp-up programs and sites in one view
All client examples are anonymized.
Applied AI & Analytics
Frequently asked questions
Who takes care of data quality?
Decisions and automation are only as good as the data underneath. That is why every offer includes the data foundation the use case needs: connecting systems, modeling data, checking quality. A separate data project up front is not required.
Do we need a new data platform before we can use AI?
No. We work with your existing ERP, BI and spreadsheet landscape and fix the data issues that materially affect the result. We do not require a platform migration.
What is the difference between data-driven decisions and AI-supported workflows?
Data-driven decisions improve how people make recurring decisions, with trusted KPIs, analytics and thresholds. AI-supported workflows change how recurring work gets done: the normal case runs automatically, exceptions go to your team.
How do you make sure the AI does not decide anything without approval?
Only what you have approved in writing runs automatically, with approval limits and stop criteria. Everything else goes to the people responsible, with all the data. Every system decision is logged and traceable.
Contact
Where should AI make a difference first?
In the initial call, describe a workflow or a decision. We tell you whether AI pays off there.
