What is (intelligent) process automation?
A business process is a sequence of work steps with a fixed goal: answering an inquiry, preparing a quote, booking an invoice. Process automation lets software carry out these steps, the same way every time and at any hour.
Traditional automation hits a wall as soon as input isn't neatly structured. A tender as an 80-page PDF, a customer email with three requests, a scanned delivery note: until now, a person had to read and retype all of it.
Intelligent process automation (IPA) closes that gap. A language model reads the document, pulls out the relevant details and hands them to the next system in structured form. The process keeps moving without anyone copying and pasting.
RPA vs. AI process automation: the difference
Robotic process automation (RPA) imitates a person at a screen. A software robot clicks into fields, copies values and fills in forms. That works as long as nothing changes. If an update moves an input field, the robot stops and someone has to readjust it.
AI-native process automation works differently:
| Feature | RPA | AI process automation |
|---|---|---|
| How it works | clicks through user interfaces | talks to systems through interfaces (APIs) |
| Input | structured only (fields, tables) | also free text, PDFs, scans, emails |
| When the interface changes | breaks | keeps running as long as the API stays the same |
| Decisions | fixed if-then rules | rules plus classification by the language model, critical steps with human approval |
| Maintenance | high with frequent software updates | low, changes in one place |
RPA still makes sense for legacy systems without an interface. Wherever there is an API, it is the more stable route.
AI agents go one step further: software that handles a task across several steps on its own, for example reading a request, spotting missing details, pulling data from the ERP and drafting a quote. Our rule applies there too: where judgment is needed, a person approves before anything goes out.
Which business processes can be automated?
Good candidates are workflows that happen often, follow clear rules and currently involve a lot of reading or retyping. Many of them start with intelligent document processing (IDP): a language model reads tenders, invoices, delivery notes and emails and passes the details to your systems in structured form. Examples from companies with large orders:
- Reviewing tenders and inquiries, extracting requirements and matching them against your own portfolio
- Preparing quotes from costing data, text modules and past projects
- Capturing and checking incoming invoices and receipts and handing them over for booking
- Keeping order data in sync between CRM, ERP and project management
- Supplier communication: requesting delivery dates, reporting deviations
- Summarizing service reports and minutes and filing them with the right case
- Creating recurring reports from multiple sources
Less suitable are decisions with a lot of discretion. There, automation prepares the groundwork and a person decides.
Sales workflows can be automated too, for example responding instantly to new inquiries. One example is speed to lead.
How an AI process automation project runs at titanspear.ai
- Map the process: We walk through the workflow with the people who handle it today. Where does data come from, where does it go, where does it wait?
- Define the target state: Which steps does the software take over, and where does your team keep an approval step? The result is a concept with effort and expected benefit.
- Connect and build: The automation is connected to your existing systems through interfaces (APIs) and runs on our own automation platform.
- Pilot with real data: One process goes live, at first with every result checked.
- Expand and support: Once the pilot runs reliably, more processes follow. We monitor the workflows and adjust them when systems or rules change.
You have one dedicated contact throughout. Our client Alex from AE Productions puts it this way: “I always have someone to talk to when I have questions.”
Benefits: efficiency, fewer errors, less workload
Efficiency: An automated step doesn't wait for vacation cover or the next business day. Cases that used to sit in the inbox for days are handled as soon as they arrive.
Fewer errors: Retyping creates typos, and copying between systems creates version chaos. When data is captured once and passed on through interfaces, it matches in every system. Unclear cases land with a person as an exception instead of quietly running on with the wrong data.
Less workload: Your engineers, estimators and clerks are expensive and hard to find. Every hour they don't spend searching and copying is available for work that needs their expertise.
“We've been working with titanspear for six months now, and we've been able to automate a lot of processes.”
If the bottleneck isn't retyping but searching: RAG for business.
GDPR & data protection in automated processes
Automated processes often handle personal data: contacts, customer data, employee details. That is why data protection belongs in the planning, not at the end.
- Data minimization: The language model only receives the details it needs for the step at hand.
- No automated individual decisions about people: Art. 22 GDPR restricts decisions that are made solely by automated means and significantly affect people. We build such steps with human approval.
- Logging: Every run is traceable, from input to result.
- Contract: We sign a data processing agreement with you under Art. 28 GDPR.
To learn how to run language models entirely under your own control, see Corporate LLM. Which data your employees may put into which AI tool is set out in an AI policy; our AI policy template is free.
Costs & ROI
We don't quote fixed prices because the effort depends heavily on the process. Three factors drive it:
- the number and type of systems to connect, and whether they have interfaces
- the quality of the input data, such as clean forms or scanned legacy records
- the number of exceptions and approval steps in the process
The ROI can be roughly calculated up front: time per case times cases per month times your internal hourly rate, compared with the effort to build and run the automation. We do this calculation in the concept with your numbers, before you commit. If it doesn't pay off, we tell you.