PROCESS AUTOMATION
Automate the work between systems, without losing control.
We identify repetitive cross-system work, redesign the workflow, and implement automation with clear exception handling, auditability and human approval where judgment still matters.
Manual and fragmented → Controlled and automated
Does this sound familiar?
If several of these are already true, this is the right conversation. If none of them are, it probably is not.
- Teams re-key the same information across multiple systems.
- High-volume work depends on email, spreadsheets and manual handoffs.
- Exceptions consume more time than the happy path.
- Existing bots break when screens, fields or upstream processes change.
- Automation ownership is unclear once the project team leaves.
- AI is being proposed before the workflow, data and approval boundaries are understood.
What it costs you
- Fragmented workflow across systems that do not agree
- Repetitive manual handoffs, held together by email and spreadsheets
- Slow cycle time and inconsistent execution, with no measurement of either
- Hidden exceptions and control gaps that only surface during an audit
- Automation that is difficult to operate or trust, so the manual work returns
Most automation that disappoints was pointed at the wrong thing. If exceptions come from inconsistent customer or supplier data, automating the workflow moves the work rather than removing it — the ambiguity has to be resolved upstream first.
What materially changes
| Today | With PaWa |
|---|---|
| Manual re-keying | API, workflow and event automation where interfaces are stable |
| Email-driven handoffs | Explicit orchestration with named ownership |
| Happy-path-only bots | Designed exception and recovery paths |
| Opaque automation | Audit trail, monitoring and accountability |
| Technology-first RPA | Mechanism selected after process analysis |
| Unbounded AI actions | Governed agent and tool permissions with human approval |
Today
Manual re-keying
With PaWa
API, workflow and event automation where interfaces are stable
Today
Email-driven handoffs
With PaWa
Explicit orchestration with named ownership
Today
Happy-path-only bots
With PaWa
Designed exception and recovery paths
Today
Opaque automation
With PaWa
Audit trail, monitoring and accountability
Today
Technology-first RPA
With PaWa
Mechanism selected after process analysis
Today
Unbounded AI actions
With PaWa
Governed agent and tool permissions with human approval
How we solve it
Mechanisms, not adjectives. Each of these is something we build, document and hand over.
Process discovery and opportunity scoring
The actual path including exceptions, scored by volume, friction, feasibility and control risk — so the first thing automated is the one consuming your people's week, not the one that demos best.
Workflow redesign before automation
Automating a process nobody has questioned encodes its worst habits at speed. The redesign comes first, and sometimes it removes the step rather than automating it.
API and event integration
Where stable interfaces exist, integrate through them. This is the most durable mechanism available and the first one we look for.
Workflow and BPM orchestration
Multi-step business processes with state, retries and idempotency, so a partial failure resumes rather than duplicating work.
RPA where it is genuinely appropriate
UI automation is the right answer when a system has no API and no realistic path to one. It is a legitimate mechanism and a poor default: bots break when screens change, which is why we place them deliberately rather than by habit.
Process mining
Where event data exists, it exposes real bottlenecks and process variants — usually disagreeing with what everyone believed the process was.
AI-assisted classification and extraction
Models where genuine judgment on unstructured input is needed: reading a document, classifying free text. Where the logic can be written down, a rule is cheaper, faster and explainable.
Human-in-the-loop and exception handling
Approval queues, escalation and override boundaries, written down before build. A process without an explicit boundary escalates anyway, informally, to whoever notices.
Monitoring, audit and runbooks
A record of what ran, what it decided and what a person overrode, plus throughput and exception-rate monitoring so drift is visible before the manual work returns.
Reference architecture
Systems and events trigger an integration layer, then a workflow orchestrator that holds process state so a failure resumes rather than restarting or duplicating. Rules and AI services handle the decidable cases — rules where the logic can be written down, models only where judgment on unstructured input is genuinely required. Anything outside those boundaries reaches human review and exception handling, defined before build rather than discovered after an incident, and the outcome is written back to the systems of record. Identity and access, governance, audit trail, observability and operational ownership span the whole flow. Where the process turns on who or what a record refers to, mastered entities from the Governance & MDM practice remove the ambiguity upstream instead of asking the workflow to guess.
1Systems & events
- ERP and CRM
- Email and documents
- Event triggers
- Schedules
2Integration / APIs
- APIs
- Event streams
- Webhooks
- File transfer
3Workflow orchestration
- Process state
- Retries and idempotency
- Routing
- SLAs
4Rules / AI services
- Deterministic rules
- Classification
- Extraction
- Decision support
5Human review & exceptions
- Approval queues
- Escalation boundary
- Overrides
6Systems of record / actions
- Write-back
- Downstream triggers
- Notifications
Across the whole flow
Identity and access · Governance and mastered entities · Audit trail · Observability · Operational ownership
What you receive
Written artifacts you keep and can act on with any firm, including without us.
- Automation opportunity inventory and prioritisation matrix, including the candidates we advise against
- Current-state process and exception map, with the origin of each exception named
- Target workflow and control design
- Automation components within the agreed scope
- Human approval and exception model, written before build
- Monitoring, audit logging and operational runbooks
- Ownership and RACI, plus change-management guidance
- Backlog for the subsequent automation opportunities
How an engagement runs
- 1
Discover
Map the process as it runs, exceptions included, and measure where the time actually goes. Perception and measurement usually disagree about which step is the problem.
- 2
Design
Decide what to automate, what to fix upstream and what to leave deliberately manual — then choose the mechanism. Mechanism last is the whole discipline.
- 3
Deliver
Build the orchestration, rules and review boundaries, running alongside the manual process until the exception rate is understood rather than assumed.
- 4
Enable
Hand over runbooks, monitoring and the rule-change process, so a new exception type becomes a rule your team adds rather than a ticket to us.
Relevant experience
Each item below is labelled with what kind of evidence it is. Nothing here claims a client outcome we cannot support.
Representative pattern: the exception queue
A cross-system process where the happy path is already automated and skilled people spend their week on exceptions. The work maps where exceptions actually originate — usually upstream data rather than the process itself — and if that is the finding, automating harder makes it worse. Mechanism selection comes after that answer, not before.
What the engagement leaves behind: a process and exception map, orchestration for the repeatable cases, a written human review boundary, and monitoring on exception rate.
Representative engagement — a realistic pattern used to explain our approach, not a client result.
Manual reconciliation as a symptom, not a task
A Tier 1 North American bank where investigators reconciled customer records by hand during investigations. The reconciliation looked like a process to automate; it was actually entity resolution surfacing as manual work. Fixing the matching removed the task rather than accelerating it, which is the distinction this practice exists to make.
Delivered by our principal in a previous role, before PaWa Data Solutions.
Technology experience
Workflow and BPM
- Camunda
- Temporal
- Azure Logic Apps
- Power Automate
Orchestration
- Airflow
- Event-driven orchestration
RPA
- UiPath
- Power Automate Desktop
- Automation Anywhere
Process mining
- Celonis
- Event-log analysis
Document and extraction
- Azure Document Intelligence
- AWS Textract
- OCR pipelines
Monitoring and audit
- Structured logging
- Process metrics
- Alerting
Platforms and tools we have worked with directly. This is experience, not a partnership claim: PaWa Data Solutions holds no reseller agreement or partner status with any vendor listed here, which is what keeps the recommendation neutral.
Papa S. Nguer
VP of Technical Sales & Engineering, PaWa Data Solutions
Automation work is led by our principal. The most useful thing we bring here is the discipline to say when a process problem is really a data problem — a judgment built on entity resolution and MDM work for Tier 1 institutions, and one that saves clients from automating the wrong thing.
Full profileQuestions buyers actually ask
Where should we start?
With the process consuming skilled people's time, not the one that is easiest to automate. Those are rarely the same, and starting with the easy one produces a demo rather than a saving.
What if the exceptions come from bad data?
Then we will say so, and automating the workflow would be the wrong engagement. Ambiguous customer, product or supplier identity is the most common root cause we find, and it is resolved upstream through mastered entities rather than inside the process.
Is RPA still relevant, or is it obsolete?
It is relevant where a system has no API and no realistic path to one, which is a real and common situation. It is a poor default because bots break when screens change. We select the mechanism after the process analysis, so RPA gets used deliberately rather than by habit.
Should we use AI for this?
Only where genuine judgment on unstructured input is needed — reading a document, classifying free text. Where the logic can be written down, a rule is cheaper, faster, explainable and does not drift. Reaching for a model to avoid writing a rule is how automation becomes unexplainable.
How do you govern automation that takes actions?
Permitted tools and actions, human approval boundaries, evidence and monitoring — the same controls the AI Readiness page describes, applied to a workflow. An agent or bot that can act is a higher risk tier by default, and the boundary is written before build.
Will this replace jobs?
The work we do targets re-keying and repeated exception handling, which is usually the part of a role people are most relieved to lose. We are not the right firm for a headcount-reduction mandate, and it is fairer to say that at the outset than to discover it mid-engagement.
Can you tell us the ROI up front?
Not honestly, and we will not publish a percentage we cannot support. The assessment establishes a measured baseline — volume, cycle time, exception rate — which is what any credible figure has to be calculated from afterwards.
Automation Opportunity Assessment
Identify and rank candidate workflows by volume, friction, feasibility, control risk and expected operational value. Includes the candidates we would advise against, and the measured baseline any future ROI claim would have to be calculated from.
Scope and commercial terms are agreed in writing before the assessment starts.