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RPA + Agentic AI

Digital Workforce

RPA + AI Agents to scale operations - governed, secure, and measurable.

Robotic Process Automation (RPA) integrates with your existing applications to automate repetitive work, increase throughput, and reduce human error. We combine industrial-grade RPA with governed agentic AI to support knowledge workers, accelerate execution, and keep automation audit-ready at enterprise scale.

  • Boost productivity by delegating repetitive work to always-on automation
  • Increase throughput with 24/7 execution in seconds or minutes
  • Reduce human error by automating data flows across systems
  • Accelerate time-to-value by building and deploying workflows fast
Book a 15-minute discovery call Request examples / use cases

IN SHORT

Achieve more in less time with fewer human resources

RPA is a system-agnostic business technology that mimics user actions across applications to increase process efficiency, employee satisfaction, and productivity without disrupting existing systems.

65%

Indicative cost savings and productivity gains from well-governed RPA programs.

50%

Reduction in manual labor effort on repetitive process-heavy tasks.

2x

Even conservative bot scenarios can execute significantly faster than manual handling.

24/7

Always-on execution with no breaks, supporting stable throughput and SLA performance.

RPA advantages

  • Rapid deployment and fast time-to-value
  • Minimal upfront investment
  • Improved process quality and compliance assurance
  • Elimination of repetitive human errors
  • No disruption to underlying enterprise systems
  • Better customer and employee experience
  • Scalable and enterprise-ready operating model
  • Rapid and measurable ROI potential

What exactly is RPA?

RPA software imitates how people work with files, systems, databases, email services, and websites. It removes swivel-chair work between multiple tools and standardizes repetitive execution.

Bots execute defined sequences, handle exceptions, and escalate decisions when human judgment is required. This enables reliable automation with minimal IT involvement and without hard integration.

Where RPA fits best

  • Legacy environments without APIs, virtual desktops (VDI), and database-driven routines.
  • Cross-system workflows that require data transfer between email, portals, files, and core applications.
  • Programs combined with OCR, ML, NLP, analytics, IoT, and blockchain components.

Shadow AI is an emerging operational and security threat

When knowledge workers adopt AI tools ad-hoc, sensitive data exposure and uncontrolled decision-making become real business risks. Evidence shows 38% of workers share sensitive work information with AI tools without employer permission, and 69% are unaware whether their company has a formal strategy for integrating generative AI.

38%

of workers share sensitive work information with AI tools without employer permission.

69%

are unaware whether their company has a formal strategy for generative AI adoption.

Be proactive: provide teams with governed and secure agentic AI, not unmanaged shadow usage.

Measurable uplift - when AI is implemented as a controlled capability

The next wave of value comes from moving beyond chat into agents that understand, plan, and execute work across tools and systems - with governance, security, and observability designed in.

25%

Decrease in time to complete tasks when using AI tools

12%

More tasks completed in total with the help of AI tools

40%

Higher quality in work completed with AI tools

Agentic shift

Generative AI is moving from chat support to autonomous agents that understand goals, plan steps, and execute work.

Digital Workers + Agentic AI, built for real operations

We combine execution automation with decision support and operational control, so teams get measurable outcomes instead of fragmented pilots.

RPA (Digital Workers)

RPA automates repetitive tasks across applications and systems, freeing teams to focus on higher-value work while improving accuracy and throughput without adding headcount.

AI Agents (Agentic AI)

AI agents are applications that can act autonomously to understand a goal, plan steps, and execute tasks by interfacing with tools, models, and enterprise systems.

Run-mode & Reliability

We design automation for production reality: monitoring, alerting, exception handling, traceability, and clear ownership - so performance is repeatable and maintainable.

Governance, security, and explainability - by design

Enterprise readiness is not a final add-on. We design controls from day one so agentic automation remains accountable, secure, and audit-ready.

Management & Governance

Lifecycle management, role model definition, policy setup, telemetry, monitoring, and actionable alerting for controlled operations at scale.

Security

Authentication across systems, secure data exchange between tools and services, and continuous monitoring for attacks and vulnerabilities.

Guardrails & Explainability

Human oversight at critical decisions, policy guardrails aligned with enterprise expectations, and explainable actions and decisions for trust and control.

Best fit: high-volume repeatable work - and knowledge tasks that need execution support

Digital workforce programs are most effective where process discipline, throughput pressure, and quality requirements need to be managed at the same time.

Customer Service

Operations

IT Management

HR Management

Financial Management

Risk & Compliance

Where to use: Finance, Logistics and Back Office

Best results come from high-volume, business-rules-driven, repeatable work. Below is a condensed map of practical use cases.

Finance and back-office examples

RPA bots support core finance and administration activities from data collection to reporting and reconciliation.

Accounting
  • Accounts Payable (AP) and Accounts Receivable (AR)
  • Processing bank statements and reconciling open items
  • Posting incoming and outgoing invoices
Taxes
  • Preparing data for tax returns
  • Verifying taxable vs non-taxable amounts and data quality
  • Checking contractor VAT EU and updating master data
Debt Collection
  • Sending reminders and dunning notes
  • Maintaining contractor communication workflows
  • Generating overdue and aging reports
Controlling
  • Preparing daily, weekly and monthly reporting inputs
  • Generating provisions and ratio analysis
  • Actual vs estimate comparison and budgeting data gathering
Statutory and Group Reporting
  • Consolidating financial statements
  • Preparing balance sheet, P&L and cash flow statements
  • Preparing statement of changes in equity
Procurement
  • Supporting purchasing activities (P2P)
  • Verifying internal order correctness and creating price lists
  • Checking supplier offers, invoices and notes against control procedures
Administration and HR
  • Fixed assets master data and usage verification
  • Candidate sourcing, employment history checks and onboarding
  • Payroll reports, earnings confirmations, benefits and absence reporting

Typical use cases in logistics operations

TSL organizations often run on many systems and partner portals. RPA connects them and removes manual swivel-chair work.

  • Shipment scheduling and tracking from pick-up request to status updates across internal and external systems.
  • Moving data from incoming emails to scheduling tools and customer portals.
  • Automated reporting: shipment status, inventory rotation, and storage occupancy.
  • Tender process support on customer transportation portals.
  • Invoicing with portal integrations, shipping data extraction, and POD attachment.
  • Automatic rate look-ups from carriers and 3PLs plus capture from load boards and emails.

The same automation patterns work beyond logistics in financial services, healthcare, manufacturing, retail and the public sector.

For supply-chain automation we use a heat-map approach to accelerate process identification and prioritization.

How to start (delivery model)

Our 7-year practice focuses on intelligent RPA for enterprise teams. We use BPMN process mapping (ISO-aligned) and a clear implementation methodology.

Process & Automation Assessment

We evaluate process frequency, handling time, steps, applications involved, FTE impact, variants, and process stability to prioritize ROI.

Design (controls included)

We design the target workflow with exception paths, access model, audit evidence, and operational KPIs for managed scale.

Build, Deploy, Run

We implement, integrate, instrument monitoring, and transition into run-mode with ownership and continuous optimization.

Process assessment criteria

  • Business pain points and reasons the process is problematic.
  • Process frequency, average handling time, and number of steps.
  • Applications and data sources used in the workflow.
  • FTE impact, number of users, and process variants.

Qualification and prioritization

  • Input is electronically readable (structured data).
  • Process and underlying applications are stable.
  • Business rules are repeatable and clear.
  • Candidates are compared and prioritized by expected ROI.

Discovery and BPMN process mapping

We run a high-level assessment with your team, map the current process state, and define automation candidates with measurable baseline metrics.

Prioritization and solution blueprint

We compare candidates, prioritize by ROI and risk, then design the target workflow, exception handling, controls, and evidence model.

Build, deploy, and scale

We implement end-to-end intelligent automation, integrate systems, activate monitoring, and move to run-mode with clear ownership and continuous improvement.

Build a governed Digital Workforce that scales

Move from isolated automation initiatives to a secure, governed, and measurable operating capability combining RPA and agentic AI.

Book a 15-minute discovery call Request examples / use cases