AI and RPA: Intelligent Automation Services

Intelligent automation combines robotic process automation (RPA), artificial intelligence and integration to take repetitive work off your team: re-keying data between systems, reading documents, reconciling records, onboarding people, producing reports. PS3G provides intelligent automation services for organizations of every size, from an owner whose bookkeeper spends Fridays copying invoices into accounting software, to an operations lead at a growing company with a shared inbox nobody can keep up with, to a shared-services department inside a larger organization that runs hundreds of bots and needs them governed. We assess the process, pick the right tool for each step, prove the return on a small scope, and scale only what the numbers support.

Intelligent automation that pays for itself

Business process automation is worth doing when a task is frequent, rule-bound or document-heavy, and when the hours it consumes or the errors it causes can be measured. We start from that measurement: the current time per item, the volume, the error rate and the cost of rework. The automation is designed to beat those numbers, and the proof of concept is judged against them, not against a demo.

The work is scoped to fit. A small company might automate one process end to end, such as invoice capture into its accounting system, on Power Automate licenses it already owns. A growing company usually needs a prioritized backlog across finance, HR and operations with a shared platform and controls. A larger organization needs a center of excellence: standards, a bot inventory, exception handling and a governance model. Our process automation consulting adapts to each of these without changing the method.

RPA vs AI vs AI agents: which fits which task

Choosing the wrong tool is the most common reason automation stalls. RPA is reliable and inexpensive for fixed rules on structured screens and files. AI handles variation: messy documents, free-text email, judgment calls with clear criteria. AI agents can plan and act across systems but need guardrails and a person in the loop. Most real processes combine all three.

Approach	Good at	Weak at	Example step
RPA	Fixed rules, structured data, legacy screens without APIs, scheduled batches	Unstructured input, changing screen layouts, exceptions	Posting approved invoices into the ERP
AI (models)	Reading documents and email, classifying, extracting fields, drafting, scoring	Exact arithmetic, guaranteed consistency, acting without a wrapper	Extracting line items from a supplier invoice
AI agents	Multi-step tasks across systems with decisions along the way	Unbounded authority, undocumented processes	Resolving a mismatched purchase order by checking three systems and asking the buyer
Integration and workflow	Connecting systems through APIs and events; routing, approvals, notifications	Systems without interfaces (where RPA fills the gap)	Triggering onboarding tasks when HR creates a new hire

Our comparison guide, RPA vs AI automation vs AI agents, goes through the decision step by step. Agent builds are delivered through our AI agents and chatbot development service.

What we automate

• Finance. Accounts-payable capture and three-way matching, expense checks, bank and card reconciliation, collections reminders, month-end report packs, and audit evidence gathering.
• HR onboarding and administration. Account creation, equipment requests, policy acknowledgments, benefits enrollment data entry, and offboarding checklists triggered from your HR system.
• Order entry and customer operations. Orders arriving by email, PDF or portal entered into the ERP, status updates sent back, and exceptions routed with the reason attached. Work inside SAP or Microsoft Dynamics draws on our enterprise resource planning practice.
• Document processing. Intelligent document processing for invoices, applications, claims, contracts and forms: OCR and layout models plus language models with structured outputs, confidence scoring, and a review queue for low-confidence fields.
• Reporting. Data pulled from several systems on a schedule, checked, formatted and distributed, with the numbers traceable to their source.
• IT operations. User provisioning, access reviews, ticket enrichment and routine maintenance tasks run through approved tools.

For a department-by-department view, see AI workflow automation examples.

Our roadmap: assess, prioritize, prove, scale

We first published this approach as a 180-day transformation roadmap: engage, assess, prove with a proof of concept, plan adoption, then scale. The sequence still holds; the calendar depends on your processes, your systems and how quickly decisions get made, so we agree it per engagement rather than promising dates.

1. Assess. Process walkthroughs with the people doing the work, volumes and timings from your systems, and a map of each step to RPA, AI, agent or integration. Process-mining data from tools such as UiPath Process Mining or Power Automate Process Mining is used where it exists.
2. Prioritize. Each candidate is scored on hours saved, error cost, volume, stability of the process, integration effort and risk. The top candidates get a one-page charter with a baseline and a target.
3. Prove with ROI. A proof of concept on real data and a limited scope, measured against the baseline: time per item, straight-through rate, exception rate, cost per item. It ends in a written decision to scale, adjust or stop.
4. Scale. Production deployment with monitoring, exception queues, a change process and training for the owning team, then the next process on the backlog.

Ask for a fixed-scope automation assessment of one process you want off your team’s desk.

Platforms

Platform	Where it fits	Notes
UiPath	Organizations standardizing on one RPA platform at scale	Studio, Orchestrator, Document Understanding, Test Suite and agent tooling; mature governance features
Automation Anywhere	Cloud-native RPA with document automation and AI agent tooling	Automation 360 with Document Automation and AI Agent Studio; strong for finance and shared services
Microsoft Power Automate	Organizations on Microsoft 365 and Dynamics that want cloud flows, desktop RPA and AI Builder on licenses they may already own	Hundreds of connectors, Copilot-assisted flow building, Process Mining; see Microsoft’s Power Automate documentation
SS&C Blue Prism	Regulated environments that need strong control over digital workers	Decipher for document processing; established audit and security model
LLM-based automation	Steps that involve reading, classifying, extracting or drafting	OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude or open-weight models, wrapped with structured outputs, validation and review queues
Intelligent document processing	High-volume forms, invoices and correspondence	Azure AI Document Intelligence, Google Document AI, Amazon Textract, or the IDP module of your RPA platform

We are vendor-neutral. The recommendation depends on what you already license, where your systems run, who will maintain the automations, and how much AI the process needs. Where a process can be automated through an API instead of a screen, we prefer it, and our API development and integration team builds the interface; screen-based RPA is reserved for systems that offer nothing better.

Governance and controls for bots

Bots are users with no judgment, so they need tighter controls than people. Our RPA services and AI automation services include the controls from the start:

• A named owner and a documented purpose for every bot and model, kept in an automation inventory.
• Dedicated service identities with least-privilege access and secrets held in a vault, never shared logins or passwords in scripts.
• Separation of duties: a bot that enters a payment does not approve it.
• Exception queues with a person accountable for clearing them, and confidence thresholds for every AI decision.
• Logging of every action and every AI output, retained according to your policy, so any transaction can be traced.
• Change control with testing before release, versioning, a rollback path and a kill switch.
• Monitoring for failure rates, run times, drift in AI accuracy and cost per item.

Where AI makes decisions inside the workflow, those controls are mapped to your policy through our AI governance service, aligned with the NIST AI Risk Management Framework.

Engagement models

• Fixed-scope project. An automation assessment, a proof of concept with ROI measurement, or one process automated end to end, with a fixed-scope quote.
• Dedicated team or staff augmentation. RPA developers, AI engineers and a business analyst working through your backlog month by month.
• Managed services. We run your bots and AI workflows under defined SLAs: monitoring, exception handling, platform upgrades and change requests, from our US and India teams.
• Advisory. RPA consulting and process automation consulting: platform selection, center-of-excellence design, and governance for an automation program you run yourself.

How we start: a first conversation, a short assessment, and a written proposal.

Why PS3G

• We build and run our own software products, including our HIPAA-compliant EHR and telehealth platform, DocVilla, in which we ship AI features under regulatory constraints, so controls and audit trails are part of how we build.
• Over a decade of software development and IT services, with a US headquarters in Wilmington, Delaware and a delivery center in India for round-the-clock bot operations.
• Microsoft-certified and open-source-certified engineers, so Power Platform, Azure, UiPath and open frameworks are all options.
• A testing center of excellence that tests automations the way it tests any other release.
• Model and use-case work delivered by our AI development team, and readiness and prioritization by our AI consulting team, in one firm.

Background reading: our white paper AI Transformation: From Pilots to Production (PDF) covers the AI patterns that ship today, and the AI agents and chatbots page covers where it has gone since.

Related services

• AI agents and chatbot development: agents for the multi-step, judgment-heavy parts of a process.
• AI development: document models, classifiers and LLM integration inside your workflows.
• AI consulting: readiness assessment and use-case prioritization.
• AI governance: policy, inventory and risk controls for automated decisions.
• API development and integration: replacing screen scraping with proper interfaces.
• Enterprise resource planning: automation inside SAP and Microsoft Dynamics. See all PS3G solutions.

Guides on this topic

• AI Workflow Automation: 15 Examples by Department (and What They Need)
• RPA vs AI Automation vs AI Agents: Which Tool for Which Task?

Frequently asked questions

What is intelligent automation?

Intelligent automation is the combination of robotic process automation, artificial intelligence and system integration to run a business process with less manual work. RPA handles the fixed, rule-based steps; AI handles steps that need reading, classification or judgment; integration connects the systems involved. The term covers everything from a single automated invoice workflow to an organization-wide automation program with its own governance.

Is RPA still relevant now that AI can read documents?

Yes. AI reads the document; something still has to post the result into the accounting system, update the ERP or move the file, and for systems without an API that something is often an RPA bot. RPA is also deterministic and inexpensive to run, which makes it the better tool for fixed rules. The practical pattern is AI for understanding and RPA or APIs for acting.

Which RPA platform should we choose?

Start with what you already license and where your systems run. Microsoft 365 and Dynamics shops often get far with Power Automate. Organizations building a larger program usually compare UiPath, Automation Anywhere and Blue Prism on governance features, document processing, pricing model and available skills. We run a short selection exercise against your top processes rather than recommending a platform by default.

What is intelligent document processing?

Intelligent document processing (IDP) reads documents such as invoices, forms, claims and contracts, extracts the fields you need, validates them against your records and routes exceptions to a person. It combines OCR and layout analysis with language models that return structured data, plus confidence scores so low-certainty fields get reviewed. It is usually the first AI step added to an RPA workflow.

How do you measure the ROI of business process automation?

Against a baseline captured before the build: time per item, volume, error and rework rates, and the cost of the people and tools involved. The proof of concept reports the same measures on real data, plus the straight-through rate, exception rate and run cost per item. The return is the difference, net of licenses and support, and the decision to scale is made on those numbers.

Can you automate processes in our ERP?

Yes. We automate around SAP, Microsoft Dynamics and other ERP systems using their APIs and integration layers first, RPA where no interface exists, and AI for the document and email steps that feed them, such as order entry and invoice capture. Controls such as separation of duties and full logging are built in so that finance and audit teams can trust the automated postings.