MightyBot Transforms Lending with Industry's First Autonomous Agentic AI.
February 25, 2026
•
AI Thinking
AI agents and RPA solve fundamentally different problems. RPA automates deterministic, UI-based tasks — clicking buttons, copying fields, following fixed rules. AI agents handle judgment-intensive work — reading complex documents, applying business policies, making decisions that require interpretation. In financial services, the question is not which to choose, but where each fits and when a hybrid approach delivers the best results.
The enterprise automation market is in transition. Organizations that invested heavily in RPA over the past decade are discovering its limits as workflows become more complex and document-heavy. Meanwhile, the rush to deploy AI agents has created its own problems — Gartner predicts over 40% of agentic AI projects will be abandoned by 2027 due to unclear business value and inadequate governance.
The truth is that both technologies have legitimate roles. Understanding where each excels — and where each fails — prevents the expensive mistake of deploying the wrong tool for the job.
Robotic Process Automation records and replays human interactions with software interfaces. An RPA bot can log into a system, navigate to a specific screen, copy data from one field, paste it into another, and trigger a workflow — exactly the way a human would, but faster and without breaks.
RPA excels at three types of work: high-volume, repetitive tasks with stable interfaces (data entry between systems that rarely change); rule-based routing where decisions follow simple if/then logic (if amount exceeds $10,000, route to manager); and legacy system integration where APIs do not exist and screen-scraping is the only automation path.
RPA struggles when any of these conditions change. A UI update breaks the bot. A new document format requires reprogramming. A judgment call that falls outside predefined rules causes the bot to fail or, worse, proceed incorrectly. In financial services, where regulations evolve, document formats vary, and compliance requires interpretation — RPA's brittleness becomes a liability.
AI agents do not replay recorded actions. They understand context, interpret documents, apply policies, and make decisions within defined boundaries. Where RPA follows a script, an AI agent follows a policy — adapting to variation while staying within governance constraints.
An AI agent processing a construction loan draw request reads multi-page document packets with variable formats, classifies each page by type, extracts relevant fields, normalizes data across documents, evaluates lending policies against the extracted data, and produces an auditable decision with evidence links. No scripted playback could handle this level of variation and judgment.
The critical architectural difference: RPA operates on the UI layer (it sees what a human sees on screen). AI agents operate on the data layer (they understand the content regardless of how it is displayed). This means AI agents are not affected by UI changes, can process documents in any format, and can handle the natural variation that breaks RPA bots.
| Capability | RPA | AI Agents |
|---|---|---|
| Decision type | Deterministic (if/then rules) | Judgment-based with policy governance |
| Document handling | Fixed templates only | Variable formats, multi-document packets |
| Adaptability | Breaks when interfaces change | Adapts to variations within policy bounds |
| Audit trail | Action log (what happened) | Why-trail (what happened + why + evidence) |
| Error handling | Fails or escalates on exceptions | Evaluates exceptions against policy |
| Setup complexity | Low for simple tasks | Requires policy definition and training |
| Maintenance burden | High (breaks with UI changes) | Lower (policy updates, not UI remapping) |
| Best for | Stable, repetitive, rule-based tasks | Complex, document-heavy, judgment-intensive work |
| Compliance capability | Follows rules but cannot interpret them | Interprets, applies, and evidences compliance |
| Cost profile | Low per-bot cost, high maintenance | Higher platform cost, lower maintenance |
RPA remains the right tool when these conditions are met:
Common RPA use cases in financial services: account opening data entry between core banking and CRM systems; regulatory report generation from structured data sources; payment reconciliation between systems with matching formats; customer communication triggers based on system events.
AI agents become necessary when the work involves any of these characteristics:
In financial services, these characteristics describe the majority of high-value work: draw request processing, credit analysis, compliance review, claims evaluation, and vendor due diligence. This is where policy-driven AI delivers the most impact.
The most effective enterprise automation strategies use both technologies, each in its zone of strength.
Pattern 1: RPA for data movement, AI for decisions. RPA collects documents from email attachments, file shares, and portals, then hands them to an AI agent for classification, extraction, and policy evaluation. RPA handles the predictable plumbing; the AI agent handles the intelligent processing.
Pattern 2: AI for analysis, RPA for updates. An AI agent reviews and validates a loan package, producing structured findings. RPA bots then update the loan management system, trigger notifications, and file records — tasks that are deterministic once the AI has made the decision.
Pattern 3: Progressive replacement. Organizations with mature RPA deployments can progressively migrate the judgment-intensive components to AI agents while keeping RPA for the stable, mechanical components. This avoids the risk and cost of a complete technology replacement.
The key principle: use the simplest technology that handles the work correctly. Do not deploy AI agents for tasks that RPA handles well. Do not force RPA into judgment-intensive work where it will fail.
The Built Technologies deployment illustrates the distinction clearly. Before MightyBot, some lenders had RPA bots that could pull draw request PDFs from email and upload them to the loan management system. The bots worked — until an email format changed or a PDF arrived as a ZIP file.
But no amount of RPA could review the actual draw documents. Reading a lien waiver, verifying insurance coverage dates, reconciling AIA form line items against the budget, and determining whether the draw complied with the lender's specific policies — this required AI agents with document intelligence and policy evaluation capabilities.
The result: 95% time reduction, 99%+ accuracy, 10x throughput increase. None of that was achievable with RPA alone, because the bottleneck was never data movement — it was document understanding and policy evaluation.
For organizations evaluating whether to supplement or replace RPA with AI agents, consider these factors:
Maintenance cost trajectory. If your RPA maintenance costs are rising as systems evolve and bots break, AI agents may lower total cost of ownership despite higher initial platform costs.
Compliance pressure. If regulators are asking for better audit trails, evidence chains, and decision transparency, AI agents with why-trail auditing address those requirements in ways RPA cannot.
Document complexity. If your team spends significant time on document review, interpretation, and validation, that work is outside RPA's capabilities and directly in AI's strength zone.
Error consequences. In financial services, the cost of an automation error can be orders of magnitude higher than the labor savings. AI agents with policy governance and human-in-the-loop controls provide the accuracy and safety financial institutions require.
Should I replace RPA with AI agents?
Not necessarily. RPA and AI agents solve different problems. Replace RPA with AI agents where work involves document interpretation, policy-based judgment, or variable formats. Keep RPA for stable, deterministic tasks like data entry between systems with fixed interfaces. Many organizations benefit from a hybrid approach.
What is the difference between AI agents and RPA?
RPA replays recorded actions on software interfaces — it clicks buttons and copies fields following fixed scripts. AI agents understand content, interpret documents, apply business policies, and make governed decisions. RPA operates on the UI layer; AI agents operate on the data layer.
Can AI agents and RPA work together?
Yes, and they often should. Common hybrid patterns include RPA collecting documents and AI agents processing them, or AI agents making decisions and RPA bots updating systems. Use the simplest technology that handles each component correctly.
Is RPA dead?
No. RPA remains effective for high-volume, deterministic tasks with stable interfaces. What is changing is the boundary — as AI agents handle more complex work, RPA's appropriate scope narrows to truly mechanical tasks. Organizations should evaluate each workflow individually rather than choosing one technology universally.
