• RPA market projected to expand to $28.6 billion by 2031.
  • Generative AI integration with RPA platforms is identified as a key market opportunity.
  • The shift could extend automation to tasks once considered too complex for RPA, widening adoption across industries.
  • Businesses face choices around risk, governance and skills as RPA evolves.

What the forecast says

The Robotic Process Automation (RPA) market is expected to expand to $28.6 billion by 2031, driven largely by the integration of generative artificial intelligence with RPA platforms. Industry observers point to this combination as a major growth driver because it promises to broaden the kinds of work automation can handle.

Why generative AI matters for RPA

Generative AI techniques can add language, reasoning and pattern‑generation capabilities to traditional RPA bots. When paired with the rule‑based workflows RPA already automates, these AI capabilities create opportunities to automate more complex, knowledge‑intensive tasks that previously required human judgment.

This blending of technologies is the central reason analysts highlight generative AI as a market opportunity: it can make automation more flexible, reduce the need for brittle scripting, and accelerate deployment across departments that handle unstructured data.

What this means for businesses

Firms that adopt RPA platforms with strong generative AI features could gain faster process improvements and cost efficiencies. Early adopters can also experiment with new use cases — for example, automating document understanding, conversational interfaces, or decision support — that were once out of reach for conventional RPA.

However, organizations should avoid assuming plug‑and‑play results. Integrating generative AI into enterprise automation requires careful design, testing and governance to ensure outputs are accurate, explainable and secure. IT, process owners and compliance teams will need to collaborate closely during rollout.

Risks and challenges

As RPA expands, several risks deserve attention:

  • Accuracy and reliability: Generative models can produce plausible but incorrect outputs; validation layers are essential.
  • Governance and compliance: New capabilities raise questions about auditability and regulatory oversight.
  • Skills and change management: Teams will need new skills to manage AI‑enhanced automation and to interpret its results.

Addressing these challenges upfront will reduce the chance of costly errors and help organizations realize long‑term value from RPA investments.

Next steps for leaders

Decision makers should treat the RPA+AI trend as an opportunity and a risk. Practical first steps include auditing current automation estates, piloting generative AI features in low‑risk workflows, and building governance frameworks that cover accuracy checks, data handling and human oversight.

The market projection to $28.6 billion by 2031 signals a clear shift: RPA is evolving from task automation toward knowledge work augmentation. Companies that prepare now — balancing experimentation with controls — are likeliest to benefit as these capabilities become mainstream.

Image Referance: https://www.globenewswire.com/news-release/2026/01/19/3221170/0/en/Robotic-Process-Automation-Market-Set-to-Expand-to-28-6-Billion-by-2031-Driven-by-Generative-AI-Integration.html