The Future of Robotic Process Automation (RPA)

2 min read

The future of RPA requires improved signal intelligence, dynamic feedback loops and richer contextual relevance.

Success requires every RPA leader to consider where and when to apply analytics, automation and artificial intelligence (AI) in their design. The brands and organizations that can improve decision velocity will succeed in anticipating customer needs, delivering on brand promise and reducing regulatory and compliance risk.

Learn how future leaders can start their journey in the 3A’s of RPA — analytics, automation and AI:

The massive evolution from RPA to intelligent autonomous applications has begun

The robotic process automation (RPA) market has helped many clients increase speed, deliver higher accuracy, achieve greater levels of consistency, reduce costs, provide scale and improve quality. Constellation Research estimates the market size in 2021 to achieve $2.2B in revenue, with a CAGR of 18.8% and growth to $5.07B in 2026. 

RPA is a transactional system technology that enables automation of business processes using software robots ("bots"). RPA tools watch users and then repeat similar tasks in the graphical user interface (GUI). RPA is different than workflow automation tools because those are explicit rules and actions written to automate actions in an unintelligent manner. 

RPA tools have reached their limit in terms of capability because transactional automation requires a large overhead of management. Simply put, transactional automation is hard to manage. Hence, a new class of enterprise apps known as autonomous applications have emerged to deliver intelligent automation, cognitive capabilities and artificial intelligence for organizations in business functions like finance, supply chain, customer experience, human resources and planning.

AI and ML power the future of autonomous enterprises

The reality is that traditional transactional applications have run their course. The pressure to reduce margins, technical debt and investment in core systems creates tremendous incentives for the automated enterprise. Customers seek cognitive-based approaches to build the true foundation for automation and artificial intelligence — driven precision decisions. The benefits include less staffing, reduced errors, smarter decisions and security at scale. The quest for an autonomous enterprise starts with a desire to consider what decisions require intelligent automation versus human judgment. These applications cannot rely on hard-coded rules to succeed. They must take a cognitive approach, apply AI and machine-learning (ML) techniques and become autonomous.

Vendors from multiple fronts intend to deliver on this promise. Legacy enterprise resource planning providers, cloud vendors, business process management solutions, robotic process automation products, process-mining vendors and IT services firms with software solutions attempt to compete with pure-play vendors for both mind share and market dominance in this market, which Constellation Research expects to hit $10.35 billion by 2030. Constellation believes every enterprise will design for self-driving, self-learning and self-healing sentience.

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