The Potential Of Generative AI Goes Way Beyond Productivity Assistants

Mihir Shukla is CEO and cofounder of Automation Anywhere, a global leader in agentic process automation.

Every organization is feeling increasing pressure to become an AI-powered company to improve service, move faster and gain competitive advantage. This has manifested in a flood of generative AI (GenAI) applications and solutions hitting the market.

Simultaneously, we’ve seen continued criticisms of this emerging AI era, arguing that things aren’t moving fast enough or that AI isn’t making a dramatic enough impact for companies based on what was promised. There's some understandable disillusionment this year following the peak excitement of GenAI’s arrival, resulting in hesitation to dip a toe in the water.

However, with all the noise around AI, people are missing the signal. We don’t have to wait five years for AI innovation to deliver across all its future manifestations; the future is indeed here now. We just have to look for it.

Personal AI Productivity Assistants

For many, personal AI productivity assistants are their first experience with GenAI. These tools drive productivity increases by helping employees write emails, research and summarize information, generate graphics, develop code and more.

However, personal AI productivity assistants aren’t enough to drive dramatic enterprise results and deliver on the promise of AI. These solutions save employees time and enhance productivity at an individual level. The long-term impact of these tools will be smaller in scale in comparison to using them in parallel with organization-wide AI solutions that autonomously handle complex cognitive tasks and workflows. That's where enterprise-grade AI agents are proving to be beneficial.

Enterprise-Grade AI Agents And Agentic Process Automation

Enterprise-grade AI agents deployed as part of agentic process automation combine the cognitive capabilities that GenAI brings with the ability to act across complex enterprise systems, applications and processes. They can learn based on data, read and extract key data from documents, make decisions, interact with humans in the loop and even act autonomously to achieve their intended goals. They make it possible to automate more elaborate workflows as an abstraction layer on top of enterprise applications and systems of record.

For instance, a series of AI agents that interface with different applications or process stages can help resolve complex customer service requests in minutes instead of hours or even days. One agent in this process might manage intake and triage of requests to make sure all necessary information is available to proceed. Another agent researches the customer request across systems, including initiating custom database queries to retrieve transactional information and checking for accuracy. Finally, another agent resolves the request by updating systems using policy documents as a guide and communicating back to the customer.

Like personal AI productivity assistants, enterprise-grade AI agents augment people’s work to enhance productivity. They also reduce the tactical busywork and “swivel chair” jumping from app to app that many employees get bogged down in. They free up time for employees to be more strategic and focus on more creative, innovative tasks, and they give employees opportunities to ramp up faster in a new role or take on more advanced and strategic work sooner.

Furthermore, agentic process automation does this at scale, across applications and platforms, teams and departments and even entire organizations. It has the potential to handle entire processes from start to finish behind the scenes to generate enterprise-level time savings while also giving people time back to do what they do best, leading to higher job satisfaction.

AI Agent Considerations

Enterprise-grade AI agents, like all GenAI assistants, raise some considerations for the organizations that create and deploy them.

Using AI to handle tasks from beginning to end, including tapping their cognitive abilities to make decisions, requires strong data-quality practices, security and privacy frameworks, governance and a degree of human oversight. Although human involvement is reduced significantly, agentic workflows still require some supervision.

Companies should stringently evaluate vendors to ensure that they have the highest standards of AI governance, security and data protection in place. Buyers should also evaluate offerings based on flexibility and customization. In addition, organizations should prioritize companywide training to ensure that any employee with the potential to use GenAI is familiar with security considerations, best practices, the value of high-quality data and more.

Conclusion

The personal AI productivity assistants that we’re seeing change how work is done today are innovative. But achieving the full potential of GenAI—large-scale, enterprise-grade transformation of business processes—requires specialized agents that work with numerous enterprise applications and across vendor platforms, shaping intelligent workflows that support employees and transform businesses. This technology exists, and it can drive dramatic enterprise results fast.

The future is already here—are you looking for it?

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