Few technologies have stirred things up as fast as Gen AI has. According to research from McKinsey , two-thirds (65%) of executives report that their organizations are now regularly using Gen AI - a figure that’s almost doubled in just ten months. And the buzz surrounding this transformational technology isn’t slowing down; with three-quarters predicting that Gen AI will lead to significant or disruptive change within their industries.
Disruption is the key word when it comes to Gen AI, with the technology garnering cross-industry attention through its potential to redefine the future of work, deliver efficiencies, and create a new paradigm for the future of enterprises. This disruptive capability is what future-facing organizations are working to harness, with a recent MIT survey revealing that 65% of enterprises are actively considering new and innovative ways to use Gen AI to unlock hidden opportunities from data.
Prior to Gen AI’s entry into the foray, leading organizations were already beginning to embrace hyperautomation as an integral part of digital transformation efforts. But, almost overnight, Gen AI burst onto the scene accelerating these journeys while promising all-new dimensions, causing hyperautomation to evolve at an unprecedented pace. In this article, we explore four distinct ways the technology will disrupt hyperautomation, creating new realities defined by the seamless convergence of human ingenuity and AI.
Disruption 1: Redefining UX
User experience represents the first arena of disruption within hyperautomation, with Gen AI’s natural language processing (NLP) capabilities transforming how employees and customers alike engage with technology. In the pre-Gen AI landscape, human-machine interaction was limited to artificial constructs - a click of a button or navigating screens. But the power of natural language is now being harnessed to facilitate seamless communication between the two.
Most importantly, Gen AI can converse in a contextual way, able to provide insights within a specific domain, be it travel, healthcare, insurance or any other applicable industry. It’s ushering in an era where machines become assistants and collaborators, transcending conventional interaction methods and providing unprecedented efficiency and productivity to the process management industry. In the future, Gen AI will become a process co-pilot - an assistive tool that empowers employees.
While use cases are myriad, focusing in on an industry like healthcare serves to demonstrate the level of disruption on offer. Gen AI can answer queries against established guidelines, eligibility criteria and medical necessity, while it can also analyze medical literature, patient diagnoses and risks, among many other capabilities. Crucially, healthcare staff can prompt machines to conduct these tasks in the same way they would a human - through conversation - using whichever channel they find the most convenient.
Already, we see doctors reporting that specialized chatbots including Doximity GPT, a HIPAA-compliant version of ChatGPT, have cut the time it takes to write prior-authorization requests. One doctor said that 90% of his requests for coverage have been approved by insurers, compared with about 10% before. As research from Everest Group , in collaboration with WNS, shows, healthcare leads the way in seeing potential in Gen AI’s text and video generation capabilities.
This kind of disruption is being experienced in industries far and wide, with 77% of organizations using Gen AI in 2024 to enhance user experience, according to MIT . GitHub’s AI-powered coding tools, for example, have become a staple among developers, while leading financial services firms launch Gen AI chatbots to make customer service more effective and efficient, showcasing this disruption in action.
Disruption 2: Hyperautomation Reconfiguration
The second disruption comes through a reconfiguration of hyperautomation, evolving from a human-centric process to a future where process orchestration is sparked by Gen AI. This vision sees Gen AI-fueled hyperautomation technologies act as ‘makers’ of a new way of working, seamlessly executing processes which are then validated by the human in the loop - the ‘checkers’.
It’s a future enabled by Gen AI’s powerful capabilities when it comes to creating cases, generating visuals, reasoning and providing insightful analyses. So, what would this look like in practice? Take the financial sector. Gen AI can scrutinize loan applications, synthesize data and even go as far as recommending whether that application should be granted or not. Prior to Gen AI, these steps had to be taken by a human.
However, it’s important to note that humans still need to remain at the center of decision making within this disruption. At this stage, Gen AI lacks several features required for complete automation, with hallucination one case in point. Models can still generate misleading result presented as fact, a result of factors including insufficient training data, data bias and opacity. It’s a roadblock that organizations are cognizant of and working to overcome: 40% of enterprises report reservations around a lack of high-quality training data for Gen AI solutions, with 62% seeking third-party assistance to enhance training on enterprise data.
Disruption 3: The Bowling Alley Effect
In the third area of disruption within hyperautomation - the ‘bowling alley’ effect - we start to see the true scale of transformation that Gen AI will unleash. In this analogy, Gen AI is our disruptive bowling ball, travelling at speed toward different platforms - our pins. Gen AI will solve compelling problems in one area, unleashing a domino effect to leverage this success and unlock new use cases in another, knocking down pin after pin until its impact reverberates across the spectrum.
Essentially, what it means is that Gen AI will help unlock new capabilities in platforms that were previously considered out of scope and enable hyperautomation platforms to evolve at an unprecedented rate.
Take intelligent document processing as an example. Traditionally low-code platforms have fallen short in this area, hampering automation efforts. But Gen AI has opened a whole new set of opportunities across the enterprise to create and manage content and data, allowing platforms to augment and develop their capabilities with IDP, Conversational AI etc. Similarly, capabilities such as case routing, document summarization, natural language chatbots have all been made easy for integration into core low-code platforms.
Gen AI is also unlocking a treasure trove of new insights with its intersection with data analytics. At present, enterprise data remains underutilized, fragmented and inaccessible to those who need it the most – but Gen AI contextualizes this data with natural language chatbots enabling easy integration. And the sheer volume of data organizations possess is only set to increase. The technology will tackle the significant hurdles posed by labor-intensive processes inherent in extracting true value from data, streamlining its analysis, configuration and optimization, conducting quality analysis, unearthing anomalies or complexities, and quantifying the enterprise impact.
New values will also be unlocked by the ingestion of unstructured data, where currently swathes of potentially transformative insights remain buried. Gen AI-tools can automate the extraction of this data and convert it into harmonized datasets able to be harnessed by the enterprise. With Gartner estimating that more than 80 percent of enterprise data today is unstructured, new avenues to be unlocked - or pins to be knocked down - are limitless.
Disruption 4: The Autonomous Enterprise
Looking further out we find the fourth - and most transformative - disruption: the autonomous enterprise. The timeline for this shift remains uncertain, but what is certain is that a profound change is on the horizon, with Gen AI set to facilitate an era where an organization’s strategic goals are automated end-to-end. Gen AI will break goals down into various tasks and processes, solving its own problems through next generation reasoning capabilities, adjusting courses to follow an optimal path in real-time. It’s these capabilities - unlocked by the convergence of human ingenuity and AI - that inform estimates forecasting Gen AI to soon add trillions of dollars in value to the global economy.
In this projected future, AI-driven bots will perform actions independently, disrupting the augmented intelligence landscape. Once again, human machine co-ordination will experience radical change and it is yet unclear which roles humans and autonomous agents will perform. But, we do know that the low end of the decision making, or goals, will belong to these agents in future. It is likely that human interaction, expertise, intuition, and insights will continue to play a key role in areas where the risk of complete automation is high or in areas where laws mandate humans in a supervisory role – think sectors like healthcare, or loan adjudication in some countries.
Final Thoughts:
The future of work will be drastically different from today. Through Gen AI, we’ll experience unprecedented levels of productivity and work more efficiently than ever before. But this isn’t just about machines automating tasks. Most transformational of all, however, is how organizations will be empowered to deliver new kinds of value, with Gen AI having the dual impact of unlocking new insights while enabling time to be repurposed toward taking decisive action on them. Harnessing this disruption will see organizations reach the threshold of an unprecedented paradigm, defined by the collaborative prowess of human expertise and machines.
The possibilities ahead are endless – and we're all for it!