It's 2024, and automation has been a constant pillar in digitization efforts of organizations, across sectors and regions. Many organizations, in a bid to ramp up their operations and streamline routine processes, embraced Business Process Automation technologies aka RPA. This automated high-volume, repetitive, and rule-based manual tasks, thanks to several RPA bots that can work 24x7x365 with little or no human intervention. Tasks like data entry, invoice processing (and any other administrative task that involves clicking buttons and copy-pasting information into forms) simply don't have to be time-intensive and manual anymore. In a 2022 survey, Deloitte found that 74% of organizations were currently implementing RPA solutions. But two years later, in 2024, have these organizations leveraged the economies of scale and efficiency gains they were expecting?

The Limitations of ONLY RPA

The answer is yes, but from a limited perspective. While Robotic Process Automation promised significant ROI, only some organizations were able to see such value. The others weren't because of the limitations of relying only on RPA. Especially for simple tasks but can become complex in varying levels fast.

Let's take an example of a popular automation initiative: customer support interactions. If an organization wants a traditional bot to handle customer queries or support tickets, the variability in customer language, tone, and the context of their issues can be highly complex. RPA technologies hence may struggle to contextualize and respond appropriately to the diverse range of customer inquiries. Hard programming the bot to reply effectively the countless variations of tone, language, coupled with context is borderline impossible with legacy automation technologies. What you thought was a ‘simple' RPA initiative is now a complex headache. Moral of the story: RPA bots can't find their way around when asked to make a cognitive decision. So, what's the answer?

Adding a Layer of Artificial Intelligence (AI): The Era of Hyperautomation

AI lends an effective layer of intelligence to all enterprise process automation initiatives that previously wasn't possible with just Robotic Process Automation. AI extends its possibilities because it introduces cognitive decision-making capabilities, allowing systems to understand, learn, and adapt to complex scenarios. While RPA excels in repetitive and rule-based tasks, AI enhances the adaptability and problem-solving abilities of automation systems. With AI, automation can handle unstructured data, make context-aware decisions, and continuously

improve through machine learning algorithms. This coming together of AI and RPA not only broadens the scope of automation but also allows for the possibility of various use-cases across diverse business processes in a very dynamic environment.

So, if were to go back to the previous example of customer support interactions with RPA, Integrating AI, such as natural language processing (NLP), would enhance the bot's ability to comprehend and respond to the nuances of customer communication, making the automation of customer support more effective and adaptable. So, as you can see, it's not about RPA vs Hyperautomation - it's all about meshing RPA with AI technology. This partnership of two amazingly transformative technologies opens a whole new world of automation possibilities.

It's not about RPA vs AI - it's all about meshing RPA with AI technology to make critical business processes autonomous and cognitively smart.


Enter Generative AI: Creativity and Innovation Unleashed

By now, AI's universal impact across business operations is well established. However, there's another compelling subset of AI itself that can force businesses to redefine what's even possible in the first place. Generative AI, which has captured the world's imagination by storm, and which also holds the key to revolutionizing businesses (industry no bar) in unimaginable ways. Generative AI is a class of AI models that focuses on creating new, original content rather than simply finding patterns in existing data. It learns from existing data to create new, unique outputs that resemble the input data, much like a virtual, creative assistant (minus any human judgement, which is why ChatGPT, for instance, is very popular!)

Using a combination of complex algorithms and deep learning, generative AI can emulate human creativity and imagination to produce clear text and realistic images. This totally reshapes both the value and future of work because it unlocks several possibilities. A recent Nielsen Norman Group study found that “using generative AI (like ChatGPT) in business improves users' performance by 66%, averaged across three case studies.”

Here are a few promising use-cases of Generative AI within a few fundamental industry sectors:

  • Design Optimization in Manufacturing: Generative AI can generate several design alternatives based on specific constraints and objectives. In terms of being innovative and efficient, these "generative" designs can be far better than their manually created counterparts. When faced with a wide range of design options, manufacturers can focus on design solutions that minimize material usage, reduce waste, and improve product performance.
  • Customization and Personalization for IT products: Generative AI can also enable mass customization by generating personalized product designs tailored to individual customer requirements. By leveraging customer data and design parameters, Generative AI algorithms can create unique product configurations that meet specific needs while also being production efficient. This capability allows providers to offer customized products at scale, enhancing customer satisfaction and market competitiveness.
  • Drug Discovery: Generative AI can revolutionize drug discovery by generating novel molecular structures with desired properties. For example, AI-powered generative models can explore the vast chemical space to design new drug candidates with specific pharmaceutical profiles. This can potentially lead to the discovery of more effective and safer medications.
  • Medical Image Synthesis for Augmented Reality: Generative AI techniques can be used to synthesize realistic medical images for training and simulation purposes. For instance, GenAI can generate synthetic medical images, such as MRI scans or CT scans, that closely resemble real patient data. This way, healthcare institutions can retain individuals' confidentiality and privacy while simultaneously enhancing medical education and improving patient care.

Enter Generative AI: Creativity and Innovation Unleashed 
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Since Generative AI represents a significant advancement within artificial intelligence, there may be doubts about the continued relevance of pure ruled-based automation technologies. Here, businesses must recognize that both these transformative technologies must not be at play against each other, rather, in tandem with each other. This is because all organizations need both technologies, for very specific reasons. While generative AI focuses on creating new content and designs, automation emphasizes the automation of repetitive tasks and workflows across an organization. One assists in creativity and innovation, while the other ensures efficiency and scalability wherever applied. Would any organization choose one over the other?

Combining AI and automation, businesses can consolidate their vast dataflows, feed the same into a data universe, and create the foundation to deploy GenAI applications and hence move towards being an intelligent enterprise.

Cloud4C: Your Partner for AI-Powered Automation

As the world's largest application-focused, automation-driven cloud managed services provider serving 4000+ across the world, Cloud4C has crafted frameworks and implementation approaches, aiding numerous organizations in seamlessly scaling their automation solutions and realizing quicker time-to-value. Our comprehensive, cost-efficient solution as a sole partner not only facilitates end-to-end process transformation but also significantly lowers Total Cost of Ownership (TCO). Harnessing cutting-edge hyperautomation technologies like RPA, process analytics, process mining, AI, and ML, we analyze on-ground business processes, pinpoint impactful areas, implement automations, and oversee both processes and robots. From assessing and building a roadmap for process automation to measuring and tracking ROI with the help of dashboards, reports, and metrics, our end-to-end services approach for hyperautomation can definitely help you accelerate your digital evolution efforts. Talk to our experts today!

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Team Cloud4C
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Team Cloud4C

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