Maximising the benefits of Generative AI for enterprises

Recently, Prince Kohli, chief technology officer, Automation Anywhere spoke to Dataquest to shed more light on Generative AI

Supriya Rai
New Update
Generative AI

Generative AI has emerged as a game-changer in the world of technology, promising a plethora of opportunities for businesses to drive innovation and enhance efficiency. With its ability to create new content, models, and solutions, generative AI offers unprecedented potential for automation and productivity. By adopting a comprehensive approach, enterprises can tap into the full capabilities of generative AI, harnessing its power to transform operations, develop cutting-edge products, and gain a competitive edge in their industries. While there may be challenges to address, the ethical considerations and data governance framework can ensure responsible and ethical use of this groundbreaking technology, unleashing its true potential for transformative impact across various sectors. Recently, Prince Kohli, chief technology officer, Automation Anywhere spoke to Dataquest to shed more light on this.


DQ: In what ways enterprises should adopt a comprehensive approach to maximize the benefits of Generative AI?

Prince Kohli: An end-to-end comprehensive approach is essential to gain benefits from Generative AI. This usually involves the following steps:

  • Understand Risks and Opportunities: GenAI creates both a risk of market disruption from known and currently unknown competitors. At the same time, it is a once in a lifetime opportunity to enhance or create products, or to introduce operational efficiencies. The first step would be to identify and prioritize these opportunities or use cases, based on their alignment with long term company vision.
  • Establish a data governance framework: Based on the use cases chosen, data will become extremely critical. This may be training or testing data if new models are being developed, or customer data that is used either for inference or fine-tuning existing models. This framework should include policies regarding data quality (and the process), data lifecycle and biases. It should also include operational steps such as what to do when data needs to be redacted.
  • Selecting and Training Models: Choose appropriate Generative AI models based on the enterprise's specific requirements and train them using diverse and relevant data. Optimize model performance through techniques like pre-training and fine-tuning.
  • Integrating and Deploying Effectively: Integrate Generative AI models into workflows and systems, ensuring smooth data exchange and interaction. Develop APIs or connectors to enable efficient integration.
  • Implementing Monitoring and Governance: Establish robust mechanisms to monitor Generative AI model performance in real-world scenarios. Continuously evaluate outputs, gather user feedback, address biases, and ensure ethical considerations, data privacy, and security.
  • Providing User Education and Training: Offer comprehensive training and support to users and stakeholders, enabling them to effectively utilize Generative AI tools. Educate them about responsible and ethical use, as well as the capabilities and limitations of Generative AI.
  • Embracing Iterative Improvement: Treat Generative AI adoption as an iterative process. Gather feedback from users, evaluate the impact on business objectives, and continually enhance models, processes, and workflows to drive performance improvements and address evolving needs.

By adopting these comprehensive approaches, enterprises can unlock the full potential of Generative AI, thus driving innovation, improving efficiency, and gaining a competitive advantage in their respective industries.

DQ: Implications of integrating a technology that so little is known about? What are the main challenges or limitations of generative AI?

Prince Kohli: Generative AI brings forth a wide array of opportunities and accessibility that will greatly enhance automation, leading to a substantial boost in worker productivity. This advancement is akin to and can build upon existing patterns of automation usage, where it assists users and drives various processes, while opening up novel avenues to enable automation in new and innovative ways. However, like in any new major technology, there are always cautions to consider:


 Need for quality control and data accuracy 

Data is the lifeblood of AI, giving AI models their form. Unfortunately, any biases inherent in the training data can be reflected in the model's outputs, potentially leading to biased results. This has implications for the overall quality and dependability of the generated outputs.

 Ethical and legal limitations 


The launch of ChatGPT elicited both excitement and skepticism. It quickly gained recognition as a highly proficient chatbot, capable of understanding and responding in a conversational manner that closely resembles human interaction. However, as ChatGPT became more widely used, concerns arose regarding its potential for facilitating academic and workplace dishonesty. Furthermore, the utilization of ChatGPT has also raised concerns related to intellectual property rights, including potential infringements of copyrights and trademarks.

 Computing power and infrastructure

If your GenAI implementations require frequent re-training with huge amounts of data, it can get immensely expensive due to the required access to powerful hardware and advanced computing infrastructure. 


DQ: Can you discuss the ethical implications of generative AI?

Prince Kohli: As Generative AI becomes more prevalent, it is crucial to address potential ethical concerns and implement measures to mitigate risks. There is a need to acknowledge the importance of fairness, transparency, and accountability in the deployment of Generative AI. We advocate for defining clear guidelines and policies to guide the responsible use of Generative AI within organizations. This includes establishing mechanisms for human oversight, feedback, and bias mitigation to ensure that decisions made by Generative AI models align with ethical standards. It is also pertinent to emphasize the importance of data privacy and protection. 

DQ: How Automation Success Platform is leveraging generative AI to serve customers across verticals


Prince Kohli: The Automation Success Platform provided by Automation Anywhere is designed with the highest standards of security, privacy, and compliance. It combines automation and AI to enhance ROI, offering adaptability and openness for businesses to leverage customized Language Models (LLMs) based on their unique needs. The platform incorporates guardrails, human feedback options, and data privacy measures to ensure responsible and ethical AI usage, protecting sensitive information and upholding company values.

Three key innovations of the Automation Success Platform are:

Automation Co-Pilot + Generative AI for Business Users: This integration revolutionizes team productivity by enabling a wide range of capabilities across systems, such as content creation, summarization, email automation, and recommendations. It empowers teams to leverage generative AI for any use case, optimizing workflows and driving increased productivity.


Automation Co-Pilot + Generative AI for Automator: This integration seamlessly immerses within the developer ecosystem, allowing individuals with different skill sets, including experienced developers and business users, to convert conversations into automation. It accelerates the speed at which organizations achieve automation ROI, making automation development accessible to anyone within the organization.

Document Automation + Generative AI: This innovation enables swift comprehension, extraction, and summarization of data from various document types, including unstructured, structured, and semi-structured data. It seamlessly integrates with automation, eliminating complex manual actions and data transfers between systems.

Through these innovations, the Automation Success Platform empowers businesses to maximize the benefits of generative AI, enhancing productivity, efficiency, and workflow optimization across verticals.

DQ: What is the immense potential for Generative AI to drive tangible business outcomes?

Prince Kohli: Companies worldwide are currently facing a significant productivity challenge. Fortunately, the convergence of generative AI and automation presents an incredible opportunity to overcome this hurdle and usher in a new era of productivity. 

Automation Anywhere offers a range of products and solutions that leverage Generative AI to assist users and enterprises in automating processes and achieving operational efficiency. Here are some of Automation Anywhere's innovations that utilize Generative AI for seamless business outcomes:

  • Automation Co-Pilot, powered by Generative AI, helps in enhancing team productivity by enabling a wide range of tasks, including content creation, summarization, email automation, and recommendations.
  • Automation Co-Pilot for Automators seamlessly integrates with generative AI, empowering both developers and business users to convert conversations into automation. This accelerates the automation ROI by making automation development accessible to individuals with different skill sets.
  • With Generative AI capabilities, Document Automation comprehends, extracts, and summarizes data from diverse document types, including unstructured, structured, and semi-structured data. The integration with automation ensures smooth data flow into process workflows, eliminating manual efforts.