How To Keep Up With the Latest AI Tools

Generative AI brings both promise and peril. Organizations that adapt to its ways and are mindful of its risks can secure unprecedented benefits.

Sunil Senan, SVP and Global Head, Data & Analytics

October 30, 2023

6 Min Read
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spainter vfx via Adobe Stock

The 2023 edition of an annual survey on AI saw generative AI taking center stage. One in three respondents said their organizations were already using GenAI in one or more business function, and two out of five said it was the reason for increasing investments in AI. This is a major transformational technology on par with the past great waves of tech-driven productivity change. To ignore the potential of GenAI would be to risk becoming irrelevant in terms of the entire baseline of your business such as costs, opportunity to grow revenue and speed to market. However, amid the hyper excitement surrounding generative AI for its unprecedented potential to disrupt and transform business, it is important not to lose sight of its challenges. Topping the list is the need to adapt and make significant changes to come to terms with and benefit from this technology. 

Traditional organizational structures will be disrupted as generative AI amplifies human potential by improving the productivity of all the roles and personas in the workforce. Beyond the efficiencies and productivity, generative AI has tremendous business potential in driving growth and acceleration in speed of business. Organizations have to find a way to set the right foundation and capitalize on the opportunities. In order to thrive in this journey, enterprises have enabled talent transformation by leveraging AI for talents that are “closest” to the technology, especially software developers, engineers, and designers who are looking at a fairly steep learning curve in deep learning frameworks and machine learning engineering.

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To protect against the downsides of AI, enterprises should embrace ‘Responsible by Design’ principles as the foundation of their AI systems. It will help models bridge the trust deficits between AI systems and tribal knowledge with humans with in-context reasoning and explanability. Such human-in-the-loop approaches will not only help improve the quality of the outputs of AI systems but also help employees develop intuition and understanding through generative AI: for example, product designs that are unlike anything that the human mind can conceive. 

Enterprises would also need to make changes on the technical and talent fronts to maximize the benefits of generative AI. For example, they would need to evolve algorithms that are more sophisticated than the commonly used prediction and classification models. Advanced GenAI programs must be supported by several capabilities to succeed, including a comprehensive technology stack comprising security, data rights, entitlement, confidential compute, collaboration platforms, hosting facilities, and complex software. After conceptualizing their tech stacks, enterprises have to decide what parts to build themselves, and what to provision through AI services, in software as a service mode, or via cloud-based platforms. 

Related:Manage Generative AI Risk Before It Manages You

This support structure for GenAI is important because adoption is already increasing. For a large machinery manufacturer, GenAI is being used to summarise important information about various parts and their usage and provide the same in a standard format on a website. This information is gleaned internally from documents, pdfs, ppts, and even videos.

Similarly, for electronics, major sentiment analysis is being done, not only on social media comments but on all customer emails to them and even calls (using voice recordings). Without LLM models such an analysis would have required a much longer time, with custom models built for the same. However, with tools like text bison and chirp, the sentiment analysis is possible to conduct in a shorter period of time.

For a home goods retailer, GenAI is being applied to create a bot for both, internal and external interactions. A railroads major is using GenAI to convert software code from one language to another. A sports goods major is using GenAI for sentiment analysis about their brand. And many more.

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Generative AI is powerful, but it is also fallible, known to make coding errors, issue wrong recommendations, come to biased conclusions, or violate data privacy, on occasion. Hence it must be under the supervision of a team of employees who are responsible for ensuring that the outcomes are accurate, fair, ethical, and compliant.

There is also need for widespread micro change management in the business organization to increase competency of non-technical users about the basics of GenAI and how to work its tools, ease the transition to new ways of working, and create the conditions for AI to be integrated across enterprise operations. At our company, we have launched Topaz, an AI-first set of services, solutions, and platforms, to amplify the potential of humans, enterprises, and communities. A critical facet of Topaz is that we employ a consistent and mutually reinforcing set of principles, practices, and procedural controls to manage training data provenance, ethics, trust, and social responsibility.

The following actions will help organizations create the right environment for their GenAI initiatives:

  • Nurture a culture of innovation at speed and innovation at scale: Generative AI can unleash a plethora of AI initiatives without visible business outcomes unless enterprises think of specific outcomes they want to generate in terms of growth, efficiencies, and more importantly, an ecosystem play to build new AI-driven products, services, and experiences. Creating an AI canvas with business, which prioritizes AI initiatives with business value impact, ease of implementation, and trustworthiness of the solution is an important tool that will allow enterprises to build a roadmap and blueprint with a meaningful and purposeful outcome. Under this framework, innovation at speed would experiment with ideas to achieve the desired outcome and innovation at scale would take those ideas to production, scale implementation, and deliver value.

  • Build a supportive organizational culture: As mentioned earlier, generative AI models can enforce new ways of working, requiring considerable openness from the workforce to embrace these new ways. To bring that about, organizations should communicate the benefits of embracing GenAI and support employees in mastering the various tools and models they will need to work with. A culture of continuous learning will ensure employees have not just the technical but also the soft skills they need to work with AI in harmony and keep pace with the evolving technology landscape.

  • Put governance in place: The use of personal data to run generative AI poses several risks, such as privacy violations, biased outcomes, spread of misinformation, etc. A robust governance framework is essential for managing these risks and also for creating policies for responsible use, consent management, and protection of content creators’ intellectual property rights. The framework should also pin accountability for irresponsible use, define a response plan to mitigate adverse events, and define the role of various people, including the top management and company board, to keep generative AI on track.

  • Leaders must drive the change: An Infosys-commissioned survey to understand the impact of AI on businesses revealed great confidence among leaders: 77% of IT decision makers said they would be able to train the employees in their company to take on the new roles created by AI. While this is commendable and important, leaders must also get their hands dirty with AI and personally lead the change at least in the initial stages of adoption. They must focus on building creative thinking and other skills to bridge the gap between employees and GenAI tools and technologies.


Is generative AI the greatest in a long list of great technologies? Its multifaceted capabilities position it to drive improvements, solve problems, and create innovations in every sphere of every business. But even as it amplifies, it challenges. Organizations must prepare for significant change and adaptation to understand GenAI, capitalize on it, and when required, even restrain it. Overall, 2024 will be a pivotal year where GenAI’s capabilities continue to soar along with the pressure to ensure its responsible development as a crucial underpinning.

About the Author(s)

Sunil Senan

SVP and Global Head, Data & Analytics, Infosys

Sunil Senan is Senior Vice President responsible for the Data & Analytics service line at Infosys. In this role, he works closely with Infosys’s strategic clients on their data & analytics led digital transformation initiatives. He is passionate about how data & analytics is creating economic impact in the society and how enterprises and governments can engage in driving this transformation. He has written the “Data economy in Digital times” paper articulating how the new data economy presents a set of new possibilities for enterprises, governments to serve their citizens and consumers.

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