By Ashok Panda, Vice President, Global Head, AI & Automation Services, Infosys
Almost everyone today is touched by AI technology, often unknowingly. While AI has existed for decades, consumer applications like ChatGPT, Google Maps, and Alexa have made it widely accessible.
But how can AI be democratised within enterprises, and why should they pursue it?
Achieve efficiency, innovation, and competitive advantage
Democratisation of AI creates more value for all. One estimate is that in 2030, AI could potentially add a staggering $15.7 trillion to the global economy, benefiting every sector.
AI can be leveraged across industries, including AI-powered automated manufacturing, chatbots in customer service, analysing transactions to prevent cyberattacks, using image analysis for faster disease diagnosis, personalising products and services, summarising documents, writing codes and generating designs.
Successful enterprises are using AI to reshape their work, workplace, and workforce. We will discuss few aspects of the same.
Technology Approach
Platform-based, poly-AI approach to futureproof AI investment: The rapid evolution of generative AI has rendered models increasingly transient. According to the Stanford HAI index report, 149 foundational AI models were released in 2023, twice the number from 2022. With the constant launch of newer, better models, enterprises must transition seamlessly between AI models. Establishing an enterprise-grade platform with an abstraction layer and connectors like APIs is crucial. This enables easy integration of AI providers, models, and configurations, supporting access to AI assets, quick scaling, shared infrastructure, and innovation at scale and speed.
AI as new UI: Abstracting away complex technical details can make AI more accessible to non-technical users. These platforms should provide intuitive interfaces, drag-and-drop functionality, and pre-built models for common use cases. Democratising access to AI tools and platforms within the enterprise can empower employees to experiment with and leverage AI capabilities. This can include providing user-friendly interfaces, pre-built models, and APIs that abstract away complex technical details, making AI more accessible to non-technical users.
Robust data strategy: A well-planned data strategy enhances the accessibility, scalability, and reliability of AI, turning data into a strategic asset for decision-making. It ensures efficient data management and aligns with business goals, enabling high-quality data pipeline for AI models and seamless integration to boost AI adoption across organisations.
Transforming the work, workplace and empowering the workforce
To ensure everyone in the organisation is leveraging the power of AI, it is very important to understand the personas and the tasks they are doing in that specific industry and enterprise context. We need to reimagine the work, what will be done by AI and what will be done by human, design the human and AI interactions.
AI agents can be designed using agentic framework to autonomously perform complex tasks. These agents, combining LLMs, tools, and memory, handle dynamic workflows intelligently. Notable frameworks include Crew AI, Langchain, LangGraph, and Microsoft AutoGen.
These frameworks integrate machine learning, NLP, and various tools to create intelligent agents that understand context, learn from data, and interact seamlessly. By integrating with enterprise applications, they automate workflows, optimise processes, and deliver personalised experiences. For instance, a large auditing firm uses agents to automate data collection and report preparation.
As roles evolve, a comprehensive re-skilling plan is needed to enhance human potential with AI, enabling auditors, doctors, and others to excel in their fields.
Use the power of AI responsibly
Democratised AI can deliver benefits at scale, but if not managed correctly, could lead to disastrous consequences. For example, if a flawed algorithm is used by a large developer community, its potential for damage increases significantly. Organisations must establish robust measures to ensure that democratisation is accompanied by responsible use of AI and data.
Organisations must understand how users plan to leverage AI, whether for querying, content generation, decision-making, or model building. Train business users to build and use AI solutions safely, emphasising responsible practices like avoiding bias in data labeling.
Organisations should train developers in secure coding practices and enforce rigorous testing to mitigate AI models’ vulnerabilities. Responsible AI is about handling data with consent, safeguarding data against unauthorised use or attack, protecting data privacy and confidentiality, and respecting ownership rights.
As AI adoption increases, so will the ethical concerns surrounding its use. High quality training data (clean, complete, consistent, accurate and unbiased), and fairness and accountability check during development will reduce the likelihood of false or biased algorithmic outcomes. While enabling access to developers, organisations should provide guidance on how to build transparent and explainable AI models. Last but not least, there should always be a human in the loop overseeing AI development. Because after all, democracy is about people power.
