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AI has emerged as a transformative force for businesses across industries. Since the release of ChatGPT in December 2022, we’ve witnessed the rapid ascent of Gen AI, marking a pivotal moment in AI’s evolution. Many organizations believe AI could provide a significant competitive edge, with Gen AI potentially increasing global GDP by 3% to 7% over the next decade and adding trillions to the economy. This includes various subfields such as machine learning, natural language processing (NLP), computer vision (CV), robotics, and now Generative AI.
According to a 2024 CEO survey, 59% of CEOs believe AI will significantly impact their industries in the next three years, a sharp rise from 21% in 2023. Additionally, 87% of CEOs view AI’s benefits as outweighing its risks, making Generative AI and AI/ML services top tech priorities.
Artificial Intelligence services includes traditional techniques like Predictive AI and Causal AI. Predictive AI (or Analytical AI) uses algorithms to forecast future events based on historical data. Causal AI determines the causes and effects of events and is used in IT operations and AIOps.
Generative AI (Gen AI) services, leveraging foundational models such as large language models (LLMs), creates new content—text, code, images, audio, and more—by learning patterns from large datasets. This technology builds on existing data to generate novel outputs.
Gen AI might not be suitable for all use cases. It must be used complementary to Traditional or Discriminative AI. Our Gen AI use cases – 6C Framework – gives a broader direction for identifying use cases for Gen AI. According to our 6C Framework, its use cases fall under six broader categories.
Hyper-modal AI combines Generative AI, Predictive AI, and Causal AI to address complex use cases. For example, in drug discovery, Predictive AI identifies potential drugs, while Causal AI analyses gene interactions to find the most effective treatment.
IT observability and operations tool maker Dynatrace recently announced their Hyper-model AI tool Davis. Historically, Davis has utilised Causal AI and Predictive AI for many AIOps use cases. Now they have augmented Davis with Generative AI capabilities, including the capability of interacting with the tool with natural language, Copilot AI Agent, and generating code for automation workflows.
Grant Thornton Bharat provides a comprehensive suite of GenAI and machine learning services designed to support organisations at every phase of their AI adoption journey. Whether you are in the early stages of exploring these technologies or working towards advanced integration, our experts deliver strategic insights and solutions to help you leverage AI for enhanced business outcomes.
Having a well thought through architecture stack is important for adopting and scaling AI and Gen AI programs across your organization. The AI architecture stack comprises of specialised infrastructure for Gen AI, cloud and data services, model as a service layer (provides a catalogue of foundational models from different providers, and model customisation), middleware (model orchestration tools and APIs), and AI powered Intelligent App. Read our whitepaper to know more about the AI architecture stack.
As AI systems and agents are being used in critical and impactful use cases from identifying the right candidate for a job to underwriting insurance to different healthcare and life sciences, use cases ensuring that these systems are unbiased, fair, safe, secure and reliable should be the topmost priority. These systems should not violate intellectual property (IP) and should abide by AI and data-related laws, regulations and compliances (e.g. the newly formed EU AI Act, Artificial Intelligence and Data Act (AIDA by Govt of Canada), GDPR, etc). Overall, AI systems should protect and reinforce positive human values.
Our responsible Artificial Intelligence services framework covers all aspects of AI ethics and governance. We utilise this framework in all our Gen AI and AI/ML projects.
We can help you end-to-end, starting from AI readiness assessment to identifying and prioritising the optimum use cases for you to Gen AI POC to design and build. Our AI ethics and governance services include all aspects of Responsible AI, FinOps for AI workloads and Sustainable AI.
We can help you end-to-end, starting from AI readiness assessment to identifying and prioritising the optimum use cases for you to Gen AI POC to design and build. Our AI ethics and governance services include all aspects of Responsible AI, FinOps for AI workloads and Sustainable AI.
We assess your readiness for Gen AI services by evaluating your people, processes, technology (AI, data, cloud, security), and operational capabilities. This helps to develop a strong business case for AI adoption.
After identifying the business case and use cases, we assist in conducting a technical proof of concept, which can range from a few weeks to several months.
We support the architecture, design, build, and testing phases across Gen AI, AI/ML, cloud, and data platforms.
Enhance your existing applications with new intelligent features powered by Gen AI and Discriminative AI.
Our intelligent automation and RPA services help you automate processes and applications.
Beyond technology, it is crucial to consider aspects such as privacy, bias, trust, risk, security, data protection, and sovereignty to maximise AI’s benefits.
XAI helps demystify the decision-making processes of AI service models, providing transparency to users and stakeholders.
As the costs associated with Gen AI services rise, we help analyse, optimise, and govern your AI/ML, cloud, and data costs through a full-stack approach.
We ensure that your AI initiatives are sustainable, benefiting your company, clients, employees, partners, the broader ecosystem, and the planet.
