Kunal Purohit joins the podcast to discuss scaling AI for enterprise applications. He outlines the four stages of digital transformation and highlights the technical and cultural challenges that can slow down implementation. Additionally, he addresses common pitfalls that organizations face when adopting AI technologies.
**Introduction to Tech Mahindra**
At Tech Mahindra, my dual role involves harnessing emerging technologies to develop innovative solutions that our clients can implement to drive substantial business benefits. We guide organizations as they transition from traditional operating models to digital and cognitive frameworks. By leveraging insights and the power of AI, we help deliver outcomes more swiftly, enhancing client satisfaction.
I also serve on the Executive Council, where we explore new ideas within the company to cultivate new business opportunities. Over the past three to four years, we have successfully launched several initiatives aimed at creating long-term value for Mahindra.
**Integrating AI with Digital Transformation for Revenue Growth**
Digital transformation has become a significant focus for organizations, and AI is revolutionizing this concept. Enterprises have long possessed data but are now starting to recognize its potential in generating actionable insights. This shift enables better decision-making and personalized customer engagement, leading to improved operational efficiency and revenue growth.
The infusion of AI across the digital landscape—from infrastructure to customer engagement—has accelerated significantly in recent years. Emerging techniques such as generative and discriminative AI have become more accessible, increasing their applicability across different sectors.
**Client AI Journey Maturity**
We work closely with enterprises to assess their maturity in AI adoption. Initially, many organizations established Centers of Excellence (CoEs) for automation, gradually evolving into Intelligent Automation teams. This transition has allowed them to explore AI-enabled use cases, leading to deeper integrations of AI and machine learning in their operations.
Currently, most enterprises find themselves between the second and third maturity phases as they seek to leverage generative AI to enhance productivity. Some progressive organizations are already implementing these advanced AI techniques, but struggles with scalability persist.
**Challenges in Scaling AI and Potential Solutions**
Numerous factors hinder companies from effectively scaling AI. While technical challenges exist, cultural aspects—such as a data-driven mindset—are equally significant. For instance, if decision-making is dominated by intuition rather than data, the adoption of AI may falter. Identifying champions within organizations who can drive success in AI projects can help cultivate a more data-centric culture.
Fear of the unknown also plays a role in stalling progress. Many enterprises hesitate to experiment with new AI technologies, concerned about potential failures. However, early experimentation can yield valuable lessons and build confidence.
On the technical front, challenges often arise when attempting to replicate models across different environments, particularly when varying levels of technology infrastructure are involved. Additionally, keeping data updated and managing models can be daunting tasks that require a systematic approach.
The people aspect cannot be ignored either. A shortage of talent proficient in modern architectural thinking and practical AI applications can limit a company’s progress. Organizations must work on bridging this skills gap to ensure effective deployment of AI initiatives. Furthermore, there’s an ongoing need to balance the costs associated with AI development against the anticipated outcomes.
**Engaging Executives with Valid Concerns**
To address these challenges, we have launched the Generative AI Studio, an initiative designed to assist enterprises in exploring generative AI without heavy initial investments. By providing access to a suite of over 30 features—including code generation and content creation—we empower companies to experiment and build understanding around generative AI while mitigating risks.
From our experience, enterprises are beginning to appreciate the vital role that testing use cases and advancing maturity play in the successful deployment of AI solutions.
**Successful Use Cases of AI Implementation**
We have seen a range of successful applications of AI across various sectors. Horizontal use cases often focus on enhancing knowledge management and improving communication with stakeholders. For instance, in a recent project with a resort company, the implementation of generative AI significantly improved response accuracy from 63% to 91%, facilitating better engagement with customers’ inquiries.
In specialized industries, such as oil and gas, generative AI is being deployed to streamline contract generation processes, reducing reliance on expensive legal resources. For example, we helped a large oil and gas company automate the creation of contract templates, achieving significant cost savings while still ensuring accuracy through human oversight.
**Common Missteps in AI Initiatives**
Enterprises often hesitate to begin AI initiatives due to concerns over accountability for potential failures. Additionally, many underestimate the resources required to achieve their desired outcomes. To help mitigate these issues, we encourage clients to pilot initial projects to build confidence and skill—laying the groundwork for successful broader implementations.
**Supporting Innovation through Startup Incubation**
Through our Garage4.0 initiative, we support startup ventures that align with our technological goals. We aim to incubate innovative ideas that can either scale independently or be integrated back into Tech Mahindra. This approach enables us to foster value creation, targeting high-growth segments while providing potential for external investment rounds.
Our mission is clear: create impactful solutions that leverage India’s growing tech landscape, ultimately benefiting both the Mahindra Group and our clients.
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