Today, the rise of GenAI and copilots from various tool vendors and organizations has generated significant interest, driven by advancements in NLP, ML, computer vision, and other deep learning models. For CIOs, CDOs, and data leaders, this shift underscores a critical point: AI-powered technologies must be responsible, transparent, ensure privacy, and free of bias to truly add business value.

Since AI and GenAI both depend on data for fuel, it cements the need for the availability of the right data with the right quality, trust, and compliance. Without strong data governance, organizations risk AI models that reinforce bias, misinterpret data, or fail to meet regulatory requirements. This further underscores the importance of Data Governance as a critical discipline that serves as a guiding light.

Hence, ‘The lighthouse remains a beacon amidst shifting tides’ – In today’s context, this metaphor reflects the challenges faced by both data-driven and AI-driven enterprises. The landscape of data generation, usage, and transformation is constantly evolving, presenting new complexities for organizations to navigate. While data governance is not new, with many a change in weather (data) patterns and the infusion of AI across industries, it has grown increasingly relevant, acting as the foundation on which AI can be governed and enabled.

At Tiger Analytics, we are constantly exploring new opportunities to optimize the way we work. Take, for example, enterprises where time-to-market is critical, product vendors have developed copilots using GenAI. We have also observed many initiatives among our Fortune 100 clients leveraging models and various AI elements to achieve a faster time-to-market or develop new offerings. Many of these projects are successful, scalable, and continue to drive efficiency. However, the inevitable question arises: How do we govern AI?

What are the biggest challenges in Data Governance – Answering key questions

Data governance is not just about compliance — it is essential to enhance data quality and trustworthiness, efficiency, scalability, and produce better AI outcomes. Strong governance practices (process, op model, R&R) empower enterprises to unlock the full potential of their data assets.

Below are a few important questions that stakeholders across the enterprise, including CxOs, business leaders, Line of Business (LoB) owners, and data owners, are seeking to answer today. As organizations strive towards data literacy and ethical AI practices, these questions highlight the importance of implementing governance strategies that can support both traditional data management and emerging AI risks.

• Who is in charge of the model or the data product that uses my model?
• Who can control (modify/delete/archive) the dataset?
• Who will decide how to control the data and make key decisions?
• Who will decide what is to be controlled in the workflow or data product or model that my data is part of?
• What are the risks to the end outcome if intelligence is augmented without audits or controls, or quality assurance?
• Are controls for AI different from current ones or can existing ones be repurposed?
• Which framework will guide me?
• Is the enterprise data governance initiative flexible to accommodate my AI risks and related work?
• With my organization in the process of becoming data literate and ensuring data ethics, how can AI initiatives take advantage of the same?
• Is user consent still valid in the new AI model, and how is it protected?
• What are the privacy issues to be addressed?

Let’s consider an example. A forecasting model is designed to help predict seasonal sales to launch a new apparel range targeted at a specific customer segment within an existing market. Now, assume the data is to be sourced from your marketplace and there are readymade data products that can be used – How do you check the health of the data before you run a simulation? What if you face challenges such as ownership disputes, metadata inconsistencies, or data quality issues? Is there a risk of privacy breaches if, for example, someone forgets to remove sample data from the dataset?

This is why Data Governance (including data management) and AI must work in tandem, even more so when we consider the risk of non-compliance, for which the impact is far greater. Any governance approach must be closely aligned with data governance practices and effectively integrated into daily operations. There are various ways in which the larger industry and we at Tiger Analytics are addressing this. In the next section, we take a look at the key factors that can serve as the foundation for AI governance within an enterprise.

Untangling the AI knot: How to create a data governance framework for AI

At Tiger Analytics, we’ve identified seven elements that are crucial in establishing a framework for Foundational Governance for AI – we call it HEal & INtERAcT. We believe a human-centric and transparent approach is essential in governing AI assets. As AI continues to evolve and integrate into various processes within an organization, governance must remain simple.

Rather than introducing entirely new frameworks, our approach focuses on accessible AI governance in which existing data governance operations are expanded to include new dimensions, roles, processes, and standards. This creates a seamless extension rather than a separate entity, thereby eliminating the complexities of managing AI risks in silos and untangling the “AI knot” through smooth integration.

The seven elements ensure AI governance remains transparent and aligns with the larger enterprise data governance strategy, influencing processes, policies, standards, and change management. For instance, Integrity and Trustworthiness reinforce reliability in model outputs and help create a trustworthy output that ensures privacy, while Accountability and Responsibility establish clear ownership of AI-driven decisions, ensuring compliance and ethical oversight. As AI introduces new roles and responsibilities, governance frameworks are revised to cover emerging risks and complexities like cross-border data, global teams, mergers, and varying regulations.

In addition, the data lifecycle in any organization is dependent on data governance. AI cannot exist without enterprise data. Synthetic data can only mimic actual data and issues. Therefore, high-quality, fit-for-purpose data is essential to train AI models and GenAI for more accurate predictions and better content generation.

Getting started with AI governance

Here is how an enterprise can begin its AI governance journey:

• Identify all the AI elements and list out every app and area that uses it
• What does your AIOps look like, and how is it being governed?
• Identify key risks from stakeholders
• Map them back to the principles
• Define controls for the risks identified
• Align framework with your larger data governance strategy
• Enable specific processes for AI
• Set data standards for AI
• Tweak data policies for AI
• Include an AI glossary for Cataloging and Lineage, providing better context
• Data observability for AI to set up proactive detection for better model output and performance

Essentially, Enterprise DG+AI principles (framework) along with Identification & Mitigation strategies and Risk Controls, will pave the way for efficient AI governance. Given the evolving nature of this space, there is no one-size-fits-all solution. Numerous principles exist, but expert guidance and consulting are essential to navigate this complexity and implement the right approach.

The Road Ahead

AI has moved from science fiction to everyday reality, shaping decisions, operations, and personalized customer experiences. The focus now is on ensuring it is transparent, ethical, and well-governed. For this, AI and data governance must work in tandem. From customer churn analysis to loss prevention and identifying the right business and technical metrics, managing consent and privacy in the new era of AI regulations, AI can drive business value — but only when built on a foundation of strong data governance. A well-structured governance program ensures AI adoption is responsible and scalable, minimizing risks while maximizing impact. By applying the principles and addressing the key questions above, you can ensure a successful implementation, enabling your business to leverage AI for meaningful outcomes.

So while you ponder on these insights, ’til next time — just as the T800 said, “I’ll be back!”

The post The Data Leader’s Guide to Responsible AI: Why Strong Data Governance Is Key to Mitigating AI Risks appeared first on Tiger Analytics.

The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally. India’s digitization journey and the introduction of the Aadhaar system in 2010 – the largest biometric identity project in the world – has opened up new venues for AI and data analytics. The interlinking of Aadhaar with banking systems, the PDS, and several other transaction systems allows greater visibility, insights, and metrics that can be used to bring about improvements. Besides using these to raise the quality of lives of citizens while alleviating disparities, AI can support more proactive planning and formulation of policies and roadmaps. Industry experts concur a trigger and economic growth spurt, opining that “AI can help create almost 20 million jobs in India by 2025 and add up to $957 billion to the Indian economy by 2035.”

The current state of AI in India

The Indian government, having recently announced the “AI for All” strategy, is more driven than ever to nurture core AI skills to future-proof the workforce. This self-learning program looks to raise awareness levels about AI for every Indian citizen, be it a school student or a senior citizen. It targets meeting the demands of a rapidly emerging job market and presenting opportunities to reimagine how industries like farming, healthcare, banking, education, etc., can use technology. A few years prior, in 2018, the government had also increased its funding towards research, training, and skilling in emerging technologies by 100% as compared to 2017.

The booming interest has been reflected in the mushrooming of boutique start-ups across the country, as well. With a combined value of $555 million, it is more than double the previous year’s figure of $215 million. Interestingly, analytics-driven products and services contribute a little over 64% of this market -clocking over $355 million. In parallel, the larger enterprises are taking quantum leaps to deliver AI solutions too. Understandably, a large number of them use AI solutions to improve efficiency, scalability, and security across their existing products and services.

Current challenges of making India-centric AI

There is no doubt that AI is a catalyst for societal progress through digital inclusion. And in a country as diverse as India, this can set the country on an accelerated journey toward socio-economic progress. However, the socio, linguistic and political diversity that is India also means more complex data models that can be gainfully deployed within this landscape. For example, NLP models would have to adapt to text/language changes within just a span of a few miles! And this is just the tip of the iceberg as far as the challenges are concerned.

Let’s look at a few of them:

• The deployment and usage of AI have been (and continues to be) severely fragmented without a transparent roadmap or clear KPIs to measure success. One of the reasons is the lack of a governing body or a panel of experts to regulate, oversee and track the implementation of socio-economic AI projects at a national level. But there’s no avoiding this challenge, considering that the implications of AI policy-making on Indian societies may be irreversible.
• The demand-supply divide in India for AI skills is huge. The government initiatives such as Startup India as well as the boom in AI-focused startups have only contributed to extending this divide. The pace of getting a trained workforce to cater to the needs of the industry is accelerating but unable to keep up with the growth trajectory that the industry finds itself in. Large, traditionally run institutions are also embracing AI-driven practices having witnessed the competitive advantage it brings to the businesses. This has added to the scarcity that one faces in finding good quality talent to serve today’s demand.
• The lack of data maturity is a serious roadblock on the path to establishing India-centric AI initiatives – especially with quite a few region-focused datasets being currently unavailable. There is also a parity issue with quite a few industry giants having access to large amounts of data as compared to the government, let alone start-ups. There is also the added challenge of data quality and a single source of truth that one can use for AI model development
• Even the fiercest AI advocates would admit that its security challenges are nowhere close to being resolved. There is a need for security and compliance governance protocols to be region-specific so that unique requirements are met and yet there is a generalisability that is required to rationalize these models at the national level.
• There is also a lot of ongoing debate at a global level on defining the boundaries that ethical AI practices will need to lean on. Given India’s diversity, this is a challenge that is magnified many times over

Niche areas where AI is making an impact

Farming

The role of AI in modern agricultural practices has been transformational – this is significant given that more than half the population of India depends on farming to earn a living. In 2019-2020 alone, over $1 billion was raised to fuel agriculture-food tech start-ups in India. It has helped farmers generate steadier income by managing healthier crops, reducing the damage caused by pests, tracking soil and crop conditions, improving the supply chain, eliminating unsafe or repetitive manual labor, and more.

Healthcare

Indian healthcare systems come with their own set of challenges – from accessibility and availability to quality and poor awareness levels. But each one represents a window of opportunity for AI to be a harbinger of change. For instance, AI-enabled platforms can extend healthcare services to low-income or rural areas, train doctors and nurses, address communication gaps between patients and clinicians, etc. Government-funded projects like NITI Aayog and the National Digital Health Blueprint have also highlighted the need for digital transformation in the healthcare system.

BFSI

The pandemic has accelerated the impact of AI on the BFSI industry in India, with several key processes undergoing digital transformation. The mandatory push for contactless remote banking experience has infused a new culture of innovation in mission-critical back-end and front-end operations. A recent PwC-FICCI survey showed that the banking industry has the country’s highest AI maturity index – leading to the deployment of the top AI use cases. The survey also predicted that Indian banks would see “potential cost savings up to $447 billion by 2023.”

E-commerce

The Indian e-commerce industry has already witnessed big numbers thanks to AI-based strategies, particularly marketing. For retail brands, capturing market share is among the toughest worldwide – with customer behavior being driven by a diverse set of values and expectations. By using AI and ML technologies – backed by data science – it would be easier to tap into multiple demographics without losing the context of messaging.

Manufacturing

Traditionally, the manufacturing industry has been running with expensive and time-consuming manually driven processes. Slowly, more companies realize the impact of AI-powered automation on manufacturing use cases like assembly line production, inventory management, testing and quality assurance, etc. While still at a nascent stage, AR and VR technologies are also seeing adoption in this sector in use cases like prototyping and troubleshooting.

3 crucial data milestones to achieve in India’s AI journey

1) Unbiased data distribution

Forming India-centric datasets starts with a unified framework across the country so that no region is left uncovered. This framework needs to integrate with other systems/data repositories in a secure and seamless manner. Even private companies can share relevant datasets with government institutions to facilitate strategy and policy-making.

2) Localized data ownership

In today’s high-risk data landscape, transferring ownership of India-centric information to companies in other countries can lead to compliance and regulatory problems. Especially when dealing with industries with healthcare or public administration, it is highly advised to maintain data control within the country’s borders.

3) Data ethics and privacy

Data-centric solutions that work towards improving human lives require a thorough understanding of personal and non-personal data, matters of privacy, and infringement among others. The responsible aspect to manage this information takes the challenges beyond the realms of deployment of a mathematical solution. Building an AI mindset that raises difficult questions about ethics, policy, and law, and ensures sustainable solutions with minimized risks and negative impact is key. Plus, data privacy should continue to be a hot button topic, with an uncompromising stance on safeguarding the personal information of Indian citizens.

Final thoughts

India faces a catch-22 situation with one side of the country still holding to its age-old traditions and practices. The other side embraces technology change, be it using UPI transfers, QR codes, or even the Aarogya Setu app. But sheer size and diversity of languages, cultures, and politics dictate that AI will neither fail to find areas to cause a profound impact nor face fewer challenges while implementing it.

As mentioned earlier, the thriving startup growth adds a lot of fuel to AI’s momentum. From just 10 unicorns in India in 2018, we have grown to 38. This number is expected to increase to 62 by 2025. In 2020, AI-based Indian startups received over $835 million in funding and are propelling growth few countries can compete with. AI is a key vehicle to ring in the dawn of a new era for India-centric AI– an India which despite the diversity and complex landscape, leads the way in the effective adoption of AI.

This article was first published in Analytics India Magazine.

The post Why India-Targeted AI Matters: Exploring Opportunities and Challenges appeared first on Tiger Analytics.