India is Adopting AI Services Especially in IT Sector

What are the key trends you observe in the evolving landscape of Data and AI, in the year 2025 and beyond?

Technology is evolving so rapidly that predicting long-term trends feels like aiming at a moving target. But one thing is certain—AI isn’t just another tech fad. Unlike many trends that lose steam after the initial buzz, AI is here to stay. Many future tech trends will either be directly powered by AI or closely connected to it. AI adoption is only going to grow. And unlike today, where AI’s benefits often seem overhyped, the impact will soon be measurable and tangible.

To break it down, I see three major categories of trends emerging:

• AI advancements
• Data evolution to support AI
• Broader tech trends influenced by AI

Trends in AI can be categorized into:

• Industry & domain-specific AI models or vertical AI: typically uses Small language models with fewer but industry-specific parameters.
• Business-Driven AI: A large part of AI today is still technology, and the benefits of AI for business are not easily explained. Everybody wants to adopt AI without having clear information on end gains. In 2025 and beyond, business teams will be able to understand AI better, define concrete goals and define value add.
• Democratization of AI: AI will become accessible to a broader range of people, including those without specialized technical expertise in data science, machine learning, or programming. People will be able to leverage AI for problem-solving, innovation, and decision-making without requiring deep technical skills.
• AI Autonomy: AI will be able to exhibit proactive decision-making capabilities without human inputs, especially in markets like self-driven automobiles. Cognitive AI will drive robotic industries.
• AI will drive sustainability including the reduction of carbon footprint & energy-efficient architectures. Standards will be built to ensure ethical usage and commonality of usage.
• New frontier models (beyond the Chatgpts and Geminis of today) will continue to evolve. There will not be any sphere of activity where AI will not penetrate. 

As a Product Engineering organization, Experion will use AI not only as a service to its clientele but also to bring efficiency into our Product engineering processes and day-to-day activities.

Success in AI is driven by the availability of data in right form. Some key Data Management trends that will be seen in 2025 and beyond include:

• Strong focus on Data Quality, Data Governance, Democratization of Data & Data security. Concepts like Data mesh will help de-centralize ownership while Data Fabric provides easy-to-use simple interfaces enabling self-service. The right quality of data is important for AI to make the right decisions.
• Synthetic data which is data generated to replicate real data types will be needed to drive AI as the availability of real data will not be enough to train models.
• The need for high-speed Data will drive technologies like Edge Computing, Advanced Parallelization (Lakehouse architecture), Exascale Computing, In-Memory Computing & Stream Processing. 

How do customer expectations in the segments of Data and AI vary across different markets, and what are the unique aspects of customer demands in the Indian market?

Data & AI projects at Experion are primarily based out of the US, Australia, Europe and Japan. Customers across all these markets have prioritized AI and Data support for AI for 2025. However, there is a distinct difference in customer expectations. 

In the U.S., since it is a very fast-moving market, the expectation is for highly innovative and advanced AI solutions. There is a strong focus on AI-driven automation, personalization, and self-service. It’s a priority in this region that AI easily integrates with their current systems. While in Europe, customers are focused on cutting-edge data and AI solutions, Data privacy and ethical use of Data and AI are priorities too. There is a lot of focus on sustainability as well. 

On the other hand, Japanese customers demand the best-in-class data and AI solutions. Japanese customers are also driven by cultural sensitivities and the focus is more on areas like robotics. Lastly, customers in Australia seem to value operational efficiency, cost reduction, and data-driven decision-making. They have clarity on how AI can help their businesses. Data Sovereignty is of extreme importance here.

India notably is buzzing with AI potential, thanks to a thriving startup scene, a push for digitalization, and solid government support. But when it comes to AI expectations, the landscape is a bit different compared to more mature markets like the US and Europe.

●    Clarity is Still Evolving: Indian companies are eager to jump into AI, but many still lack a clear roadmap or specific goals. There’s a lot of excitement, but the long-term vision isn’t always well-defined.
●    Data Privacy is a Work in Progress: Trust around data use is still shaky. India is working on building strong data privacy laws (like the Personal Data Protection Bill) but navigating the regulatory environment can be tricky.
●    Data Availability Issues: High-quality, reliable data isn’t always easy to come by. Advanced data management practices are still catching up.
●    Cost is a Big Factor: Infrastructure and data storage costs are high, so Indian companies often prioritize budget-friendly solutions over high-end, ultra-efficient ones.
That said, India has been quicker to adopt AI than many other technologies, especially in IT services, large corporations, and government agencies. However, challenges like limited research funding, unclear policies, privacy concerns, and reaching rural areas still need to be addressed.

What are some of the challenges while working with clients from different countries in the Data and AI domain? Could you share examples of how these challenges differ regionally?

Experion’s Data and AI projects span across the US, Australia, Europe, and Japan. While the global demand for AI solutions is strong, each region brings its own set of unique challenges and expectations. Here’s how these challenges map out geographically:

US clients, experienced in Data and AI, have high expectations for mature, high-speed solutions that address not only project goals but also infrastructure, compliance, and data privacy. While pricing is less of a concern, speed is critical, and our team excels in delivering quickly. Additionally, US clients expect continuous innovation throughout the project lifecycle, ensuring our solutions remain fresh and forward-thinking.
European clients tend to take longer to initiate projects compared to the US, spending more time finalizing scopes, building business cases, and defining goals for Data and AI initiatives. GDPR compliance is non-negotiable, with data privacy and consent being top priorities, making AI projects more complex. Regional diversity across Europe also means solutions must be tailored to meet local needs. Additionally, European clients prioritize flexibility and overall quality, often valuing these more than strict adherence to timelines or budgets, which requires our teams to stay agile and ready to adapt.

Australian clients are cautious and prefer starting with Proof of Concepts (POCs) or Minimum Viable Products (MVPs) to build trust before committing to full-scale projects. They have a strong ROI-driven mindset, requiring clear, data-backed business cases to demonstrate tangible benefits. Additionally, they favor a hands-on, iterative approach to development, which, while slower, aligns well with our team's collaborative working style and is crucial for project success.

Japanese clients prioritize reliability and proven results over cutting-edge innovations, often requiring Proof of Concepts (POCs) to build trust before scaling up. Due to cultural and communication barriers, execution can be challenging, and Japanese clients expect frequent progress updates, demos, and continuous engagement to build confidence. 

Navigating these regional differences requires adaptability and cultural understanding. Whether it’s delivering high-speed, innovative solutions for the US, balancing flexibility and compliance in Europe, building trust in Australia, or ensuring reliability and communication in Japan—Experion’s teams stay agile, proactive, and client-focused to succeed globally.

How do you address the diverse challenges and expectations in various regions, and what effective strategies ensure customer satisfaction?

At Experion, we follow a unified framework for our Data & AI programs, but we know that one size doesn’t fit all. With nearly two decades of experience across global markets, we’ve learned how to adapt this framework to meet the unique needs of each region and client.

Our Core Framework

1. Understanding the Need: We start by deeply understanding the client’s business goals and project requirements. We recommend a discovery phase to define the scope if details are unclear.
2. Accelerators & Solution Ideas: We propose pre-built accelerators and solution ideas to fast-track delivery. If needed, we suggest developing a Proof of Concept (POC) or Minimum Viable Product (MVP) to validate the solution.
3. Tailored Delivery Approach: Once the approach is finalized, we choose the best delivery method—ideally a rapid Agile model—to ensure flexibility and efficiency.

Region-Specific Customization

In the US, clients operate in a fast-paced environment and expect rapid, innovative solutions. When project details are unclear, we often recommend a discovery phase followed by a POC to align expectations. However, US clients also expect a complete, high-level delivery plan that outlines how we will deliver efficiency, continuous innovation, and added value throughout the project. Special attention is given to managing timelines and costs, ensuring that our solutions comprehensively address infrastructure, data security, compliance, and privacy. Project reviews typically focus on staying within budget and meeting deadlines.

For Australian clients, building trust is a key part of the engagement. If the project scope lacks clarity, we initiate the relationship with a discovery phase or a POC to showcase the solution’s potential. Australian customers are highly ROI-focused, so every dollar spent is justified with clear, measurable outcomes. Status reviews emphasize proof of concept and the solution’s impact on business performance. A collaborative, hands-on approach is essential, and we actively involve clients throughout the development process to build trust and ensure alignment.

In Europe, especially in the UK, clients prioritize compliance, security, and privacy—particularly in adherence to regulations like GDPR. European clients typically engage in multiple rounds of discussions to carefully evaluate how our solutions meet their compliance and data security needs. This thorough, detail-oriented approach can lead to longer project initiation times, but it ensures clarity and alignment. During project reviews, compliance with data privacy and security standards is a primary focus, along with delivering the requested features and functionality.

For Japanese clients, effective communication and relationship-building are essential. To bridge cultural and language gaps, we include a Japanese team member to facilitate smoother collaboration and communication. Japanese clients often prefer to start with POCs and MVPs to validate the feasibility and effectiveness of solutions. However, they sometimes face challenges in defining project goals and expected outcomes, which requires us to provide continuous updates through regular demos and detailed progress reviews. We also pay close attention to formal communication protocols and respect hierarchical decision-making processes to maintain a smooth and respectful workflow.

While this framework guides our approach, we understand that every client is different. That’s why we remain flexible—further customizing our strategy to fit each client’s unique needs and business goals.

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