How Modern AI is Reshaping Industries

Artificial Intelligence (AI) isn't just a buzzword anymore—it's the driving force behind business transformation. From automating banking operations to deciphering unstructured data, AI-driven platforms are fundamentally reshaping industries.

Deepak Dastrala, Chief Technology Officer - India at IntellectAI, offers a front-row perspective on how AI platforms are evolving, the innovations fueling them, and the trends shaping their future. In this exclusive conversation, he walks us through the AI revolution and why businesses can no longer afford to ignore it.

AI-Driven Platforms: The Future of Business Intelligence

"At a high level, businesses seek technology that makes processes faster, cheaper, and better. That’s where AI plays a critical role." – Deepak Dastrala

The core function of AI-driven platforms is to transform raw data into actionable insights. But how are modern AI platforms achieving this? The secret lies in a combination of machine learning, data fabric architectures, and intelligent automation.

The AI-Driven Transformation Journey

Deepak’s journey in AI began nearly a decade ago when he took on the role of leading strategy and innovation at Intellect. Back then, AI was still in its infancy, and businesses were just beginning to explore its potential. Fast forward to today, and AI-driven platforms are making major strides, particularly in the Banking, Financial Services, and Insurance (BFSI) sector.

How did IntellectAI build its AI capabilities?

•    Understanding Market Needs: The team recognized that AI could fill gaps in financial operations, especially in risk assessment and automation.
•    Experimentation in Commercial Insurance: They began by applying AI in commercial insurance, where privacy concerns were manageable, before expanding into broader BFSI solutions.
•    Building AI at Scale: AI isn't just a data science problem—it’s also an engineering challenge. IntellectAI developed a scalable, cloud-agnostic AI platform to integrate with existing enterprise systems.

The Roadblocks to AI Adoption

Despite its potential, AI adoption isn't a smooth ride. Deepak highlights a key challenge:

"Even deterministic digital transformation solutions face resistance. AI-driven solutions are probabilistic, meaning even with a 98% confidence level, businesses still question the remaining 2%."

In simpler terms, businesses like certainty. If AI predicts an outcome with 98% accuracy, they still worry about the 2% uncertainty. Overcoming this mindset is crucial for AI adoption at scale.

Key Trends Shaping AI-Driven Platforms

1. The Rise of Unstructured Data Utilization

Here's an eye-opener: 80% of enterprise data is unstructured. That includes financial reports, emails, contracts, and even casual customer interactions. Traditional analytics struggle with this, but AI-driven platforms are now equipped to extract and process insights from these massive, unstructured data pools.

2. Intelligent Agents: The API Revolution for AI

Think about how APIs transformed the way software applications communicate. AI is heading in the same direction. Instead of being standalone systems, AI-driven platforms will function like intelligent agents, offering expertise as a service—just like an API call.

3. Operational Cost Reduction in BFSI

Financial institutions spend 40–46% of their budget on operations, even after heavy investments in technology. AI has the potential to drastically cut operational costs by automating repetitive tasks, minimizing errors, and streamlining workflows.

Case Study: AI in Responsible Investing

One of IntellectAI’s flagship projects involved a sovereign wealth fund managing $1.7 trillion in assets. 

Their challenge?

•    Tracking over 9,200 companies across multiple industries.
•    Extracting Environmental, Social, and Governance (ESG) insights from millions of reports and regulatory filings.
•    Maintaining data accuracy above 90% for responsible investment decisions.

How did AI solve this?

•    Data Aggregation: Structured and unstructured data were consolidated into a unified datastore using MongoDB.
•    Advanced Search Capabilities: Vector databases improved semantic retrieval, making it easier to find relevant ESG insights.
•    Automated Decision Support: AI models processed vast datasets in real time, flagging risks and opportunities for investment managers.

The result? Better investment decisions with significantly reduced manual effort.

Security & Compliance in AI Systems

With governments tightening data regulations, AI platforms need to evolve.

Deepak highlights how evolving laws like India’s DPDP Act (Data Protection & Digital Privacy Act) and ISO 42001 are pushing companies to rethink security strategies.

How AI Platforms Ensure Data Security

•    Data Anonymization & Masking: Protects sensitive customer information.
•    Role-Based Access Control (RBAC): Ensures users only see the data they need.
•    Toxicity Detection & AI Compliance: Identifies and mitigates biased or harmful AI outputs.
•    Field-Level Encryption: Adds an extra layer of security for sensitive datasets.

In Deepak’s words:

"AI platforms must take a platform-first approach to security rather than relying on ad-hoc fixes."

What’s Next? Future Innovations in AI & Data Management

The AI industry is evolving at breakneck speed. Here are three game-changing advancements on the horizon:

1. Vector Databases: The Next Leap in Data Storage

Traditional databases struggle with unstructured data. Vector databases allow structured, semi-structured, and unstructured data to coexist, making AI faster and more context-aware.

IntellectAI utilizes MongoDB’s features, such as time series collections, to efficiently process unstructured and semi-structured data from company reports spanning multiple years. This capability enables the extraction of key performance metrics and trends, providing deeper compliance insights and enhanced decision-making.

2. Agent-Native Infrastructure: AI’s Next Evolution

Just as cloud computing moved from monolithic applications to microservices, AI will move from centralized models to agent-native infrastructure—where AI agents can operate autonomously, at scale.

3. AI Governance & Observability

As AI systems handle more business-critical decisions, companies will need:

•    Audit Trails: Tracking how and why an AI decision was made.
•    Real-Time Benchmarking: Ensuring models remain accurate and relevant.
•    Regulatory Compliance Frameworks: Keeping AI in line with industry regulations.

Future-Proofing AI Platforms

As a final takeaway, Deepak shares three golden rules for building resilient, future-proof AI platforms:
1.    Schema Flexibility: The ability to handle evolving data structures without breaking workflows.
2.    Modular Architecture: Allowing AI models to be replaced or upgraded without major re-engineering.
3.    Observability & Governance: AI decisions must be traceable, explainable, and continuously monitored.
"Future-proofing AI is about making it scalable, adaptable, and secure while keeping pace with rapid advancements."

Final Thoughts

AI-driven platforms are no longer optional—they are becoming the backbone of digital enterprises. As Deepak highlights, businesses that fail to adapt will fall behind in efficiency, agility, and cost-effectiveness.

With the rise of intelligent automation, vector databases, and agent-native AI, the next decade will be about scaling AI from experiments to enterprise-wide solutions.

And as AI becomes smarter, the real question isn’t “Should businesses adopt AI?”—it’s “How fast can they keep up?”