DEV Community: Chandani

AI has always been powerful. But the real leap is happening now. With agentic AI.

Chandani — Tue, 02 Sep 2025 07:02:43 +0000

Unlike traditional models that only respond, agentic AI is built to plan, reason, and act independently. It takes us from passive insights to proactive orchestration.

The potential across industries is staggering:

Healthcare: Agents that monitor patient data continuously, flag anomalies early, and suggest tailored care pathways for clinicians.
Finance: AI systems that don’t just detect fraud but anticipate suspicious patterns and recommend preventive actions before losses occur.
Energy & Utilities: Autonomous agents that balance grid demand in real time, optimising renewable inputs while preventing outages.
Education: Personalised learning agents that adapt lesson delivery in real time based on student engagement and comprehension.
Logistics & Supply Chain: Intelligent agents that not only track shipments but also re-route dynamically in response to delays, weather, or geopolitical risks.

As we head into 2025 and beyond, the conversation isn’t just about AI adoption. It’s about whether organisations are ready to trust AI as a decision partner—one that acts alongside humans to shape outcomes. Read more: https://linkly.link/2EXs6

🔮 Do you see agentic AI becoming a trusted collaborator in your domain—or is it still an experimental frontier?

The shelves are never static. Neither should your data strategy be.

Chandani — Mon, 01 Sep 2025 12:41:46 +0000

Retail today is a story of constant movement - new pack sizes, dynamic pricing, loyalty program shifts, and promotions that change faster than reporting cycles can capture.

The future isn’t about looking at stale quarterly dashboards. It’s about capturing the rhythm of the shelf in near-real time, across digital and physical touchpoints, and using that to re-wire decisions on assortment, pricing, and promotions.

The retailers and brands that will thrive are those who:

Track SKU-level shifts with depth and breadth
Combine share-of-shelf analytics with consumer sentiment signals
Refine demand forecasts using category seasonality and search behaviour

📈 Tomorrow’s winners won’t just measure what is selling. They’ll decode why and adapt before the shelf resets.

This page highlights how retail intel helps - https://linkly.link/2EWSv

👉 How ready is your organisation to move from “rear-view data” to real-time retail intelligence?

How Location Intelligence Elevates Retail Performance

Chandani — Fri, 29 Aug 2025 04:21:54 +0000

Location intelligence has become a strategic asset for retailers looking to thrive in a competitive landscape. It empowers them to make smarter decisions, ranging from where to open new stores to how to optimize in-store experiences and staffing.

Key applications include:

Site Selection Made Smarter: Retailers can evaluate demographic profiles, foot traffic patterns, nearby competitors, and local conditions to pinpoint the most promising locations for expansion.

Optimizing In-Store Experience: Heat maps of foot traffic reveal which aisles attract attention, where bottlenecks form, and how to arrange layouts for intuitive navigation and convenience.

Data-Driven Staffing: By understanding when and where customers congregate, retailers adjust staffing levels, for instance, at checkout counters, busy aisles, or during peak shopping periods, for smoother operations.

Personalised Engagement at the Point of Interaction: Location-triggered offers and coupons, aligned with observed customer behaviour, help deliver relevant messaging that enhances the shopping experience.

These capabilities highlight how location intelligence serves as more than a tool. It has evolved into a core driver of operational efficiency and customer satisfaction.

Is location intelligence being built into retail strategies from the ground up, or is it still treated as an optional enhancement?

Know more

Navigating Cloud Migration and Modernisation with Azure

Chandani — Thu, 28 Aug 2025 05:32:27 +0000

Adopting cloud infrastructure is about more than cost savings. It’s a strategic shift toward agility, efficiency, and future readiness. A phased migration, moving workloads in waves rather than all at once, allows organisations to modernise steadily while minimising risk.

A structured three-step framework guides the journey:

Plan and Strategise – Begin by establishing what drives the move: scalability, analytics enablement, automation, or unified digital workflows across ERP, CRM, QMS, and supply chains. A clear migration roadmap, stakeholder alignment, business continuity planning, and financial buy-in set the foundation.

Implement with Care – Success hinges on the right talent and partner support. Starting with lift-and-shift migrations, organisations can stabilise apps in Azure before refactoring or rearchitecting for cloud-native advantages. Testing, performance tuning, and security remain critical throughout. This approach aligns with broader best practices that stress iterative phased migration, pre-migration assessments, and automated testing protocols.

Operate and Govern Effectively – Post-migration, efficiency and insight require robust operations and governance. Tools like Azure Arc, Monitor, Security Center, Sentinel, Blueprint, and cost-management utilities help track performance, reinforce security, enforce policies, and keep spending optimised.

This layered approach of setting clear business drivers, executing iteratively, and mastering ongoing governance, positions cloud migration not as a one-off project but as a transformation enabler.

How are organisations today ensuring their migration journeys to Azure are both strategic and sustainable? Is incremental adoption now the norm, or does it still feel like a leap of faith?

Click here to know more

Pharma Success Through Competitive Intelligence

Chandani — Thu, 28 Aug 2025 05:30:07 +0000

In pharmaceuticals, success isn’t only about scientific breakthroughs. It also depends on strategy. Competitive intelligence enables companies to track pipelines, market moves, and regulatory shifts, helping them make smarter decisions from development to commercialization.

It plays a role in refining drug development, improving due diligence in partnerships, differentiating brands, optimizing portfolios, and navigating market dynamics with agility.

The challenges are real—accessing reliable data, finding the right talent, avoiding information overload, and maintaining ethical standards in data collection. Yet, when done well, competitive intelligence becomes more than an add-on. It’s a strategic necessity.

Is competitive intelligence being treated as central to pharma strategy in your experience, or still as a supporting function?

Click here to know more

AI, Compliance, and the Privacy Balancing Act

Chandani — Tue, 26 Aug 2025 11:42:17 +0000

AI is increasingly being seen not just as a tool for efficiency but as a partner in managing compliance and safeguarding data privacy. Its ability to detect anomalies, flag risks, and adapt to shifting regulations is reshaping how organisations think about governance.

A key consideration is whether compliance should be treated as an add-on or designed into AI frameworks from the beginning. The latter approach seems to offer greater long-term sustainability and trustworthiness.

In industries such as healthcare, finance, and e-commerce, the challenge is less about ticking boxes and more about maintaining credibility. Balancing innovation with privacy is proving to be a critical test for AI adoption.

How are others approaching this balance? Is AI being built with compliance in mind from day one, or is it still something that gets layered on later?

[Know more.](AI is increasingly being seen not just as a tool for efficiency but as a partner in managing compliance and safeguarding data privacy. Its ability to detect anomalies, flag risks, and adapt to shifting regulations is reshaping how organisations think about governance.

How are others approaching this balance? Is AI being built with compliance in mind from day one, or is it still something that gets layered on later?

[Know more](AI is increasingly being seen not just as a tool for efficiency but as a partner in managing compliance and safeguarding data privacy. Its ability to detect anomalies, flag risks, and adapt to shifting regulations is reshaping how organisations think about governance.

How are others approaching this balance? Is AI being built with compliance in mind from day one, or is it still something that gets layered on later?

Know more: https://www.meritdata-tech.com/resources/ai-compliance-data-privacy

Automated Data Gathering for Renewable Energy Firms With RAG & Agentic Workflows

Chandani — Fri, 22 Aug 2025 05:23:42 +0000

80% of AI initiatives still rely on reactive tools - but the future lies in systems that can think ahead.

Humans don’t operate on prompts alone. We plan, remember, and reason - continuously adapting to new goals, feedback, and context. Modern AI agents are now beginning to do the same.

What sets this next generation apart isn’t just better responses - it’s their ability to:

Plan multi-step actions towards defined goals
Remember past interactions to refine future decisions
Reason through logic, data, and uncertainty

These aren’t just capabilities - they’re cognitive foundations.

Agent architectures built on these principles are powering a shift from task-doers to goal-seeking collaborators across industries - from code generation to customer support. We’re entering the age of synthetic thinking agents - AI systems designed to move with intent, adapt over time, and act with context.

If you're still building around prompts, you should check out this case study - https://rebrand.ly/Rag-dev - on how RAGs, LLMs and agentic workflows enhance intelligent data extraction.

Automated Data Gathering for Renewable Energy Firms With RAG & Agentic Workflows

Chandani — Thu, 21 Aug 2025 06:36:17 +0000

92% of enterprises call AI “strategic.” But most of today’s systems? They just react. The real leap is agents that can reason, adapt, and pursue goals and still know when to hand things back to a human.

Think coding assistants that follow long-term logic. Or supply chain agents that flag a disruption before it happens. That’s intent-driven AI. They:

Reason, not just respond
Adapt to dynamic conditions
Pursue goals, not just complete tasks
Combine tools and memory to deliver meaningful outcomes
Know when to bring in a human

If your AI still waits for prompts, it may be time to redefine what autonomy should look like. If you're still building around prompts, you should check out this case study - https://rebrand.ly/Agentic-dev - on how RAGs, LLMs and agentic workflows enhance intelligent data extraction.

Smarter Decisions from Strategy to Shelf

Chandani — Tue, 19 Aug 2025 07:16:10 +0000

Retailers with solid data strategies move 2–5x faster when making decisions.

But here’s the thing - many are still running on fragmented or outdated data. Demand signals don’t line up, pricing is siloed, and ops metrics lag behind.

The best retailers connect it all - demand, pricing, operations - so they don’t just move quickly, they move with clarity. Here are some powerful practices for industry data sourcing: https://rebrand.ly/retail-dev

If you’re in retail, where do you see the biggest cracks: demand, pricing, or ops?

Bespoke B2B Contact Data Built Live for Precision Marketing

Chandani — Mon, 18 Aug 2025 11:34:47 +0000

Nearly 80% of marketers admit they struggle to make sense of their own data.

Ever looked at a dashboard and thought, “This report looks broken”? 📉

Turns out, most of the time the tool isn’t the issue. It’s the data behind it — incomplete sources, taxonomies that keep changing, delayed syncs, and manual fixes that don’t scale.

That’s why nearly 80% of marketers say they struggle to trust their own data. It’s not a visualisation problem. It’s a data supply chain problem.

What do you think trips teams up more: bad data quality or shaky data architecture?

Check out more here - https://rebrand.ly/merit-cm

Why Legacy Code Modernisation Matters

Chandani — Fri, 20 Jun 2025 11:25:30 +0000

Details

As organisations modernise legacy systems for cloud and AI readiness, traditional approaches to code reengineering and manual testing often create bottlenecks, limit scalability, and increase the risk of defects. This is not due to AI integration itself, but rather the absence of automation-particularly in testing-which becomes a key constraint during AI readiness efforts.

This webinar offers a practitioner’s perspective on how AI-enabled automation can transform both code modernisation and testing workflows for greater speed, reliability, and scalability.

In this session, we’ll delve into a systematic modernisation framework-covering static and dynamic analysis of monolithic codebases, decomposition into modular services, and refactoring strategies that preserve core business logic while ensuring functional equivalence. We’ll also demonstrate how performance and functional benchmarks are embedded throughout the pipeline to validate the accuracy and impact of transformation at every stage.

Join Now: https://events.teams.microsoft.com/event/048ac7d3-a8db-4bd9-a71d-3cddf4f7ab43@7fe14ab6-8f5d-4139-84bf-cd8aed0ee6b9