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As organisations prepare for cloud and AI adoption, one challenge keeps surfacing: legacy code. The real blocker isn’t AI integration itself, it’s the reliance on manual reengineering and testing, which slows down projects, limits scalability, and increases the risk of defects.  

In our on-demand webinar, three practitioners share hands-on strategies and live demos for accelerating modernisation with automation, GenAI, and intelligent testing. 

Here’s what you’ll learn:  ⚡ How GitHub integrations enable CI/CD triggers, version control, and results tracking  ⚡ Practical approaches to modularisation, logic transformation, and secure coding  ⚡ How GenAI-powered tools speed up reengineering through prompts, templates, and reusable patterns  ⚡ Why manual testing creates blind spots — and how intelligent automation ensures coverage and quality 

If you’re a technology leader, architect, or developer looking for scalable, cloud-agnostic modernisation strategies, this session breaks it down with demos, frameworks, and practitioner insights. https://www.meritdata-tech.com/resources/legacy-code-modernisation-matters  

In fast-moving industries like automotive and retail, pricing can change daily, sometimes even hourly. Traditional methods that rely on manual tracking or static datasets just can’t keep up.

That’s why businesses are turning to AI-powered data harvesting. By capturing real-time pricing data from diverse sources - OEM sites, e-commerce platforms, brochures, and even scanned documents - AI helps companies:

⚡ Adjust prices within hours, not weeks

📊 Plan smarter promotions aligned with inventory and demand

🤝 Build customer trust with transparent, consistent pricing

According to Statista, organisations that embrace real-time pricing intelligence see a 10–20% revenue lift and an up to 25% improvement in profit margins.

The real edge isn’t just in collecting data, it’s in understanding it. With GenAI, OCR, and NLP, businesses can standardise messy datasets, detect trends, and act proactively in volatile markets.

Curious how this works in practice? We recently shared a breakdown of how AI-first data harvesting is transforming pricing strategies in automotive and retail. https://www.meritdata-tech.com/resources/ai-powered-data-harvesting-pricing-intelligence

Over 80% of enterprise data is unstructured - locked in PDFs, scans, contracts, and static portals. This “hidden” data often holds critical value for compliance, pricing, and decision-making, yet most tools still struggle with context, poor-quality scans, and complex layouts. 

The shift we’re seeing is from basic extraction to contextual understanding using GenAI, OCR, and NLP to not just read documents but interpret them. From detecting regulatory clauses to classifying sensitive content dynamically, intelligent extraction is becoming essential for compliance and operational agility. 

How is your organisation tackling unstructured data today and where do you see the biggest challenges? 

We recently wrote a piece digging deeper into this - Unlocking Value from the Forgotten 80% - https://www.meritdata-tech.com/resources/why-intelligent-data-extraction-matters  

AI works best when it complements human strengths, not replaces them. The real opportunity lies in building systems where machines handle scale and speed, while people bring creativity, ethics, and context.  

Here’s a piece that explores how this balance can be achieved - from designing transparent workflows, to ensuring accountability, to making collaboration between humans and AI feel natural.  

In your experience, what helps teams trust and adopt AI as a partner rather than a replacement?  

For anyone interested, here’s the article: https://www.meritdata-tech.com/resources/humans-collaborating-ai-solutions 

AI has moved well beyond prototypes but scaling it across an organisation is where many leaders still struggle. From aligning teams to managing data pipelines and governance, the challenges are as real as the opportunities. 

A recent resource that outlines 10 best practices for scaling AI, touching on strategy, adoption, and the practical steps needed to move from pilot projects to enterprise-wide impact. 

What makes it interesting is how much emphasis is placed not just on the tech itself, but on culture, collaboration, and continuous improvement. 

For those working on AI initiatives: what’s been your biggest hurdle in moving from proof-of-concept to production?  

Here’s the guide if you’d like to explore the full set of practices:   https://www.meritdata-tech.com/resources/scaling-ai-10-best-practices  

In the last three years, global automotive supply chain disruptions have surged by 88%. 🚗

Combine that with evolving regulations and shifting consumer demand, and it’s clear: inventory blind spots are no longer minor inefficiencies. They are missed revenue opportunities! 💡

Many automotive leaders are now leaning on real-time inventory intelligence to stay ahead. It allows them to:

▶️ Track market movements as they happen ▶️ Align product and pricing strategies with live demand signals ▶️ Strengthen resilience against regulatory and supply chain shocks

What stands out is how raw signals - pricing shifts, model updates, regional demand fluctuations - can be transformed into structured data that drives sharper, faster decision-making.

In such a competitive landscape, the ability to act on what’s happening now often decides who keeps pace and who sets the pace. Here’s a blog that caught my attention. It explores the 5 Technologies Reshaping Automotive Supply Chains - https://lnkd.in/d6RyUK6a

Keeping advertiser and agency data accurate is no small task. One UK publishing and events business recently shared how they approach it: 

✅ 400K+ contacts verified 

✅ 1,500+ companies validated each month 

✅ 250+ new high-value contacts added monthly 

✅ 6,000+ contacts monitored continuously 

Their biggest hurdle was ensuring their media database could keep pace with constant changes in the market. Outdated or incomplete records meant weaker insights and missed opportunities for sharper campaigns. 

By moving towards automated verification and enrichment, they’ve been able to maintain accuracy at scale and give their teams more confidence in the data driving strategic decisions. 

It raises an interesting question for all of us working with large datasets: what approaches have you found most effective for keeping contact and company information current in your own organisations? 

Full case story here if you’d like to dive into the details:

http://bit.ly/42Ci6hD 

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

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://lnkd.in/diaCaEWU

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.

🛒 Optimising 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.