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Code modernization simplified

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  

#codemodernisation #modernization #cloudadoption  

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Pricing Intelligence

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

#pricingintelligence #retail #automotive

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B2B outreach redefined

Even when you follow all the “best practices,” reply rates are dropping. The problem isn’t the messaging. It’s the data quality, the way you scale, and the outreach models you rely on. In our on-demand session, top B2B marketers share how they’re consistently achieving 10–15%+ reply rates by: ✅ Prioritising relevance over reach ✅ Protecting domain reputation while scaling ✅ Moving beyond surface-level personalisation with smarter data We also unpack why providers like ZoomInfo and Apollo may be holding outreach back and what leading teams are doing differently to stay ahead. 🎥 If you’re a senior B2B marketer, sales leader, or RevOps professional looking to boost replies and safeguard deliverability, this one’s for you. https://lnkd.in/dkwhBYpZ #Marketing #conversions #B2Boutreach

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Why Intelligent Data Extraction Matters

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  

#unstructureddata #dataextraction #data  

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The Human-AI Collab

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 #HumanCenteredAI #Collaboration #FutureOfWork 

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AI best practices

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  

#AI #DataStrategy #Innovation #DigitalTransformation 

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Automotive SC 3.0

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

#AutomotiveIntelligence #BusinessIntelligence #MobilityInnovation #DataDriven

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What does precision validation look like in action?

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 

DataIntelligence #MediaInnovation #Publishing 

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Gen AI Apps and Ethics

A whopping 71% of companies are using generative AI in at least one part of their business this year, while 92% say they plan to increase their AI investments soon. But with all that momentum comes new questions: how do enterprises build trust, ensure compliance, and secure these powerful new tools? Trust and risk management are on every tech leader’s mind, and getting them right is the real challenge of scaling AI beyond experiments. Let’s swap stories: what’s working, what’s tricky, and how’s your team handling it? The latest blog has practical ideas and examples. Check it out! https://lnkd.in/dmpMdQZG #genAI #AIsecurity #AIethics

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Automotive Supply Chain

With the North American automotive supply chain market expected to hit $65 billion by 2025, and AI now driving everything from demand forecasting to real-time operational visibility, the way cars get built and delivered is being rewritten, literally. 🚍 EV trends, supply chain digitalisation, and green manufacturing are more than industry talk; they’re changing how suppliers, OEMs, and logistics firms operate on the ground. ⚙️ What does it all mean for resilience and speed, and how are companies keeping up as disruption becomes the norm? This article presents a few perspectives - share your thoughts! 💡 https://lnkd.in/d6RyUK6a #automotivesupplychain #autoindustry #digitalisation

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AI Modernisation

Here's a jaw-dropper: 78% of global organisations now use AI in at least one business function, and 90% of all business apps are set to be cloud-native this year. That kind of tech momentum is no accident - it’s propelled by a “continuous modernisation” mindset where updates never stop. Instead of waiting for a huge overhaul, companies are chipping away at tech debt and keeping systems agile with constant improvements. The result? Fewer fire drills, less downtime, and the freedom to jump on new innovations as soon as they land. Why are so many leaders rethinking legacy upgrades? Join the conversation and check out this blog for real-world examples! https://lnkd.in/d7g7aC3H #continuousmodernisation #sustainablemodernisation #modernization

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BI in Retail

Did you know over 80% of retailers plan to boost their AI and business intelligence investments in 2025 and nearly 70% say these tools directly raised their annual revenue last year? 🛍️⚙️ It's clear that smart data and analytics aren't a buzzword; they're becoming the backbone of how retailers track what customers actually want, optimise inventory, and adapt to real-time trends. Whether it's personalising offers or simply not running out of stock, data-driven retail is reshaping how stores and e‑commerce brands show up for shoppers. 🛒 Let’s chat about what this shift really means for the industry. Check out this blog for a closer look! https://lnkd.in/dBrHFMQE 🎯 #retailBI #AI #retailintelligence

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Global automotive trends in data ecosystem

According to McKinsey, connected-car services alone could create USD 750 billion in value by 2030. That’s before factoring in the wider impact of data across design, manufacturing, supply chain, and customer experience. Three trends stand out in the global automotive #dataecosystem: ➡️ Connectivity – enabling personalised services, predictive maintenance, and safer driving experiences. ➡️ Integration – building seamless, on-demand mobility ecosystems. ➡️ Open-source collaboration – where automakers share architectures to accelerate EV and digital transformation. As one of our experts observes: “We’re moving from a product-centric mindset to a flexible, data-driven digital era.” The question for 2025 is not whether data will transform automotive. It’s who will master velocity and integration first and set the pace for the industry. I came across this blog and believe you will find it very insightful too - https://lnkd.in/gmXiKqZC 👉 What do you see as the most transformative use of automotive data in the next 5 years?

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Automotive Innovation 5.0

🚘 Automotive isn’t just about vehicles anymore. It’s about ecosystems—and ecosystems run on data. From electrification to connected-car services, the complexity of decisions has exploded. Success now depends on access to intelligence that is granular, harmonised, and fast enough to keep pace with change. The leaders we see emerging are those who integrate: ➡️ Detailed component-level data with OEM strategy mapping ➡️ Time-stamped pricing updates with global inventory signals ➡️ Regional language nuance with standardised global taxonomies The industry’s next competitive edge isn’t about how many data points you have. It’s about how fast and precisely you can turn them into foresight. Here’s an article I found interesting for automotive leaders: https://lnkd.in/dw3JFy9y ⚡ In a world where launches, regulations, and customer expectations are shifting simultaneously, how prepared are we to make precision decisions at the speed of the market?

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Agentic AI applications

AI has always been powerful. But the real leap is happening now. With agentic AI. 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 2026 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://lnkd.in/d2eHWag2 🔮 Do you see agentic AI becoming a trusted collaborator in your domain—or is it still an experimental frontier?

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

🛒 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

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

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How Location Intelligence Elevates Retail Performance 🗺️

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.

Is location intelligence being built into retail strategies from the ground up, or is it still treated as an optional enhancement? https://linkly.link/2ELtF