How Traditional Enterprises Can Actually Win with Generative AI – A Data-First Approach

Spark
May 27
3 min read

A modern factory with industrial equipment and sensor-lined production lines, representing the integration of data and AI in traditional manufacturing environments. — Factory floors like this are becoming smarter—with generative AI helping predict failures, optimise processes, and turn sensor data into real-time insights.

For large, established or 'traditional' companies, generative AI isn’t just a shiny new tool for chatbots and content. When it’s built on the right data foundations, it can genuinely transform how businesses operate.

The challenge? Most companies don’t struggle because they lack ambition—they struggle because their AI efforts are disconnected from their data strategy.

Here’s how legacy organisations can get real value from generative AI by keeping data at the centre.

1. Start with Data – It’s the Fuel for Generative AI

Before you even think about deploying AI models, your data needs to be in shape. That means structured, clean, accessible, and ready to feed intelligent systems.

What to focus on:

Break down data silos – Data is often scattered across ERPs, CRMs, and legacy systems. A modern architecture (think data mesh or lakehouse) helps bring this all together for AI to tap into.
Clean and enrich your data – Garbage in, garbage out. AI’s only as good as the data you give it. Invest in pipelines that validate, deduplicate, and resolve entities.
Use synthetic data when needed – In areas like manufacturing, where edge cases are rare, AI can generate synthetic datasets to help train smarter models.

Example:A financial services firm uses large language models (LLMs) to pull insights from decades of unstructured PDFs—turning legacy reports into structured, searchable data for risk analysis.

2. Start Where Your Data is Strongest

Don’t get distracted by flashy AI demos. The best ROI comes from applying AI where your data is already mature and your processes are well understood.

High-impact areas include:

AI-augmented data engineering – Use tools like GitHub Copilot for SQL or PySpark to speed up pipeline development.
Auto-generated documentation – LLMs can handle the boring bits like metadata tagging, data lineage, and schema docs.
Smart anomaly detection – Combine forecasting with AI-generated explanations to troubleshoot pipeline issues faster.

Example:A manufacturing firm trains a diffusion model on sensor data to predict equipment failures—cutting unplanned downtime by 20%.

3. Build It Right: Secure, Auditable, and Enterprise-Ready

In an enterprise setting, AI can’t just be clever—it has to be responsible. That means putting the right guardrails in place from the start.

Key elements to get right:

Domain-specific models – Skip the generic GPT-4 approach. Smaller models fine-tuned on your own data (like contracts or research) can be more effective.
Retrieval-Augmented Generation (RAG) – Let AI pull answers from your data, not the open web.
Role-based access – Not every user should be able to ask the AI about payroll and legal at the same time. Good governance ensures the right level of access for each role.

Example:A pharmaceutical company uses a RAG-based assistant trained on internal research papers—ensuring accurate answers and protecting their IP.

4. Prove the Value – Track the Right KPIs

Generative AI can’t just be a black box. If it’s working, you should be able to measure the impact clearly.

Metrics worth watching:

✅ Speed – How much faster are you processing unstructured data?
✅ Accuracy – Are insights staying true to your underlying data?
✅ Efficiency – How many hours of manual work are being saved?

Example:A logistics company reduced manual data entry by 70% and improved demand forecasting accuracy by 15%—both directly attributed to their AI rollout.

Wrapping Up: Treat AI as a Data Multiplier

For traditional enterprises, generative AI isn’t about replacing people—it’s about making your data work harder, smarter, and more efficiently.

Where to begin:

Assess your data readiness – Can AI actually work with what you’ve got?
Start with strong data areas – Don’t test AI on your messiest systems.
Scale with structure – Keep governance, compliance, and security front and centre.

The companies that win with AI won’t be the ones chasing hype. They’ll be the ones who treat AI as a data problem first—and solve it that way.

Is your data ready to lead the way?