Category: Blog posts

Corporate data platforms are entering a new era. Where their role used to be collecting and reporting data, they are now primarily developed to enable the use of AI, and above all, to improve business processes.

This shift is significant. Data is no longer just a support for decision-making; it is an active part of operations. AI no longer merely analyzes processes, it participates in executing and optimizing them.

A modern data platform connects data, analytics, and AI directly to business processes. The result is systems that guide and automate operations.

1. Automation of Routine Tasks

AI is highly suitable for repetitive and rule-based tasks such as invoice processing, accounting, customer service chatbots, email sorting, and drafting responses. This saves time and reduces human error.

2. Data Analytics and Forecasting 

AI enables a shift from reactive to proactive operations. Examples include:

• Sales forecasting
• Demand and inventory optimization
• Customer behavior analysis
  

This allows for faster and better data-driven decisions.

3. Improving Customer Experience

Personalization and real-time responsiveness: through tailored recommendations and targeted marketing enhance both sales and customer satisfaction.

4. Streamlining Internal Processes

Internal processes can be improved through automated document processing, AI-supported project management, and predictive insights. This leads to faster information flow and less manual work.

5. Optimization of Production and Operations

Operational efficiency improves through the combination of data and AI. Examples include predictive maintenance, logistics optimization, and quality control using image recognition. These can reduce costs and downtime.

6. Content Creation and Communication

Generative AI accelerates content production, such as marketing texts, reports, proposals, and training materials. This enables faster, more consistent communication and cost savings.

7. Decision Support

AI supports leadership at an increasingly strategic level, for example in scenario modeling, risk assessment, KPI monitoring, and anomaly detection. This leads to better strategic decisions.

Organizational Change Is Required

Although technology is advancing rapidly, the biggest challenge is not technical, it is organizational. Leveraging AI to improve processes requires changes in how organizations operate:

1. From Silos to Data-Driven Operations

Data cannot remain solely the responsibility of IT. It must become part of everyday decision-making at all levels.

2. New Roles and Capabilities

New capabilities are needed:

• Data engineers and ML specialists
• Roles that bridge business and technology (e.g., AI product owner)
• Above all, the ability to understand processes from a data perspective

3. Rethinking Processes

AI should not be layered on top of old processes. The greatest benefits come from redesigning processes with AI in mind from the start.

4. Change Management

Organizations must learn to trust data and AI. This requires a cultural shift, not just new tools.

Towards Autonomous Business Processes

The next phase is already emerging: autonomous processes.

In an autonomous process:

• Data updates in real time
• AI analyzes the situation
• Systems make decisions and trigger actions automatically

For example:

• Inventory replenishes automatically based on demand forecasts
• Customer service resolves a large share of inquiries without human involvement
• Production lines continuously optimize themselves using sensor data

The human role does not disappear—it evolves. The focus shifts from execution to supervision, control, and handling exceptions.

What Does Success Require?

Leveraging AI to improve processes requires combining three key elements:

1. Understanding the processes to be improved
2. AI that can be integrated directly into operations
3. High-quality, unified data on a modern data platform

Without a unified data platform, AI initiatives often remain disconnected experiments.

Autonomous Data-Driven Business

The transformation of data platforms is not just a technological upgrade, it is a shift in how companies operate.

Organizations that successfully combine data, AI, and processes will not only improve efficiency but also build the foundation for autonomous, continuously optimizing business. This is where future competitive advantage is created.

Such transformation requires deep expertise and practical experience.

At Etlia, we have been working with these themes from the very beginning. Our high customer satisfaction (NPS 88) reflects our ability to deliver concrete value and strengthen our clients’ competitiveness, now and in the future.

If you’d like to discuss the topic further, feel free to get in touch!

—Juuso Maijala, CEO

Just a few years ago, building data architectures was largely manual craftsmanship: fine-tuning SQL queries, debugging Python scripts, and constructing heavy ETL pipelines. Then generative AI arrived — and the playing field changed permanently.

I have been following the data industry, software development, and consulting for over 25 years, since the turn of the millennium. I have seen technologies rise and fade. Yet I can confidently say that the transformation we are witnessing now is the most significant shift since the move to the cloud began in the 2010s.

While the cloud changed where data resides and where processing happens, AI is changing how we work with data on a daily basis.

This does not mean data professionals are disappearing. It means the emphasis of the role is shifting: from hands-on coders to architects, curators, and quality controllers.

Software giants at the forefront of AI

Major data platforms and integration tools have not stood still. They have embedded AI directly into their core capabilities automating a lot of work.

Some examples:

Platform	Examples of AI Solutions
Snowflake (Cortex)	Brings LLM models directly to the data, enabling intelligent analysis without moving data outside secure environments.
Databricks (Agent Bricks)	Provides tools to build autonomous agents capable of retrieving information and executing tasks based on enterprise data.
Microsoft Fabric	Copilot guides users across the entire data flow, automating code generation, documentation, and reporting.
SAP (Joule)	Simplifies navigation within complex ERP data structures and translates business processes into faster, clearer insights.

“Vibe” and Agent Coding require true seniority

One of the most interesting emerging trends is vibe coding. In this model, the expert does not necessarily write every line of code themselves. Instead, they describe the desired outcome in natural language – and AI handles much of the technical implementation. The focus shifts from syntax to intention.

Another rapidly emerging concept is agent coding.
Agent coding refers to a model in which AI agents operate partially or fully autonomously: they retrieve information, process data, and execute defined tasks within an enterprise data environment based on predefined objectives. In this model, the role of the expert shifts from hands-on implementation to supervision, orchestration, and governance.

However, there is an important risk to consider: if you do not understand what high-quality code and sustainable architecture are supposed to feel like, vibe coding can lead to solutions that are difficult to maintain and evolve over time. AI is an excellent assistant, but it still requires human guidance.

This is where seniority and experience become essential. Experienced professionals quickly recognize when an AI-generated solution is technically impressive but architecturally weak, insecure, or hard to scale. Paradoxically, the easier code generation becomes, the more important deep expertise is.

As organizations move toward broader agent-based environments, where multiple AI agents operate semi-autonomously, the ability to evaluate, orchestrate, and govern these systems becomes increasingly important.

At Etlia, we are at the core of this development

This technological shift is unfolding faster than any previous transformation. That is why at Etlia, we invest heavily in continuous learning and active market monitoring, in ways that directly benefit our clients.

• Active monitoring: we continuously analyze announcements from market leaders. We do not rely on marketing materials alone – we test new features in practice to understand their real value. Credit is also due to technology vendors who keep partners well informed about new AI capabilities.
• Validation of expertise: we use AI to accelerate routine tasks, but senior-level expertise always ensures that the end result is production-ready, secure, and maintainable.
• Knowledge sharing: we actively share insights within our team so that the latest understanding immediately translates into value for our clients.

For us, it is a matter of professional pride that our clients receive up-to-date strategic guidance. We do not build yesterday’s solutions for today’s problems.

Is traditional expertise still needed?

Although AI handles many routine tasks, a deep understanding of data structures and integrations is more critical than ever. The role of the expert has shifted from execution to oversight, architecture, and orchestration. One must know precisely what is being built in order to guide AI in the right direction.

Data engineering is evolving from technical execution toward a more strategic function. As native AI tools within platforms handle heavy lifting, more space is created for what truly matters: turning data into real business value.

-Petri Räsänen

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If you would like to explore how to navigate this transformation and adopt AI tools in a controlled and sustainable way, our experts at Etlia are ready to support you.