How Unilever Achieved 40% Faster Data Processing with No Code AI
As artificial intelligence continues to positively restructure how businesses operate, large enterprises are actively exploring ways to embed it into their workflows. The integration of AI traditionally required heavy technical investments, specialized talent, and months of development. No code AI platforms, which democratize access to AI, enables business users to implement sophisticated data models without writing a single line of code, making it accessible to anyone.
Among the many companies that have successfully embraced this shift, Unilever is one that stands out. With a portfolio of over 400 brands and a presence in more than 190 countries, Unilever handles vast amounts of data every single day. By integrating no code AI tools into their data processing pipelines, they were able to accelerate data processing by 40% while reducing the strain on their data science teams.
What Is No Code AI?
Before diving deeper into Unilever’s story, it is important to understand what no code AI actually is. No code AI refers to artificial intelligence development platforms that allow users to create, train, and deploy AI models without writing any programming code. Instead of scripting in languages like Python or R, users work through intuitive interfaces that involve dragging and dropping components, selecting options from menus, and uploading datasets with simple configurations.
These platforms rely on powerful backend engines that handle data preprocessing, algorithm selection, and model training automatically. The goal is to abstract away the complex technical work and empower non-technical users such as business analysts, marketers, and operations managers to leverage AI in their daily workflows.
Key features of no code AI platforms typically include:
Drag and drop model builders
Automated data cleaning and transformation
Built-in algorithms and pre-trained models
Easy integration with databases and business intelligence tools
Real-time deployment with minimal configuration
By simplifying the AI development process, no code tools lower the barrier to entry and help businesses scale AI initiatives faster and more broadly.
The Challenge of Scale & Complexity
Unilever’s data operations are massive. From supply chain metrics to marketing analytics and consumer behaviour insights, the company processes terabytes of data daily. Previously, this meant relying heavily on central IT and specialized data science teams. With increasing demand for faster insights, the bottlenecks became evident.
Some key challenges included:
Delayed reporting: Business teams had to wait days or even weeks to get analytical reports.
Limited scalability: As data volumes grew, traditional ETL pipelines struggled to keep up.
Talent bottlenecks: Data scientists were overloaded with requests that were repetitive or simple in nature.
Unilever recognized that to continue growing and succeeding, they needed to make data accessible and actionable across the organization.
No Code AI’s Role
In 2022, Unilever partnered with a no code AI platform. The goal was to empower business analysts to build predictive models without needing to code or have a deep statistical background.
The no-code AI platform offers a drag and drop interface that lets users import datasets, define prediction goals, and generate models that can be deployed in production systems. It automates data preprocessing, model selection, training, and validation.
Here is how Unilever implemented it:
Pilot projects: Unilever started with a few pilot use cases including predicting inventory needs and optimizing promotional campaigns.
Training workshops: Business analysts received training in how to use the no code platform effectively.
Integration into existing systems: The outputs from the AI were fed directly into dashboards and operational systems, streamlining the decision making process.
Feedback loops: Continuous monitoring and feedback allowed teams to refine models and improve accuracy over time.
Results & Measurable Impact
Within six months of deploying no code AI solutions, Unilever reported several measurable improvements:
Data processing time reduced by 40%: Analysts could now generate predictive insights within hours instead of days.
Increased productivity: Data science teams could focus on complex models while business analysts handled day to day predictions.
Cost efficiency: Reducing reliance on custom-coded solutions lowered development and maintenance costs.
Higher model adoption: Business units were more likely to trust and use models they had a hand in creating.
One example is in the realm of demand forecasting. Before they implemented AI, forecasting models were created by central teams and updated quarterly. Using the no code approach, marketing managers could now build their own demand forecasts weekly, allowing for more agile responses to market changes.
“When we first introduced no code AI to our teams, there was understandable hesitation. But once analysts saw how quickly they could produce meaningful forecasts and test their ideas, it shifted the culture. Suddenly, data science was no longer a gatekeeping function, it became a shared language across our organization.”
Other Challenges & How They Overcame Them
Despite the success, Unilever did face hurdles in adopting no code AI:
Cultural resistance: Some team members were skeptical of AI tools replacing traditional analytics.
Data quality issues: Feeding poor quality data into AI models leads to unreliable outputs.
Model governance: Ensuring that models met compliance and ethical standards required oversight.
Unilever addressed these challenges through a combination of education and process design. They implemented a governance framework to review and approve models before deployment, and they invested in data cleaning processes that ensured high quality inputs. Moreover, early wins helped to build trust and excitement around the new tools.
Lessons for Other Enterprises
Unilever’s success provides several takeaways for organizations considering no code AI:
Start small but think big: Begin with limited use cases that have clear success metrics.
Empower domain experts: Allow business users to drive model development with the tools they understand.
Maintain human oversight: AI should augment, not replace, human decision making.
Invest in training: Skill development is essential to unlock the full potential of no code platforms.
Ensure strong data practices: Clean and well structured data is the backbone of any successful AI initiative.
Could No Code AI Work for You?
To help you explore how no code AI could benefit your organization, consider the following reflective questions:
What routine decisions could be improved with predictive insights?
Where are your current data bottlenecks?
How much time does your team spend on repetitive analysis?
Do your business users have access to the data they need, when they need it?
Unilever’s journey shows that no code AI is a practical solution to real world business problems. By shifting from centralized, resource intensive data modelling to a distributed, user empowered approach, Unilever achieved faster processing, better insights, and stronger alignment between data and decisions.
As more organizations look to harness the power of AI, the question is not whether to use no code tools, but how to use them effectively. With the right strategy, training, and governance, any company can replicate the success of Unilever and make AI an everyday part of decision making.
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