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By Brown Chimezie
Adesola Abigeal Ogunnubi is a Nigerian-born finance and analytics expert whose professional journey spans from leading financial operations at Lafarge Africa to applying advanced analytics and AI models across industrial finance systems in the United States. With a background in accounting and an academic foundation in information systems and data science, she brings a rare combination of technical fluency and operational insight to manufacturing finance.
In this interview, Adesola discusses how data engineering, predictive modelling, and AI-powered decision systems are reshaping financial strategy in industry.
You began your professional life in Nigeria. What inspired your pivot from accounting to analytics?
Starting at Lafarge Africa gave me a solid foundation in core finance, especially in budgeting, performance analysis, and cost controls. But I constantly asked, “Why can’t we do this faster?” or “Why don’t we spot these problems earlier?” That curiosity made me realize finance had to evolve from static reporting to dynamic decision-making. So, I pursued graduate studies in the U.S., focusing on information systems and analytics. I wanted to combine the logic of accounting with the architecture of data systems.
Along the way, I learned that automation, database optimization, and cloud-based processing weren’t optional upgrades – they were critical infrastructure. I became especially interested in how data warehouses, ETL pipelines, and visualization tools could create real-time visibility in environments where delays cost money.
How did your background in Nigeria shape the kind of problems you aim to solve?
In Nigeria, I often worked in fragmented systems – disconnected ERPs, batch-processed reports, and siloed data. These inefficiencies led to reactive decisions and systemic waste. It pushed me to explore systems integration, where you build feedback loops between finance, production, and procurement.
At Lafarge, I embedded inflation-indexed forecasting into our budget cycle, integrating currency volatility models into our CAPEX simulations. I also managed data alignment between SAP FC and production metrics, which was challenging but revealed how critical system interoperability is in financial planning.
You mentioned predictive modelling.
How do you apply it in real settings?
One area is BOM (Bill of Materials) variance prediction. I developed a supervised learning model using historical BOM consumption data and scrap rates to predict likely cost overruns based on incoming production schedules. This model helped preempt material waste and improved forecast accuracy.
Another model I built predicted supplier non-compliance risks using a mix of structured data (delivery lags, invoice history) and unstructured data (procurement logs with NLP tagging). It flagged vendors that might breach SLA thresholds before the operations team detected problems.
The key isn’t just building a model. It’s ensuring the model integrates into workflows through real-time dashboards or alerts in the ERP. That’s what closes the loop.
AI is transforming industrial processes.
What’s your approach to embedding it in finance?
I use AI at multiple layers: anomaly detection, NLP classification, and time series forecasting. For example, I deploy anomaly detection algorithms (like isolation forests) on expense data to catch outliers not flagged by traditional controls. I also use NLP to extract trend signals from qualitative audit notes and supplier reviews.
A big push in my work is toward explainable AI. I use SHAP values and LIME to interpret model behaviour so finance and operations teams can understand and trust the outputs. Without explainability, AI becomes a black box. In finance, that’s unacceptable.
I also use AI to model forecast scenarios: if unit cost rises 8% and delivery delays stretch by two days, what’s the projected margin impact in two quarters? These are the kinds of questions I want my systems to answer on demand.
What do you think finance professionals need to embrace next?
We need to embrace systems thinking and full-stack literacy. That means not just knowing Excel or BI tools but understanding SQL joins, API integrations, and basic machine learning workflows. Finance is no longer a downstream report generator – it’s a strategic modelling hub.
In the near term, I want to help more manufacturers adopt microservice architecture for finance workflows – modular, scalable, event-driven systems that can process data in near real-time and adapt quickly to supply chain shocks.
“Finance is no longer just about compliance. It’s a platform for simulation, foresight, and operational resilience.”
Adesola Ogunnubi represents a new generation of technical finance leaders who are not only fluent in the language of accounting but also write the code that transforms it. From Lagos to U.S. data labs, her work is redefining how industry plans, adapts, and thrives.
