Adeola Noheemat Raji is a powerhouse in data analytics, business intelligence, and strategic consulting. In this interview with Jade Jackson, Raji, who has far-reaching experience at PwC, Deloitte, and BCE Solutions, speaks about how to use high-impact analytics initiatives using a blend of AI, machine learning, and marketing insights to drive real-world business outcomes.
How do you define AI-powered business intelligence in your work, Adeola Raji?
AI-powered BI is about going beyond static reports—giving decision-makers real-time, predictive insights they can trust. I use machine learning to detect trends, anomalies, and future outcomes before they happen. At PwC, we used AI models to predict fraud risk, and at BCE Solutions, we applied predictive dashboards to improve project ROI forecasting. AI gives life to data—it helps us shift from “what happened” to “what’s next,” and that’s where real strategic value is created.
Can you share a specific moment where AI directly influenced a business decision?
Yes! One moment that stands out was at BCE Solutions. We were tracking construction project delays and noticed recurring bottlenecks. I built a predictive model using Python and Power BI to identify early warning signals based on cost spikes and resource lag. We avoided a $250K delay within a week by shifting procurement timelines. The AI model alerted leadership three days earlier than traditional methods. That was when I saw how AI isn’t just a buzzword—it’s a practical tool that saves actual money and drives better decisions.
What tools do you use most when integrating AI into business intelligence?
I use a mix depending on the project, but Python and SQL are my foundation for model building and data wrangling. Power BI and Tableau are my go-to platforms for visualization—they make insights accessible. I also use Snowflake for scalable data warehousing and Azure Data Factory for building ETL pipelines. Regarding AI, I often turn to Python libraries such as scikit-learn to build predictive models and detect anomalies effectively.” Designing systems where the data flows seamlessly and the insights speak clearly is what ties it all together.
How do you make complex AI insights understandable to non-technical stakeholders?
That’s honestly one of my favorite challenges. It’s about storytelling—translating technical jargon into business impact. I don’t show models first; I talk about outcomes. For instance, instead of saying, “Our model has a 92% precision score,” I’ll say, “This system correctly flags 9 out of 10 high-risk vendors before they cause financial exposure.” I also use dashboards with visuals like KPIs, trend lines, and heat maps. If the insights aren’t understood, they won’t be used. So, my job is to connect the dots between models and meaning.
You’ve worked in both consulting and project-based environments. How does AI strategy differ in each?
Great question. In consulting, the focus is often on scalable frameworks and repeatable processes. You need models that generalize well across clients. In project-based environments like construction, AI has to be hyper-contextual—what works in one region may not work in another. At BCE, I built dashboards tailored to specific project milestones. At PwC, we developed models applicable across different SME funding structures. So, the key is agility—knowing when to build broad tools and when to go deep into a client’s unique data DNA.
What are your most common challenges when building AI-enabled dashboards?
One big challenge is data quality. AI is only as good as the input. I’ve worked with datasets that needed extensive cleaning and normalization before they were usable. Another issue is adoption—executives want “AI” but sometimes don’t trust the black box. That’s why I focus heavily on transparency and explainability. I include confidence intervals, show historical accuracy, and build toggles into dashboards so users can explore scenarios. The third challenge? Change management. You need champions who’ll advocate for new tools across departments.
How do you balance AI-driven insights with human judgment in decision-making?
I think AI should support, not replace, human judgment. Data gives you the facts, but people bring the nuance. I always design my models with room for human override. For example, if a risk flag is raised, the dashboard suggests options but doesn’t automatically execute them. At Deloitte, we had a scenario where the AI flagged a drop in portfolio performance, but human insight revealed it was a seasonal trend, not an issue. The magic happens when AI and human intuition complement each other.
Can you talk about the role of AI in modern marketing strategies?
Oh, it’s massive. AI now drives everything from campaign personalization to ROI tracking. I once used regression modeling to analyze ad performance across multiple channels and helped redirect $3M in spend toward higher-performing segments. We improved marketing ROI by 25%. AI helps predict customer behavior, segment audiences more accurately, and optimize budget allocation. But beyond optimization, it brings agility—you can test, learn, and pivot in real time. In today’s market, marketing without AI is like flying blind.
How do you ensure data governance and ethical AI use in your analytics work?
I always start with compliance and transparency. At PwC, we implemented GDPR protocols using row-level security in Power BI, so users only saw what they could. I also document model assumptions and embed fairness checks—especially in grant allocation or hiring models. Bias is actual, and as data scientists, we are responsible for testing for it. Ethics isn’t just a checkbox—it’s part of the model architecture. We’ve failed if our insights harm trust or fairness, no matter how “smart” the model is.
Looking ahead, Adeola Raji, what excites you most about the future of AI in business intelligence?
I’m most excited about the democratization of AI. Tools are getting more accessible, and more teams—beyond data science—can now build, test, and iterate on insights. Low-code platforms, augmented analytics, and generative AI are all opening new doors. I envision a future where real-time, AI-driven decisions are the norm across marketing, operations, and finance. But what excites me most is impact—seeing AI help small businesses, nonprofits, and developing markets make smarter, faster decisions. That’s the future I want to build.
