Allen Drennan is the Founder and CEO of Cordoniq Inc., a next-generation platform for immersive, AI-integrated video collaboration. A veteran technologist and entrepreneur, Allen has spent his career building and scaling real-time SaaS and PaaS solutions that power secure communication across mobile and desktop platforms.
I get asked all the time about the role artificial intelligence really plays in business decision-making. There is no shortage of hype around AI, but the reality is both simpler and more powerful than many people think. As someone who has spent years developing technology that blends human insight with machine intelligence, I wanted to share a straightforward, non-technical look at how AI and machine learning are helping organizations make smarter, faster and more informed decisions.
Across industries, organizations are increasingly relying on artificial intelligence to guide both day-to-day operations and long-term strategies. According to Harvard Business Review, AI-driven tools are enhancing business decision-making by improving forecasting accuracy and enabling real-time visibility into processes, bringing measurable efficiencies to core operations.
While generative AI is expanding creative and operational possibilities, it is machine learning (ML) that serves as the backbone for faster, more accurate and more efficient business decisions. By applying machine learning, organizations can strengthen critical workflows and make more informed choices instantly. This technology is driving transformation in sectors such as financial services, manufacturing, software and IT, retail and healthcare.
Understanding Machine Learning
Machine learning, an essential branch of AI, uses algorithms to detect patterns in large datasets. These systems learn from data rather than explicit programming, allowing them to spot irregularities, recognize trends and generate predictions.
The International Institute of Business Analysis highlights ML’s ability to process vast quantities of information swiftly and accurately as one of its most significant advantages for business analysis. Unlike traditional analytical methods, ML systems can handle massive, complex data sets to uncover insights that would otherwise remain hidden.
Because of their sophisticated pattern recognition and adaptability, machine learning systems play a central role in predictive analytics, a discipline that uses statistical modeling, data mining and AI to anticipate future outcomes based on historical data.
By applying these methods, companies can now make use of enormous volumes of structured, unstructured and raw data for advanced decision-making. ML algorithms extract actionable insights from this data, helping leaders make more confident and evidence-based choices.
Machine Learning in Action: Cross-Industry Applications
Research from McKinsey & Company identified more than 400 machine and deep learning use cases across 19 industries and nine business functions, demonstrating the versatility of this technology in both operational and strategic contexts.
Fraud Detection
Machine learning is particularly effective at identifying anomalies that may indicate fraudulent activity. PYMNTS.com reports that 30 percent of chief operating officers surveyed are already applying AI to detect fraud. These algorithms analyze transaction data, such as time, location and amount, to flag suspicious patterns in real time. Applications range from credit card and banking fraud to e-commerce, mobile payments, and invoice verification.
Natural language processing (NLP) further enhances system security by powering AI-based firewalls capable of identifying and mitigating threats more dynamically than traditional systems.
Sales and Operational Decisions
Sales analytics benefit significantly from ML’s predictive capabilities. By combining real-time and historical data, these systems support more precise decisions around sales forecasting, inventory optimization and supply planning. Machine learning models integrate data from sales, marketing and finance to deliver faster, more reliable predictions, helping teams adapt quickly to changing market conditions.
Customer Insights and Experience
Another powerful application of ML lies in customer relationship management (CRM) and experience design. Machine learning powers recommendation engines that personalize offerings, improve satisfaction and strengthen customer retention. By interpreting behavioral and purchasing data, retailers can anticipate individual preferences with remarkable precision.
As Shopify observes, ML enables retailers to identify subtle patterns in customer behavior that might otherwise go unnoticed, resulting in smarter, data-driven decisions that directly enhance performance. These insights also help reduce churn by revealing early signals in customer demographics, engagement and purchase history.
The Future of Decision-Making
AI and machine learning are redefining how companies make choices: faster, smarter and with greater precision. Whether predicting customer needs, preventing fraud or optimizing operations, these technologies translate complex data into real-time insights. Businesses that learn to harness AI effectively are not just improving performance; they are building a more adaptive and resilient foundation for the future of decision-making.
