Editorial
While generative AI and agentic AI dominate the headlines, analytical AI — data science and machine learning — continues to hum along in the background, powering decision-making across many industries.
Analytical role maybe less flashy, but it remains essential. Analytical models transform raw data into insights, patterns and predictions, enabling organizations to quantify risk, optimize operations and uncover opportunities. Unlike generative models or agentic systems, analytical AI is the engine of rigor, ensuring decisions are grounded in data rather than guesswork.
As important, analytical AI is gaining new relevance as it becomes integrated into agentic workflows. Instead of simply generating forecasts for humans to interpret, it powers agentic AI to act, decide and adapt autonomously. In this approach, agents translate insights into real-time actions with minimal oversight. The question is no longer whether analytical AI matters, but how fully it will fuse itself with agentic systems to drive business outcomes. This makes it timely to assess the state of analytical AI and its key enabler: ModelOps.
Analytical AI: Quietly Powering the Modern Enterprise
Analytical AI encompasses a broad range of technologies statistics, modeling, machine learning, neural networks and data mining. These approaches enable organizations to make predictions and uncover insights that guide action.
According to the latest Dresner Advisory Services research, 83% of organizations report that analytical AI plays either a direct or supporting role in their business. Yet only 11% call it a cornerstone — directly shaping business strategy — highlighting the gap between experimentation and enterprise-wide adoption. But this may be less important as analytic AI becomes applied to discrete business processes in agentic AI.
Related Article: AI Agent vs. Agentic AI: What’s the Difference — And Why It Matters
Key Forces Accelerating Analytical AI Investment
The drivers for analytical AI investment are clear: organizations deploying it solve inefficiencies, experiment with new approaches and prepare for digital disruption. The top objectives are consistent across industries — operational excellence, data-driven decision-making and enhancing customer experience.
Unsurprisingly, sales and marketing are dominant users, applying analytical AI to customer segmentation, fraud detection and cross-sell. What’s surprising is that strategic adoption remains below 10% in operations, finance and executive management — functions that stand to benefit greatly for churn prevention, price optimization, risk management and customer lifetime value analysis.
Industry adoption, also, shows an interesting pattern. Financial services and manufacturing lead, with 51% and 47% respectively applying analytical AI today. Technology, consumer services, business services and healthcare are not far behind. Larger organizations are also more likely to use analytical AI to tackle inefficiencies and specific business problems. Still, governance is inconsistent, and access to analytic content is a major determinant of adoption. Organizations that find it “extremely easy” to access analytics are far more likely to elevate AI into a strategic role (50%) compared to those that struggle with access (29%).
Why ModelOps Matters More Than Ever
Model operations (ModelOps) has become a critical discipline for organizations seeking to scale their analytical AI. At its core, ModelOps encompasses the practices, processes and technologies used to manage models throughout their life cycle — from development and deployment to monitoring and maintenance. The goal is to ensure models are effectively integrated into production environments and deliver consistent, reliable results.
According to the authors of "Rewired" ModelOps is needed so that AI can scale. They say, “AI /ML models require constant monitoring, retraining and debiasing — a challenge with even a few ML models but simply overwhelming with hundreds of them.”
Building Stronger Model Management Practices
Despite the importance, most organizations today still struggle at managing their models. In 2025, a third of organizations reported having between one and 25 models in use. While more than half openly shared, they did not actually know how many models were operating within their enterprises. This lack of awareness underscores a clear governance gap: models may be delivering business value, but organizations lack the oversight structures to manage them effectively. Making things even more complex will be accounting for agents and agents connected to models.
When asked about ModelOps priorities, organizations consistently put governance at the top. Key requirements include life cycle management, lineage tracking and version control — capabilities that enable traceability, reproducibility and rollback. Monitoring, alerting and CI/CD pipelines are also high on the list, reflecting the need for automation and observability in production environments.
Collaborative tools and searchable repositories highlight a shift toward co-creation and shared model libraries. While adoption of ModelOps remains uneven, those investing are focusing on fundamentals that establish trust, accountability and scalability. The real challenge is moving from fragmented oversight and limited awareness to disciplined, enterprise-wide practices that treat models as core business assets. Looking ahead, the authors believe ModelOps will soon extend beyond analytical models to include both agentic and generative AI.
Related Article: AI Governance Isn’t Slowing You Down — It’s How You Win
The Future Belongs to Well-Governed Models
Analytical AI may not capture the spotlight, but it remains the foundation of trusted, data-driven enterprises. With analytical AI in the mix, organizations can change how they compete.
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As analytical models are embedded into agentic workflows and AI adoption scales, the need for strong governance, leadership and operational discipline will intensify. ModelOps is emerging as the bridge — ensuring models are not only developed but also managed, monitored and aligned with business goals.
The future of AI will depend less on raw capability and more on whether organizations can elevate every form of AI from a supporting role to a strategic cornerstone. Achieving this requires robust ModelOps practices that treat models as enduring business assets.
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