How AI, automation and data can be deployed to drive better decision-making
By the DartPoints Product & Solutions Team
AI and advanced analytics are only as effective as the infrastructure and operational discipline behind them. At a recent Cincinnati Business Courier Data Driven Forum, industry leaders discussed how organizations are moving beyond experimentation and putting data, automation, and AI to work in real business environments.
DartPoints’ VP of Solution Architecture, Matt McKee, joined the panel to share a pragmatic view from the infrastructure layer. The discussion focused on what it actually takes to support AI-driven decision-making at scale, including data governance, workload placement, and resilience in environments where performance and availability matter.
This article recaps key themes from the conversation and highlights where enterprises are focusing as AI moves from pilot projects to production workloads.
Key Takeaways:
1. AI readiness starts with infrastructure discipline, not algorithms
Enterprises that succeed with AI focus first on where data lives, how it moves, and how reliably systems perform under load. Without that foundation, even the best models fall short.
2. Data governance is a prerequisite for trusted decision-making
AI amplifies both insight and risk. Clear data ownership, security controls, and compliance frameworks are essential to ensure analytics drive confident decisions rather than create exposure.
3. Not all AI workloads belong in the public cloud
Performance-sensitive, latency-aware, or regulated workloads often require dedicated infrastructure closer to users and data sources. Hybrid and regional strategies are becoming the norm, not the exception.
4. Resiliency enables AI at scale
As organizations rely on AI for operational and strategic decisions, uptime and recoverability matter more than ever. Infrastructure must be designed to withstand failures without disrupting critical workloads.
5. Execution beats experimentation
Enterprises are shifting from proof-of-concepts to production environments that support real business outcomes. That shift demands predictable performance, transparent costs, and infrastructure that can scale without rework.
