- Forum hosted by the Cincinnati Business Courier focused on deploying AI, automation and data from back office to production line.
- Experts stressed that poor data quality and weak governance are the biggest barriers to better decision-making.
- Practical use cases highlighted: predictive maintenance, inventory optimization and automated compliance checks.
- Immediate takeaways: start small, map decision workflows, build cross-functional teams, measure outcomes.
Forum Spotlight: Turning Data into Better Decisions
Why the urgency?
At a recent forum organized by the Cincinnati Business Courier, business and technology leaders converged to discuss a pressing theme: how organizations can deploy AI, automation and data to improve performance, reduce risk and stay competitive. Panelists framed the discussion around the reality that data-driven decision-making is no longer optional — it’s a strategic necessity.
Common barriers the panel identified
1. Data quality and governance
Speakers emphasized that many initiatives fail not because models are faulty, but because underlying data is incomplete, inconsistent or siloed. Establishing clear governance — ownership, standards and lineage — was presented as the foundational step for reliable decision systems.
2. Overlooking people and process
Automation and AI excel when paired with redesigned workflows. The forum warned against “spray-and-pray” technology deployments that leave users confused; instead, leaders should invest in training, change management and role redesign so teams can adopt new tools effectively.
3. Short-term thinking
Panelists urged organizations to move beyond pilots that never scale. Successful programs define measurable outcomes, tie projects to business KPIs (cost, uptime, cycle time, compliance) and plan for integration into production environments from the outset.
High-impact use cases discussed
- Predictive maintenance on the production line to reduce unplanned downtime and extend equipment life.
- Inventory and supply-chain optimization using real-time data to lower carrying costs and improve fill rates.
- Automated compliance and risk monitoring to detect anomalies and reduce regulatory exposure.
Practical first steps recommended
Map decisions, not just data
Start by mapping the key decisions people make daily. That clarifies what data and models are required and where automation can have the biggest impact.
Build cross-functional teams
Bring together operations, IT, analytics and legal early to ensure solutions are usable, secure and compliant.
Measure and iterate
Define success metrics up front and iterate quickly. Use short feedback loops to validate assumptions and scale what works.
Bottom line
The forum made it clear that companies that invest in data quality, governance and pragmatic automation will gain measurable advantages — improved uptime, lower risk and faster decisions. Those that delay risk falling behind as competitors convert data into action. The message: start with small, measurable projects, secure executive buy-in, and treat data as a strategic asset.
Image Referance: https://www.bizjournals.com/cincinnati/news/2026/01/02/table-experts-ai-automation-data-deployed.html