When people talk about the artificial intelligence (AI) revolution, they usually imagine flashy chatbots or Super Bowl–ready demos. But as Mark Talbot,  Director of Architecture and AI at Appian, points out inhis article on ERP Today, the AI that’s truly transforming businesses is… boring. Quiet, reliable, and deeply embedded into core workflows, this is where real ROI happens.

Talbot calls it “boring AI” because it focuses on the back-end processes that actually run the business: enterprise resource planning (ERP) systems, scheduling, billing, and cross-platform coordination. The flashy AI pilots—generative chat tools or marketing-focused bots—are failing 95% of the time because they aren’t integrated into daily work. Employees rarely use them, and executives end up measuring hype instead of measurable outcomes. The magic of boring AI is that it works where it counts. In ERP environments, it reconciles data across systems, automates financial reporting, streamlines supply chain operations, and handles repetitive administrative tasks. For example, a healthcare provider using AI to automate patient intake and documentation reduced waitlist times by over 80%—all without dramatic fanfare. Talbot emphasizes that the real value of AI comes from embedding it into existing processes, not forcing users to adopt separate tools. Governance, audit trails, and compliance should be built in from day one, making AI trustworthy and risk-free. Low-code platforms often make this easier, letting organizations maintain transparency and human oversight without slowing adoption.

The takeaway for IT leaders: the AI worth investing in isn’t flashy—it’s operational. It quietly accelerates ERP-driven finance, HR, and supply chain processes, reduces errors, ensures compliance, and frees teams to focus on higher-value work. In other words, boring AI is the AI that works.

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Artificial Intelligence (AI) is no longer just a “nice-to-have” in logistics—it’s becoming a core driver of operational efficiency when integrated with enterprise resource planning (ERP). In a recent ERP Today article, Chris Vavra explains how manufacturers and distributors are combining AI with ERP to reduce costs, improve delivery performance, and stay resilient amid supply chain disruptions.

Companies like Aptean are embedding AI directly into transportation, warehouse, and demand management workflows, moving operations from reactive problem-solving to continuous optimization. For example, a midmarket food and beverage producer saw a 12% drop in empty miles, 9% lower fuel spend, and 20% better on-time delivery within six months by integrating AI-driven route optimization into its ERP. Similarly, warehouse management systems with AI are reallocating labor dynamically, boosting picking productivity.

For IT leaders, this shift changes daily responsibilities. Teams focus less on reconciling siloed data and more on validating AI recommendations, fine-tuning constraints, and managing exceptions that require human judgment. Early adopters emphasize the need for disciplined data governance, master data standardization, and phased rollouts—starting with high-impact areas like route optimization before scaling to predictive inventory and warehouse orchestration.

Vavra also notes that vendor evaluation is critical: leaders should prioritize transparent AI models, ERP integration, industry-specific accelerators, and continuous model tuning. Ultimately, ERP platforms that unify logistics data and embed AI natively will define the next wave of competitive advantage, while organizations that delay data cleanup or integration risk falling behind.

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Manufacturing ERP is shifting from passive recordkeeping to active execution. ERP Today’s senior editor Chris Vavra explains in a recent article how new technologies and regulatory pressures are redefining ERP’s role—from system of record to system of action.

Vavra identifies three converging trends reshaping ERP strategy and day-to-day operations:

• Agentic operations replace AI copilots
  ERP systems are moving beyond recommendations to autonomous action. Software agents can reroute production, issue purchase orders, trigger maintenance, and resolve routine exceptions without human prompts. Leaders spend less time entering transactions and more time setting policies, guardrails, and approval thresholds. Early adopters report reduced downtime and productivity gains as agents handle predictive maintenance, scheduling, and high-volume transactional work.
• Composable and headless ERP architectures
  Instead of large, disruptive upgrades, organizations are adopting modular, API-driven ERP stacks. This allows teams to add or swap capabilities—such as demand planning engines or MES functions—without destabilizing core systems. Executive focus shifts toward integration design, vendor orchestration, and data governance rather than monolithic customization.
• The rise of the sustainability ledger
  With new regulations taking effect in 2026, sustainability data is becoming as critical as financial data. Emissions, waste, and resource usage must be accurately captured, auditable, and tied to every batch and shipment. Poor data quality now represents a compliance risk, not just a reporting issue.

The message for ERP insiders is clear: success in 2026 will depend on governing autonomous systems, managing modular ecosystems, and treating sustainability data as a first-class ERP workload.

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