What Manufacturers Should Look for in a Modern ERP Platform

Manufacturers are facing growing pressure to improve efficiency, increase visibility, and support growth without adding complexity. As labor constraints, rising costs, and supply chain challenges continue, many organizations are rethinking whether their current systems can support the business moving forward.

In this Nucleus Research report, explore the trends shaping the ERP market and learn what manufacturers should consider when evaluating solutions to support operational performance, automation, and future growth.

You'll learn:

  • How manufacturers are using ERP to improve operational visibility and coordination
  • Why automation, AI, and usability are becoming key evaluation criteria
  • What capabilities help organizations scale without increasing complexity
  • Which ERP vendors are recognized as market leaders

Whether you're planning a modernization initiative or simply exploring what's changing in the ERP landscape, this report provides valuable insight into what leading manufacturers are looking for in today's ERP platforms.

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How Manufacturing ERP Solutions Compare in 2026

Manufacturers evaluating ERP solutions face no shortage of options. As operations become more complex and the need for visibility, automation, and agility grows, choosing the right platform has never been more important.

In this G2 Grid® Report, explore how leading mixed mode ERP solutions compare based on customer satisfaction, market presence, and feedback from real users. The report highlights the platforms manufacturers rely on to support production, supply chain, inventory, quality, and business operations.

You'll learn:

  • Which ERP solutions are recognized as leaders in the market
  • How real users rate leading mixed mode ERP platforms
  • Key capabilities manufacturers should consider during ERP evaluations
  • How top solutions support complex manufacturing environments

Whether you're actively evaluating ERP systems or planning for future modernization, this report provides valuable insights to help guide your research.

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How to Deploy AI on the Shop Floor—and Scale with Confidence

Manufacturers are under increasing pressure to do more with less. Labor shortages, production complexity, supply chain disruption, and rising customer expectations are pushing organizations to explore AI—but success depends on turning operational data into faster, more informed decisions.

In this IDC PeerScape report, discover how manufacturers are using agentic AI to connect people, machines, and data in practical ways that improve productivity, decision-making, and operational resilience.

You’ll learn how to:

  • Turn disconnected data into real-time insights and actions
  • Help frontline teams make faster, more informed decisions
  • Improve production, scheduling, and resource utilization
  • Automate quality and operational workflows

Whether you're just beginning to explore AI or looking to expand existing initiatives, this report offers practical examples and guidance from manufacturers already putting these technologies to work.

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Unlocking AI’s Potential Through Context Management

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

  • Understand the fundamental differences between metadata for humans and context for AI agents
  • Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
  • Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
  • Explore the transformative possibilities that context management will unlock for enterprise AI
View Now

Unlocking AI’s Potential Through Context Management

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

  • Understand the fundamental differences between metadata for humans and context for AI agents
  • Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
  • Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
  • Explore the transformative possibilities that context management will unlock for enterprise AI
View Now

How AI Code Fails at Scale and What Your Team Can Do About It

AI coding tools promise faster development, but without proper oversight, speed becomes a liability. When Amazon and Microsoft both faced major production failures from AI-assisted code, the lesson was clear: code generation is not the same as code understanding.

This white paper examines real-world incidents where AI-generated code passed initial checks but caused cascading system failures - and what engineering leaders can do to prevent it.

In this white paper, you'll learn:

  • Why AI-generated code looks reliable but fails under production complexity
  • Three critical AI programming limitations every dev team should understand
  • How unchecked speed leads to cascading production failures
  • A practical governance framework for safely integrating AI tools into your development workflow

Whether you're evaluating AI coding tools or already using them, this guide gives you the clarity to move fast without breaking production.

Get Whitepaper

How AI Code Fails at Scale and What Your Team Can Do About It

AI coding tools promise faster development, but without proper oversight, speed becomes a liability. When Amazon and Microsoft both faced major production failures from AI-assisted code, the lesson was clear: code generation is not the same as code understanding.

This white paper examines real-world incidents where AI-generated code passed initial checks but caused cascading system failures - and what engineering leaders can do to prevent it.

In this white paper, you'll learn:

  • Why AI-generated code looks reliable but fails under production complexity
  • Three critical AI programming limitations every dev team should understand
  • How unchecked speed leads to cascading production failures
  • A practical governance framework for safely integrating AI tools into your development workflow

Whether you're evaluating AI coding tools or already using them, this guide gives you the clarity to move fast without breaking production.

Get Whitepaper

How to Deploy Agentic AI on the Shop Floor—and Scale with Confidence

Ready to move beyond AI pilots? As manufacturing grows more complex and unpredictable, traditional approaches can’t keep up. Before scaling AI, organizations need the right foundation to turn data into real-time, actionable decisions.

In this IDC PeerScape report, discover how manufacturers are deploying agentic AI to connect people, machines, and data—enabling smarter, faster decisions directly on the shop floor. Learn how to move from isolated use cases to a more resilient, adaptive operation.

You’ll learn how to:

  • Turn fragmented data into real-time, guided actions for operators
  • Augment frontline workers with AI-driven decision support
  • Optimize production, scheduling, and resources continuously
  • Automate quality and workflows with intelligent, closed-loop processes

If you’re exploring AI—or ready to scale—it provides a practical roadmap to transform operations and build a more resilient manufacturing business.

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The #1 ERP for Mixed Mode Manufacturing

Manufacturers face numerous challenges, especially when operating in a mixed mode environment:

  • Complex production schedules
  • Supply chain volatility
  • Outdated legacy systems

By treating these as interconnected issues, manufacturers can gain a competitive advantage.

Access this G2 Grid® Report for Mixed Mode ERP to see why Epicor Kinetic, the AI-powered ERP, is rated the #1 solution for mixed mode manufacturers in the mid-market.

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How a Leading Health System Simplified Employee Benefits

See how Healthee helped a leading health system increase HDHP enrollment by 38.9% and drive a 210% surge in benefits engagement—all in just 42 days.

With a massive, diverse workforce—from desk-bound admins to front-line providers—this leading health system faced a classic benefits paradox: too many options, not enough clarity. HDHP adoption led to overinsurance, while fragmented communication left the team overwhelmed with over 21,000 manual inquiries.

The leading health system partnered with Healthee to deploy Zoe, an AI benefits assistant. In a record-breaking 6-week rollout, Healthee simplified the complex:

  • Personalized Guidance: Achieved a 210 percent rise in total enrollment inquiries.
  • Frictionless Enrollment: A 16.7% increase in total completed enrollments.
  • Educated Choice: Significant enrollment growth across all voluntary benefits, including a 16.7% lift in Hospital Indemnity.
View Now

Unlocking AI’s Potential Through Context Management

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

  • Understand the fundamental differences between metadata for humans and context for AI agents
  • Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
  • Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
  • Explore the transformative possibilities that context management will unlock for enterprise AI
View Now

Unlocking AI’s Potential Through Context Management

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

  • Understand the fundamental differences between metadata for humans and context for AI agents
  • Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
  • Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
  • Explore the transformative possibilities that context management will unlock for enterprise AI
View Now

Unlocking AI’s Potential Through Context Management

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

  • Understand the fundamental differences between metadata for humans and context for AI agents
  • Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
  • Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
  • Explore the transformative possibilities that context management will unlock for enterprise AI
View Now

How a Leading Health System Simplified Employee Benefits

See how Healthee helped a leading health system increase HDHP enrollment by 38.9% and drive a 210% surge in benefits engagement—all in just 42 days.

With a massive, diverse workforce—from desk-bound admins to front-line providers—this leading health system faced a classic benefits paradox: too many options, not enough clarity. HDHP adoption led to overinsurance, while fragmented communication left the team overwhelmed with over 21,000 manual inquiries.

The leading health system partnered with Healthee to deploy Zoe, an AI benefits assistant. In a record-breaking 6-week rollout, Healthee simplified the complex:

  • Personalized Guidance: Achieved a 210 percent rise in total enrollment inquiries.
  • Frictionless Enrollment: A 16.7% increase in total completed enrollments.
  • Educated Choice: Significant enrollment growth across all voluntary benefits, including a 16.7% lift in Hospital Indemnity.
View Now

Unlocking AI’s Potential Through Context Management

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

  • Understand the fundamental differences between metadata for humans and context for AI agents
  • Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
  • Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
  • Explore the transformative possibilities that context management will unlock for enterprise AI
View Now