공정 제조업에서의 디지털&AI 도입(2026년)

A 112-page report on how process manufacturers (chemicals, metals, pulp & paper, …) are adopting digital tools across their operations with a focus on AI adoption.

Questions answered

• What are the top priorities and challenges for process manufacturers in their digital transformation journey?
• At what stage are process manufacturers in adopting key technologies and use cases?
• Where is software predominantly deployed (on-premises vs. cloud), and which applications are migrating to the public cloud?
• What are the main challenges in migrating to cloud-based manufacturing software?
• To what extent do process manufacturers expect AI to impact their core applications over the next 3–5 years?
• Which use cases are expected to have the biggest impact and how large are the expected cost savings?
• How frequently is AI used in R&D, and what are the key barriers to adoption?
• What are the biggest workforce challenges, and which AI tools are expected to address them?

Companies mentioned

• Apollo Tyres
• BASF
• Borouge
• Dow Chemicals
• Forza Steel
• Georgia-Pacific
• Honeywell
• Norsk Hydro
• Yokogawa

About the report

Process manufacturing organizations face increasing pressure from energy price volatility, carbon costs associated with the energy transition, and shrinking skilled labor pools. While these industries have historically trailed discrete manufacturing in digital adoption, the data-driven era of Industry 4.0 is driving a shift toward customized production at scale and flexibility.

The Digital & AI adoption in Process Manufacturing 2026 report provides a structured analysis of how process manufacturers are integrating digital tools and AI across their operations. Based on a survey of a large group of senior stakeholders across several industries and major world regions, the research outlines current technology priorities, deployment stages, and ROI expectations for the coming years.

Report at a glance

• Adoption report: Details the adoption of digital tools across process manufacturing operations with a focus on AI.
• Stakeholder insights: Comprises data from senior decision-makers, including CxOs and directors, at organizations with more than 1,000 employees.
• Industry breadth: Analyzes several process manufacturing sub-sectors, including general chemicals, pulp and paper, petrochemicals, rubber and plastics, basic metals, fertilizers, and non-metallic minerals.
• Technology and use case tracking: Examines the deployment status of various technologies and operational use cases.
• ROI models: Details expected average cost reductions from digital initiatives by 2028.

Key areas of analysis

• Transformation priorities: Identifies revenue growth and operational efficiency as the primary drivers, with a vast majority of surveyed manufacturers rating each as a top or significant priority.
• Technology adoption landscape: Outlines the widespread deployment of smart sensors and process automation, concurrently identifying AI optimization and AI-driven R&D optimization as leading exploration areas.
• Software and cloud migration: Details that foundational applications such as SCM and process control have reached near-universal adoption. Concurrently, it indicates that migration to the public cloud remains slow due to high costs cited by most respondents.
• AI impact expectations: Examines where AI is expected to have the strongest impact, with predictive quality analytics and energy management ranking at the top.
• AI in R&D deep dive: Details how a portion of manufacturers utilize AI tools in research to address manual data processing challenges that affect a majority of organizations.
• Frontline workforce solutions: Analyzes pressing workforce gaps, such as remote support and real-time troubleshooting, and evaluates the role of generative AI in addressing these challenges.
• Case studies in transformation: Features detailed implementations from BASF, Forza Steel, Apollo Tyres, Borouge, and Georgia-Pacific.

Table of Contents

1. Executive summary

• Executive summary (4 parts)
• Summary: Where process manufacturers are in their digital maturity journey
• Key action items from this report

2. Introduction

• Introduction: Chapter overview
• Historical context: Manufacturing has changed in several technology waves
• Interest in smart manufacturing and Industry 4.0 has been growing
• Today’s technology priorities: Security, automation, software and AI
• The vision for the coming years: Automated industrial sites that are scalable and serviceable
• Process manufacturing is uniquely different to discrete manufacturing
• Every process manufacturing industry has unique characteristics
• Overarching Challenge 1, Challenge 2, Challenge 3
• Process industries are typically digital laggards in manufacturing
• Now CEOs of process manufacturers are driving AI adoption
• Case study: How BASF is adopting AI (4 parts)
• Case study: How Borouge is building the industry’s first AI-powered autonomous chemicals facility
• This report examines how process manufacturers adopt digital technologies
• Sample companies that took part in the survey

3. Priorities of process manufacturers

• Priorities of process manufacturers: Chapter overview
• Priorities for transforming operations (3 parts)
• Corporate priorities: Example 1—Norsk Hydro focuses on Trend 1, Trend 2, Trend 3, and Trend 4
• Corporate priorities: Example 2—Dow Chemicals focuses on Trend 1, Trend 2, Trend 3, and Trend 4

4. State of digital in process manufacturing

• Chapter definitions (2 parts)
• State of digital in process manufacturing—Technology and use cases: Chapter overview
• Technology adoption (4 parts)
• Use case adoption (3 parts)
• State of digital in process manufacturing: Software landscape: Chapter overview
• Software applications (4 parts)
• Challenges when migrating to the cloud (2 parts)
• State of digital in process manufacturing: Challenges: Chapter overview
• Roadblocks in adopting digital technologies (3 parts)
• State of digital in process manufacturing: ROI expectations—Chapter overview
• Digital transformation cost savings (3 parts)
• Impact of technologies on OEE (2 parts)
• Cost savings by digital transformation activity (3 parts)

5. The role of AI for process manufacturers

• The role of AI for process manufacturers: Chapter overview
• Starting point for AI: 4 things on the minds of process manufacturing executives
• Example: How a steel manufacturer created a data foundation for AI
• Importance of data & analytics in process manufacturing
• Exploration of AI technologies
• Impact of AI in core applications (2 parts)
• Generative AI use cases in process manufacturing

6. Deep-dive: AI in R&D

• AI in R&D: Chapter overview
• Use of AI tools in R&D (2 parts)
• Barriers in adopting AI-driven R&D methods (3 parts)
• R&D concerns and challenges (3 parts)
• Impact of AI in R&D activities (3 parts)
• How a tire manufacturer accelerated R&D with AI-driven virtual prototyping

7. Deep-dive: AI for workforce challenges

• AI for workforce challenges: Chapter overview
• Workforce Challenge 1
• AI solutions to workforce challenges
• How a pulp and paper company created a chatbot for operator guidance

8. Methodology

9. About IoT Analytics