2026年工业AI状况报告：公用事业篇

Introduction Over 175 utility decision makers participatedin the 2026 State of Industrial AI Report. Following the success of the inaugural 2024 State of Industrial NetworkingReport, this edition looks at how utility firms worldwide are adopting ArtificialIntelligence; the challenges they face; and the opportunities for AI-poweredtransformation. We spoke to decision-makers at firms in 19 countries,operating with annual revenues over $100 million. This report provides a utilities-specific view of the2026 State of Industrial AI research, highlightingwhere the sector is seeing progress, whereconstraints remain, and what foundations arerequired to move from targeted deploymentsto secure, repeatable scale. Cisco, in association with Sapio Research, undertook this study to establish levelsof AI adoption in industrial networking; the operational outcomes organizationsare achieving from AI investments; barriers to scale; and how to align for success. Executive summary Industrial AI demands networkmodernization Cybersecurity is both the #1 barrierand the #1 asset IT/OT collaboration is critical to AIat scale Expanding AI adoption elevates cybersecurity risk. Effective collaboration between IT and OT teamsdirectly impacts AI outcomes. AI implementation is placing unprecedenteddemands on utilities’ underlying infrastructure. ●41% cite cybersecurity concerns as a topobstacle to AI adoption●49% identify security as their biggestnetworking challenge●87% expect AI to improve theircybersecurityposture ●44% continue to operate with limited or no IT/OT cooperation●43% cite siloed IT / OT ownership as a majorchallenge to AI adoption●33% say OT domain expertise is critical toscaling AI ●52% of utilities expect significant increases inconnectivity and reliability requirements●97% of decision-makers say reliable wirelessnetworks are vital for enabling AI●Reliable connectivity (52%), bandwidth(46%), and mobility (39%) are top networkrequirements for AI at scale While security gaps are limiting AI scale today,utilities view AI as a tool to strengthen detection,monitoring, and resilience. While disparate teams slow AI deploymentand increase operational risk, IT/OT alignmentaccelerates scalability, stability, and security. Network readiness now determines AI success,with infrastructure limitations directly constrainingthe ability to scale deployments. Table of contents Implications for AI scale in utilities20Section 4: Cybersecurity & industrial AI interconnectedness21Cybersecurity is the #1 obstacle to AI adoption22Cybersecurity as a foundational requirement23Cybersecurity threats of greatest concern24AI strengthens cyber defense25Section 5: IT/OT collaboration – the operating model for scaling AI26IT/OT collaboration in utilities remains uneven27Independent teams limit AI confidence & outcomes28Skills required to scale industrial AI29Section 6: Future outlook – scaling AI across industrial operations30Confidence in scaling industrial AI31How transformational will AI be for utilities?32Section 7: Key takeaways for utility leaders33Priorities for utility leaders34Section 8: Industrial AI partner considerations for utilities35Section 9: Demographics & firmographics37 Introduction2Executive summary3Introduction letter5Section 1: Sector overview6AI adoption has reached active deployment7Operational improvements drive AI adoption8AI adoption evolves from efficiency to resilience9Cybersecurity concerns limit AI adoption10Section 2: AI outcomes, ROI & investment urgency11Utilities expect AI to deliver operational and sustainability gains12Energy management as a core AI use case13AI investment brings high expectations14Investment priorities for enabling AI15AI investment priorities shift with maturity16Section 3: AI runs on the network: infrastructure as the foundation for scale17AI adoption rewrites industrial infrastructure requirements18AI is coupled to grid operations and network visibility19 Introduction letter I’m excited to introduce Cisco’s 2026 State of Industrial AI Report– an evolution of our State of Industrial Networking Report. Asindustrial operations continue to transform, artificial intelligenceis rapidly emerging as a gamechanger across manufacturing,utilities, transportation, and beyond. 2025 was the year for manyto experiment with AI in industrial settings; 2026 promises tobe the year when many organizations move from pilots to real,production-ready AI projects. We’re seeing companies bring AI to life in impactful ways:from deploying machine vision to ensure product quality inmanufacturing, to rolling out AI-powered automated guidedvehicles (AGVs) and autonomous mobile robots (AMRs) that arereshaping material handling and logistics, to leveraging agenticoperations that drive more autonomous, adaptive, and efficientworkflows across industrial environments. Additionally, AI isplaying an increasingly critical role in cybersecurity for OT, wherethe scale and complexity of machine data demand intelligen