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Industrial cybersecurity was the second-most selected AI investment priority in Cisco’s 2026 survey of industrial professionals, behind process automation and efficiency. That is a measure of what respondents prioritized—not a share of their budgets, a spending recommendation, or proof that one security investment delivers better results than another.
What did Cisco’s survey find?
Cisco asked, “What are your top priority areas for AI investment in industrial operations?” Respondents could select up to three answers. In that multiple-selection question, process automation and efficiency came first, industrial cybersecurity second, and supply chain and logistics optimization third.
| AI investment priority | Respondents selecting it | Rank among reported priorities |
|---|---|---|
| Process automation / efficiency | 41% | 1 |
| Industrial cybersecurity | 32% | 2 |
| Supply chain / logistics optimization | 30% | 3 |
| Automated quality inspection | 29% | 4 |
| Energy optimization / sustainability | 26% | 5 |
| Predictive maintenance | 22% | 6 |
These percentages are the shares of surveyed respondents who selected each option, as reported by Cisco in 2026; because respondents could choose up to three, the figures do not add up to a budget allocation or a single-choice ranking. The results show cybersecurity as a prominent concern within a broader set of AI-related priorities, not as the only reason organizations are investing.
The global survey was conducted by Cisco with Sapio Research and covered more than 1,000 industrial professionals and OT decision-makers across 19 countries and 21 industrial sectors. Cisco announced the findings on April 7, 2026. The findings are vendor-published survey results and reflect respondents’ stated priorities.
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Why does cybersecurity feature in AI investment planning?
Industrial AI systems may depend on data moving between operational technology (OT), enterprise IT, edge devices, and cloud services. In environments where equipment must remain available and safe, adding connectivity and AI workloads can raise questions about visibility, access, segmentation, and how teams respond to incidents.
Cisco reports that 98% of respondents consider cybersecurity foundational to AI-ready infrastructure, while 40% identify it as the biggest obstacle to scaling AI. These are survey responses, not independent measurements of security risk across all industrial organizations. They help explain why security appears both as an investment priority and as a scaling concern.
The survey also found that 85% of respondents expect AI to improve their cybersecurity posture. That figure describes an expectation, not a demonstrated outcome: the survey finding does not establish that AI deployments have delivered better security or that AI itself reduces risk. AI-enabled security tools still need validation, oversight, and integration with an organization’s operational response processes.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWhat infrastructure may need attention before scaling industrial AI?
Cisco reports that 97% of respondents expect AI workloads to affect industrial network requirements, and 96% say wireless networking is essential to enabling AI in their operations. The report also identifies connectivity, reliability, wireless, edge computing, and power as factors that affect the ability to scale AI in physical operations.
Those figures indicate perceived infrastructure needs among survey respondents; they do not show that every facility needs the same upgrade. A plant with adequate coverage and resilient connectivity may face different constraints from one with legacy equipment, gaps in asset visibility, or limited edge capacity. Map the actual workload and operating conditions before choosing network changes.
Cisco’s AI-ready industrial networking materials describe wired and wireless connectivity, an expanded Industrial Ethernet portfolio, unified management and assurance, and integrated cybersecurity features. These are Cisco’s descriptions of its own offering, not an independent comparison or proof that a particular product is suitable for a facility. See Cisco’s AI-ready industrial networking overview.
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How should an organization evaluate its investment options?
Cisco’s survey does not prescribe spending levels, provide comparative return on investment, or rank vendors and products. The following is an editorial framework for comparing needs and options; it is not a Cisco recommendation. Use it to connect AI ambitions to operational risks and measurable outcomes.
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- Identify the operational use case. Specify what the AI system is expected to do—such as improve process efficiency, inspect quality, optimize energy use, or support predictive maintenance—and which assets, data, and network paths it depends on.
- Establish asset and data visibility. Determine whether teams can identify relevant OT devices, understand their communications, and see how data moves between OT and IT. Account for both legacy and newer equipment.
- Assess security controls and constraints. Review segmentation and enforcement options alongside uptime, safety, and change-control requirements. A control that disrupts a critical process may be unsuitable even if it is technically available.
- Check network fit at the site level. Evaluate reliability, latency, wireless coverage, edge capacity, power, and interoperability with installed equipment against the specific AI workload and facility conditions.
- Plan ownership and incident response. Clarify who manages network access, monitors alerts, approves changes, and coordinates response across IT and OT. Shared visibility and workable procedures matter alongside hardware and software.
- Define measures of success. Set operational and security measures before deployment, then check whether the investment meets them without unacceptable effects on safety, availability, or maintenance demands.
When comparing providers or approaches, these same dimensions—visibility, operational fit, segmentation, network performance, interoperability, cross-team workflows, maintenance demands, and measurable outcomes—are useful evaluation criteria. Cisco’s cited materials do not score options against them or establish comparative ROI. Cisco discusses network modernization, asset visibility, segmentation, and IT/OT coordination as parts of its own approach; that framing should be distinguished from neutral evidence about the whole market.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the survey say about other AI priorities?
Cybersecurity sits alongside investments aimed at production and operations, rather than replacing them. In Cisco’s 2026 results, respondents also selected supply chain and logistics optimization (30%), automated quality inspection (29%), energy optimization and sustainability (26%), and predictive maintenance (22%). These are selections in the same multiple-choice question, not budget shares.
The mix matters for planning: an organization may need to weigh security and network readiness against specific operational goals, while recognizing that the survey does not say how any individual company should divide its resources. Local risk, equipment, workforce, and business objectives determine which investments make sense.
Quick Recap
What the findings do—and do not—establish
- They establish: among respondents to Cisco’s 2026 survey, industrial cybersecurity ranked second among the listed AI investment priorities, with 32% selecting it.
- They do not establish: an industry-wide budget rule, a recommended cybersecurity spend, comparative product performance, or proof that AI has improved respondents’ security posture.
- They are best used as: evidence that industrial professionals surveyed view cybersecurity and infrastructure readiness as important to AI plans, to be weighed against the needs and risks of each organization.
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