The first half of 2026 has passed and one of the topics that has taken the digital signage industry by storm is AI. Almost every product presentation, roadmap, and strategy update centers around AI. The possibilities are huge. AI can support software development, automate operations, improve content workflows, and increase productivity across your organization. It has become a hygiene factor. Vendors that fail to integrate AI into their platforms risk losing relevance.
At the same time, customers want choice. AI capabilities must be modular. Enterprises need the option to enable, disable, or replace vendor-provided AI with their own approved frameworks and services.
Amid all the excitement, four important topics are receiving less attention than they should.
• AI and Cybersecurity
• AI and sustainability
• AI and governance
• AI and cost
AI and cybersecurity: the new attack surface
For many years, digital signage cybersecurity has focused on media players, networks, and remote device management. AI changes that. Large-scale language models, AI-generated content, and automated workflows create entirely new attack surfaces.
A compromised AI service could do more than manipulate a single display. They can access content libraries, generate misleading messages at scale, or expose sensitive business data through improperly configured integrations. As digital signage platforms connect AI services with CMS platforms, CRM systems, and enterprise applications, the separation between signage infrastructure and business-critical systems is becoming increasingly blurred.
AI also enhances cybersecurity. Automated threat detection, anomaly monitoring, and predictive analytics help operators identify risks early. But AI security is no longer just an IT issue. All AI capabilities require clear controls over data access, model permissions, and human oversight. Future security audits will need to evaluate not only devices and networks, but also the AI models and services that support daily operations.
AI and sustainability: efficiency comes at a price
AI promises efficiency. Optimize content scheduling, automate repetitive tasks, improve targeting, and help carriers manage their networks more effectively. AI analytics can also reduce energy consumption by adjusting brightness levels, operating times, and content delivery based on viewer behavior.
The industry rarely discusses the other side of the equation.
AI is resource intensive. Training and operating large models requires computing power, data center capacity, and electricity. AI-generated campaigns, automated translations, content recommendations, or analytical queries all create additional processing demands. Most of these costs remain invisible to customers.
For the digital signage industry, sustainability has two dimensions. Companies need to measure both the efficiency gains enabled by AI and the environmental footprint created by their AI infrastructure. As sustainability reporting becomes standard practice, the energy consumption behind AI services will come under greater scrutiny.
AI and governance: From innovation to accountability
Digital signage vendors have adopted AI with remarkable speed. Product announcements are increasingly focused on automated content creation, intelligent recommendations, and AI-supported network management. Governance has not kept up.
Questions about ownership of AI-generated content, accountability for errors, transparency, and regulatory compliance often emerge after deployment rather than during development. This approach poses risks, especially for enterprise customers operating in regulated industries or public environments.
Governance will be a key purchasing criterion. Customers need clarity on where the data is processed, what models are used, how the output is validated, and who is responsible if the AI makes a mistake. Retailers, carriers, healthcare providers, and public agencies face increasing compliance requirements.
Flexibility is essential no matter which AI capabilities vendors develop. Customers are increasingly demanding the ability to connect their own certified AI environments with approved enterprise models. Therefore, AI needs to become a modular layer within the platform, rather than a required component that the customer has no control over.
AI and Cost: The Business Model No One Talks About
The most important issue in AI may be the least discussed. It’s about who pays for AI.
Over the past 18 months, vendors have added AI-assisted content creation, translation, analytics, workflow automation, and conversational interfaces at an unprecedented pace. Many of these features appear to be included at no additional cost. The underlying economics tell a different story.
Unlike traditional software features, AI incurs ongoing operational costs. Cloud computing, token-based model licensing, data processing, and infrastructure usage incur recurring costs every time a user interacts with an AI service. As usage increases, these costs also increase.
The industry will soon have to make difficult commercial decisions. Vendors cannot absorb unlimited AI consumption, but customers increasingly expect AI capabilities as part of their existing subscriptions. Software will move from fixed licenses to pay-as-you-go pricing, and pay-as-you-go pricing models may become more common.
At the same time, AI on the edge is also gaining momentum. Local AI servers reduce reliance on cloud-based tokens, reduce latency, and improve responsiveness. It also gives customers more control over their data and operations. For many enterprise deployments, locally managed AI environments can offer lower operating costs and stronger security than fully cloud-based alternatives.
The digital signage industry is embracing AI. The next phase is not about innovation, but about economics. Success will depend on secure architectures, responsible governance, measurable sustainability, and business models that can support AI at scale.
