Those of us who work with digital signage technology today may not be aware of its long history. What most of us would recognise as the beginning of ‘true’ digital signage probably dates from 1962, when Nick Holonyak Jr. of General Electric developed the first visible-spectrum LED. Plasma display panels were invented in1966 by Donald Bitzer and Gene Slottow at the University of Illinois. First adopted for use with for computer terminals, plasma technology would later highlight the potential of large-format flat-panel displays. In 1968, Hewlett-Packard developed the first LED display, using LEDs as indicator lights on electronic equipment. In short, the technological fundamentals of digital signage were developed up to 50 years ago, so what’s next?
Digital signage das been progressively developed by smarter technologies, richer experiences, sustainability demands, and the ongoing integration of software and data insights. As business organisations, educational institutions and leisure facilities are in increased competition for attention in both physical and virtual spaces, digital signage has become more than static screens – it’s an interactive communication necessity that drives customer engagement, operational efficiency, and measurable business outcomes. Display screens have become increasingly detailed and colour correct. Size, shape and functionality no longer present insurmountable obstacles, and supplementary technologies create a wide range of additional functions and capabilities.
Each innovation in display technology has spawned a new generation of applications and solutions. Today, digital signage investment decisions are increasingly factoring in environmental and economic considerations. Drivers of this trend include energy-efficient LED and advanced LCD technologies that reduce operational costs and carbon footprints. Modular and adaptive formats, from ultra-wide aspect ratios to flexible configurations which expand creative possibilities. Current and forthcoming generations of displays remain vivid and readable in all lighting conditions, even in outdoor or well-lit spaces, so helping sustain engagement throughout the day. These hardware innovations not only drive visual impact but also align with broader sustainability and lifecycle goals.
Emerging trends
Of late, the most exciting opportunities in the digital signage space stem from the integration of AI and IoT. AI and IoT developments are fundamental to the way that content is created, scheduled, delivered, and optimised. In particular, digital signage systems increasingly rely on:
- Predictive analytics to anticipate audience preferences and adjust messaging in real time
- Vision and sensor-based insights to estimate dwell time, gaze direction, and engagement, tuning content accordingly.
- Contextual personalisation adapts messaging on the fly based on environment, weather, traffic, and last-mile inventory data.
There is a marked shift from static broadcast signage to smart engagement hubs. These behave more like responsive digital assistants than fixed displays.
The latest solutions feature a clear move beyond the traditional ‘screen + playlist’ approach to experiential engagement. Features of these new solutions include:
- Touchless interfaces, including gesture and voice interactions, are becoming the preferred choice as hygiene and accessibility expectations rise.
- Augmented reality (AR) and projection technologies continue to blur boundaries, integrating the virtual and real words, with hybrid digital-physical environments for wayfinding, virtual try-ons, and product demos.
- Social media and real-time content integration brings external engagement into physical spaces, transforming signage into dynamic, community-aware environments.
These interactive formats deepen engagement and lengthen dwell time—a crucial metric for both retail conversions and brand recall.
Cloud solutions
Over the last 20 years, digital signage has largely been bought and sold on the basis of the display and signage player hardware. Greater functionality and integration with a wider rage of software applications, the emphasis is very much on the cloud, because:
- Centralised cloud dashboards enable businesses to manage distributed networks of screens with minimal IT overhead.
- Cloud-based architectures support rapid deployment, automated updates, and remote troubleshooting, reducing operational load.
- Security frameworks have matured, with encrypted content delivery and hardened endpoints addressing rising cyber threats.
This shift toward cloud-centric deployment reduces the total cost of ownership and supports rollouts where the client is a global enterprise.
A further consideration here is the roll of ‘big data’. The change here is the move from proactively collecting data, to actively leveraging it. Digital signage analytics can now be used to:
- Quantify audience engagement at scale.
- Optimise content schedules based on performance patterns.
- Connect signage metrics to broader business outcomes.
AI-augmented analytic al models can forecast effects, suggest optimal content mixes, and refine messaging strategy over time.
Changing attitudes
Going forward, digital signage will shift from being a communication tactic to a strategic business asset, supporting the customer experience, driving measurable outcomes, and harmonizing with broader IT and marketing ecosystems. Investing in intelligent platforms, rich analytics, and immersive formats will help organizations stay competitive and relevant in a constantly evolving engagement landscape. Digital signage will continue to be influenced by smarter technologies, richer experiences, sustainability demands, and the ongoing integration of software and data insights. As organisations compete for their share of attention in both physical and virtual spaces, digital signage has become more than static screens. It is fast becoming an organisations interactive communication backbone that driving customer engagement, operational efficiency, and measurable business outcomes.
Businesses and other and digital signage users will depend on AI for predictive personalisation. AI-augmented analytical models can forecast effects, suggest optimal content mixes, and refine messaging strategy over time. Predictive personalization is a marketing and customer experience strategy that leverages a combination of data analytics, machine learning, and AI to predict individual customer preferences and behaviours. Unlike traditional personalization, which reacts to user actions, predictive personalization anticipates what a user is likely to do next based on historical data, contextual signals, and behavioural patterns, enabling businesses to deliver highly relevant content, product recommendations, or offers in real time.
Predictive personalization often follows a staged process:
- Data collection: Businesses gather data from multiple sources, including past purchases, browsing history, demographics, social media activity, and real-time interactions.
- Data analysis with machine learning: Algorithms identify patterns and correlations, such as which products are often purchased together or which content engages specific user segments.
- Real-time personalization: Insights are applied immediately to tailor experiences, such as dynamically adjusting website content, sending context-aware emails, or recommending products based on predicted intent.
Predictive personalisation is already at work in everyday life. For example, Netflix recommends shows and movies based on viewing history and ratings. Amazon personalizes product suggestions, homepage content, and deals using past purchases and browsing behaviour. The benefits of predictive personalization for businesses are that it produces higher conversion rates by suggesting to users what they are likely to want – even before they know what they want! Predictive personalisation also offers improved engagement with the digital sign, as personalised experiences make customers feel understood and valued. The technology has also been shown to make customers more loyal to a business when interactions feel tailored to them, and it reduces ‘churn’, i.e. customers switching to competitors.
While predictive personalization is pretty much state-of-the art today, the latest developments see the emergence of hyper-personalisation, predictive customer service, and voice-activated personalisation. AI will anticipate problems even before they occur, tailor every aspect of digital experiences, and integrate seamlessly with voice assistants to provide highly individualised interactions. By combining historical data with real-time contextual information, predictive personalisation enables businesses to orchestrate experiences that feel intuitive, timely, and human, transforming customer interactions from transactional to genuinely helpful.
Movers and shakers
Whether you opt for predictive personalization or hyper-personalisation, effective deployment depends on determining the correct strategy for implementation. You will need a range of complementary technologies to provide an effective basis for whichever digital signage model you adopt. For example, you will need to be able to group your target users by action patterns rather than static demographics. You will need to adapt the client’s website or app content in real time based on user behaviour. Based on these or other available metrics you will need to generate trigger emails or notifications based on predicted intent, not just timing.
At this year’s ‘Digital Signage Summit’, Florian Rothberg made the points that today “Digital signage is data driven” and that high growth market will continue to be retail and enterprise communication. By analysing viewer interactions, demographics, and environmental factors, businesses can tailor their signage content to better meet the needs of their audience. This approach not only improves engagement but also maximizes the return on investment for digital signage initiatives. As technology advances, the ability to collect and interpret data becomes increasingly sophisticated, allowing for more precise and impactful signage solutions. By collecting data from various sources such as sensors, cameras, and user interactions, businesses can gain insights into how audiences engage with their signage. This data can include metrics like dwell time, foot fall, and demographic information. Advanced analytics tools can process this data to identify trends and patterns, enabling businesses to tailor their content to specific audience segments. For instance, a retail store might use data analytics to determine peak shopping times and adjust their signage content to promote relevant products appropriately. Additionally, real-time analytics allow for dynamic content adjustments based on current audience behaviour, ensuring that the signage remains relevant and engaging.
Implementing data-driven strategies in digital signage involves integrating data collection and analysis into the content management process. This begins with setting clear objectives for what the signage aims to achieve, such as increasing brand awareness or driving sales. Once objectives are established, businesses can deploy sensors and analytics software to gather data on audience interactions and environmental conditions. This data is then analysed to identify opportunities for content optimization. By continuously monitoring and analysing data, businesses can refine their signage strategies over time, ensuring that they remain aligned with audience preferences and business goals. The implementation of data-driven decision making in signage not only enhances content relevance but also improves the overall customer experience.
AI enables digital signage to tailor content in real time based on audience demographics, behaviour, and context. For example, McDonald’s uses AI-driven menu systems to recommend items based on time of day, restaurant traffic, and item popularity, increasing upsell performance and average check sizes Similarly, computer vision can analyse viewer age, gender, and dwell time to adjust content, as seen in mall photo kiosks that increased sales by 24%. Generative AI can produce original text, images, video, or audio for digital signage, adapting content dynamically to the environment or audience preferences. This includes creating context-sensitive visuals, summarising news, translating messages, or rewriting content in different tones. Interactive signage can further enhance personalization by integrating touch, gesture, or voice inputs to understand user interests. AI can forecast audience behaviour, foot traffic, and engagement patterns to optimise content scheduling and placement. Retailers can combine inventory data, local events, and weather conditions to automatically adjust promotions, ensuring the most relevant content is displayed at the right time. This predictive capability improves campaign performance and operational efficiency. AI reduces the need for manual updates by automating content selection, scheduling, and display management. Machine learning models rank content based on historical engagement, business rules, and real-time context, ensuring the highest-performing messages are shown. This allows businesses to scale digital signage networks while maintaining relevance and responsiveness.
Solution options
So far, so good, but as with any discussion of AI, we have to sound a note of caution. While AI enhances personalisation, it should comply with privacy regulations and ethical standards. Systems should anonymise audience data and provide transparency about future data usage to maintain trust among users. So, who might you turn to for information and solutions? One of the key technologies driving the market for AI in digital signage is Edge AI, developed with integrated Neural Processing Units (NPUs), delivering high TOPS (Tera Operations Per Second) data. These technologies are enabling smarter, faster, and more energy efficient digital signage solutions across retail, transportation, hospitality, healthcare, and beyond, delivering into smart city environments.
Traditionally, digital signage systems have relied heavily on cloud computing for content management and analytics. While effective, cloud-based processing introduces latency, bandwidth dependency, and privacy concerns, this form of AI enables AI processing directly on the device. Instead of sending video streams and user interaction data to remote servers, edge enabled signage can process data locally, and in real time. Supporting faster decision making, improved responsiveness, and reduced network and computing costs.
Integrated NPUs play a critical role in enabling Edge AI capabilities. NPUs are designed specifically for AI processing, such as computer vision, facial recognition, audience analytics, gesture detection, and natural language processing. Their performance is commonly measured in TOPS, or how many trillion operations per second the processor can execute. Higher TOPS ratings enable more advanced AI models to run efficiently on embedded devices without relying on external Graphics Processing Units (GPUs) or cloud resources.
Digital displays, such as the new Philips Signage portfolio, equipped with integrated NPUs ranging from 1 TOPS to 6 TOPS can perform AI functions directly on the display device. For example, AI powered retail signage can analyse customer demographics, dwell time, and emotional engagement to dynamically adjust the content being shown in real time. That allows stores to provide a more tailored experience – whether for the customer who wants to be in store for as little time as possible, or the one who would prefer to browse, interact, and enjoy their retail time.
In transportation hubs, intelligent displays can detect crowd density and automatically update wayfinding information. And it brings the ability to change displays in a fleet at the touch of a button, unlocking services that rely on rapid responses. In restaurants and QSRs, interactive kiosks can provide personalised recommendations, guiding customers through choices not only based on predetermined ‘what goes well together’ but also on demographic profile. It’s all more possible with integrated AI processing.
Integrated NPUs for Edge AI support better energy efficiency. NPUs are optimised for parallel AI operations while consuming significantly less power than traditional CPUs or GPUs. This makes them ideal for always on digital signage applications that require continuous operation with minimal heat generation and lower operational costs. In fact, use of NPUs reduced energy usage by 35% compared to a cloud based solution. Compact embedded systems with integrated NPUs also simplify installation design and reduce the need for additional hardware – and its consequential draw on power.
Privacy and security are extra benefits of Edge AI in digital signage. Sensitive data such as facial recognition or customer interaction analytics can be processed locally, meaning that businesses can minimise the transfer of personal data to cloud servers. This supports compliance with data protection regulations while improving user trust. As AI models continue to become more sophisticated, the demand for high performance NPUs built into displays will increase. AI powered by integrated NPUs, in the digital signage portfolio from Philips Professional Displays is already available in the Philips Signage 5000 Series with 6 TOPS neural processing power.
Generative AI
Philips is by no means the only digital signage vendor to develop its own brand of AI. NEC Corporation has enhanced its generative AI, ‘NEC cotomi’, which it suggests achieved world-class accuracy while maintaining high speed in Japanese language benchmarks. At the same time, NEC has also developed technologies that double the computational efficiency of GPUs while maintaining the performance of generative AI. The use of NEC cotomi, a key technology in the value creation model for digital transformation (DX) ‘NEC BluStellar’ (*1), enables NEC to strongly promote the use of generative AI in actual business. Moreover, NEC technologies can help to alleviate issues such as GPU shortages and power problems associated with the growing demand for AI, while contributing to the creation of an environment in which AI can be used more easily and comfortably.
Due to the rapid development of generative AI, various companies and public institutions are capitalizing on it to promote business innovation. In line with this, there is a demand for improvements in the functions and performance of generative AI itself, such as support for use cases tailored to more specialized tasks and for incorporation into business systems. Studies have shown that global demand generative AI market is expected to grow by about 20 times from 2023 to 2030. In line with this, the demand for GPUs is also rapidly increasing, and the increase in power consumption in data centres is becoming an issue. In response to these challenges, NEC has developed technology to enhance the performance of NEC cotomi and increase the computational efficiency of GPUs, providing conditions for the use of generative AI that are more environmentally friendly.
NEC cotomi has enhanced its performance and achieved accuracy in the Japanese LLM (Large Language Model) benchmark ‘Japanese MT-Bench’ that is comparable to global top-level LLMs such as Claude, GPT-4, and Qwen (*2). Furthermore, it has achieved a speed that is about twice as fast as typical commercial LLM, successfully achieving both overwhelming inference speed and high accuracy. With this performance enhancement, NEC is making the automation of advanced specialized tasks using generative AI a reality.
So why does this matter? Generative AI is transforming digital signage by automating content creation, enabling real‑time personalisation, and significantly improving engagement, often boosting dwell time by around 40% and conversion rates by up to 30%. As of this year, 41% of digital signage deployments use AI-generated content, up from 12% two years ago. AI has shifted the industry from hardware-centric to intelligence-centric, with major vendors (including Samsung and LG) launching AI content platforms. Generative AI creates dynamic backgrounds, languages, seasonal variants, and weather-based versions.
Generative AI enables screens to react to real-world conditions, detect demographics, dwell time, and emotional response. Contextual triggers include weather, traffic, inventory and time of day. Real-time targeting has been shown to increase recall by up to 83% and sales by 20–30% in retail pilots. AI-personalised content increases dwell time by up to 40% and delivers conversion lifts of 25–35% (when compared to static schedules. Engagement rates improve 40–60% with dynamic personalisation. This is because digital signage displays become adaptive channels rather than passive displays. So what’s stopping you? There are issues which interrupt deployment of the latest AI solutions: Privacy concerns demand anonymized analytics; human oversight still needed for brand safety and message accuracy; and there are practical issues where the network operator must ensure that all of the devices installed must support AI workflows and cloud platforms.
Conclusion
There is no doubt that the digital signage community is in the midst of perhaps the most exciting periods of digital signage innovation ever. Generative AI is reshaping digital signage into a real-time, data-driven, highly personalised communication channel. Its impact spans content creation, operational efficiency, audience targeting, and measurable commercial outcomes, marking a fundamental shift from static displays to intelligent, adaptive media networks. Add to this the largely unexploited potential of AR and VR in the digital signage world, and we are likely to see this rate of innovation to continue for many years to come.
