IoT devices and systems have always represented an ideal source of real-world data to feed enterprise AI systems, driving increased efficiencies and optimizing operations. The fast-paced world of technology often sees the buzz surrounding hot topics wane, and IoT, celebrated fervently a decade ago, is not immune. When a colleague asked me, “Is IoT dead?” I responded with a succinct, “No, IoT is not dead, but the initial novelty has worn off.” This concise reply touches on a vital truth: IoT is evolving, not vanishing. In this maturation process, IoT is transitioning from a standalone novelty to a fundamental enabler of high-ROI, AI-driven applications across industrial landscapes.
To provide a thorough answer to my colleague’s question, this article delves into the intricate synergy of three evolving tech trends—platform-based devices, AI’s insatiable demand for real-world data, and solution-focused product development. Together, these trends elevate IoT from a temporary fascination to a profitable cornerstone of future technology.
The Rise of Killer Apps in Industrial Verticals
The “killer app” concept refers to applications so transformative that they make the underlying technologies indispensable. Historically, applications like Microsoft Office, Uber, and streaming services have reshaped their respective domains. IoT is uniquely positioned to foster similar disruptive applications across various industries by leveraging interconnected devices to deliver revolutionary benefits and upending existing practices.
While IoT has permeated multiple sectors, the holy grail of a universally acknowledged killer app remains elusive. Smart home products, for instance, offer convenience and incremental improvements through smart thermostats, door locks, and cameras. Yet, these applications fall short of being deemed indispensable. The true killer apps will autonomously optimize entire systems, such as home energy consumption and safety, demonstrating IoT’s full potential.
AI and IoT: A Symbiotic Relationship
AI is the guiding light for modern IT investments, offering enterprises unparalleled opportunities to enhance operational efficiency, boost product quality, and derive actionable business insights. This potential largely hinges on the vast amounts of data collected and analyzed from company operations and product performance. Enter IoT, the quintessential provider of real-world, real-time operational data vital for AI’s transformative capabilities. The real reason IoT is evolving rather than fading into obscurity lies in this symbiotic relationship.
For enterprises dependent on physical infrastructure, AI-optimized IoT systems represent the first wave of killer applications. AI-enabled applications require copious amounts of operational data from IoT devices, driving demand. However, this demand has not yet catalyzed widespread adoption, partly due to three significant deployment barriers.
Overcoming IoT Deployment Challenges
Despite longstanding IoT deployments, three primary impediments have slowed the emergence of killer apps:
- Extracting Business Value: Collecting and analyzing large streams of diverse device data is complex and costly.
- Scalability: IoT device development struggles to scale adequately.
- Customer Value Focus: Most IoT companies focus more on enabling technologies than on addressing transformational business opportunities directly.
Fortunately, a trio of evolving trends is now flattening these barriers:
- AI Simplifies Data Processing: By simplifying data ingestion and contextualization, AI can transform raw IoT data streams into actionable business processes.
- Platform-Based Development: IoT device development is shifting from custom solutions to platform-based approaches, simplifying and accelerating the process.
- Product-Centric Focus: Progressive companies are shifting from mere device connectivity to delivering comprehensive solutions that enhance business operations.
These converging trends are fostering the development of transformative IoT applications. Let’s explore these trends in detail.
Trend 1: AI Changes the Game
AI is driving IoT transformation in three primary ways. Firstly, as enterprises rush to optimize and automate business processes, the requirement for real-time data from IoT devices explodes. This insatiable demand heralds the arrival of automation and optimization applications that will define the next decade. Secondly, multimodal AI models, capable of handling diverse data types and sources, mitigate the need for custom middleware. These models excel in processing unstructured IoT data, reducing the complexity and cost of data reformatting and analysis. Thirdly, even small, battery-powered IoT devices can leverage AI inference locally to convert raw data into actionable insights, cutting cloud computing costs and improving efficiency. By pushing AI capabilities to the edge, IoT devices become smarter and more autonomous.
In summary, the rapid adoption of AI within enterprises increases the demand for IoT data. Simultaneously, multimodal AI simplifies data processing, and on-device AI extends the reach of business transformation to real-world operations.
Trend 2: Platform-Based, Software-Defined IoT Devices
AI-driven demand for process automation illuminates lucrative opportunities for IoT products. However, IoT device development remains a bottleneck due to the need for product-specific customization of embedded hardware and software. This bespoke development approach is slow, costly, and fraught with risk. Modern IoT devices, however, require sophisticated features such as multiple network stacks, enterprise-grade security, and high-level programming languages, all challenging to integrate into custom products sustainably.
A promising alternative lies in using platform-based IoT development. Leading semiconductor companies now offer powerful, application-independent platforms that enable faster development, better security, and improved product quality. By employing fully integrated platforms, developers can bypass system engineering complexities and start building applications immediately. This approach improves time-to-market, reduces risk, and lowers development costs significantly.
Trend 3: Product-Centric IoT
The confluence of AI and platform-based IoT devices paves the way for IoT products that offer substantial enterprise business value. A shift from technology-centric to product-centric IoT is crucial, where companies focus on solving specific customer problems rather than merely providing IoT components.
One excellent example of product-centric IoT is Samsara, a leader in the physical operations industry. Samsara’s offerings, including rugged, GPS-enabled gateways and the Connected Operations Cloud, exemplify how integrated solutions can provide high-value operational insights without necessitating deep technical discussions with customers. Instead, Samsara focuses on enhancing fleet management, workflow automation, and safety through AI-enabled applications, making their solutions indispensable.
Accelerating IoT Growth
The interaction between AI technologies, IoT platforms, and business-focused IoT products creates a virtuous cycle, propelling the development and adoption of the next wave of killer IoT applications. By providing substantial ROI, enterprise operations automation boosts demand for IoT products, which in turn stimulates further investment in IoT platforms and AI technologies. This continuous growth cycle is expected to power industries with physical assets through indispensable, transformative applications.
In future articles, I will explore how companies across various verticals are driving digital transformation. From Samsara to industry giants like Honeywell, Siemens, and John Deere, and innovative platform providers, these explorations will reveal how IoT and AI converge to turbocharge growth and enable a new era of technological advancement.