IoT & AI Powering the Next Wave of Smart Connected Products
Executive Summary
The Internet of Things (IoT) is fueling a proliferation of connected smart devices and sensors across consumer and industrial applications. As the volume of real-time data generated from IoT endpoints grows exponentially, Artificial Intelligence (AI) is emerging as the critical technology for extracting value from this data deluge. This whitepaper analyzes the intersection of IoT and AI, explaining how these symbiotic technologies can drive the next generation of intelligent connected products across industries.
Key Highlights:
IoT generates massive datasets required to train and validate AI models
AI delivers the predictive power to optimize IoT device performance
Together, IoT and AI enable smarter products and processes
Early adopters focus on predictive maintenance, supply chain optimization
Success factors include unified data infrastructure, trustworthy AI practices
Table of Contents
Introduction
IoT Drives an Explosion of Real-Time Data
AI Extracts Value from IoT Data
Powerful Together: IoT + AI
Use Cases and Industry Applications
Critical Success Factors
Looking Ahead
Introduction
The Internet of Things and Artificial Intelligence are two of the most transformative technologies of the 21st century. IoT enables an ecosystem of connected sensors and devices generating continuous streams of telemetry data about equipment performance and usage. AI provides the predictive capabilities to analyze data patterns and optimize automation. While powerful independently, it is together that IoT and AI truly create game-changing potential across industries through smarter connected products and processes.
This whitepaper analyzes the synergies between IoT and AI that are driving intelligence into the next generation of products. It covers key capabilities, use cases, best practices and a view into the future where nearly every product leverages embedded intelligence driven by AI algorithms.
IoT Drives an Explosion of Real-Time Data
Internet of Things (IoT) is the concept of sensors and devices connected to networks to generate data and be managed/controlled across applications. By 2025, analysts forecast over 75 billion IoT devices will be deployed1. Consumer examples include smart home devices, wearables, appliances, toys and more. On the enterprise side, manufacturing, energy, transportation and healthcare leveraging IoT sensors.
A major benefit of IoT devices is sensing information about performance, usage patterns and environment conditions to optimize operations. This generates tremendous volumes of real-time telemetry data from product/asset sensors and must be efficiently collected and processed by AI algorithms.
IoT data has four primary characteristics:
Real-Time: Continuous data streams from sensors
High Velocity: Significant throughput volumes
Variety: Structured + unstructured mix
Value Density: Small signal to noise ratio
Organizations must implement data platforms supporting ingestion, processing and analysis of massive streams of real-time IoT data efficiently and cost-effectively.
AI Extracts Value from IoT Data
Artificial Intelligence refers to data-driven algorithms capable of learning patterns, predicting outcomes and optimizing decisions automatically. IoT datasets serve as important fuel for developing and validating AI models. Combined together, AI and IoT unlock new sources of operational value across applications:
Predictive Maintenance: Continuously monitor asset health and predict maintenance needs before failure
Product Quality: Detect early performance anomalies suggesting flaws requiring tuning
Inventory Optimization: Align supply based on real-time demand signals and product usage
Yield Improvement: Learn parameters maximizing output and tune processes automatically
Autonomous Systems: Apply exact interventions dynamically based on operating conditions
AI and IoT together deliver optimized, adaptive and autonomous systems. But AI models must be rigorously developed, validated, monitored and managed responsibly according to ML best practices in order to be trustworthy and explainable.
Powerful Together: IoT + AI
While IoT and AI Technologies deliver significant value independently, together they enable a new generation of smart connected products and processes across every industry:
Next-Gen Products: Consumer and industrial products with embedded connectivity, telemetry and intelligence
Predictive Infrastructure: Public works, cities and utilities leveraging AI on sensor data to predict, optimize and automate management
Intelligent Factories: Connected machines and supply chains self-optimizing based on production analytics
Proactive Healthcare: Patient monitoring predicting risk alerts and driving personalized care automation
The convergence of AI and IoT gives rise to a more observable, optimized and autonomous world through continuous data feedback fueling real-time learning and adaptation.
Use Cases and Industry Applications
IoT + AI are driving transformation across nearly every industry:
Manufacturing: Predictive maintenance, quality optimization, micro-segmentation
Transportation: Fleet/cargo tracking & optimization, autonomous vehicles
Oil/Gas: Predictive analytics on exploration sensors,optimized drilling
Utilities: Smart grid optimization, predictive grid maintenance
Insurance: Risk monitoring, usage-based policies, fraud detection
Healthcare: Patient health/treatment monitoring, personalized medicine
Retail: Shopper insights, predictive inventory, self-checkout, cashier-less stores
The applications highlight improvements in operational efficiency, cost reductions and new data-driven business models. But success requires focus on people, process and responsible AI practices in addition to technology capabilities.
Critical Success Factors
Realizing value from AI + IoT initiatives depends on several key factors:
Unified Data: Integrating siloed sources (IoT, operational, customer, product) for AI algorithms
Cloud Platforms: On-demand scale for ingestion, storage and analyticsIDI
Talent & Skills: Data engineers, data scientists, ML Ops professionals
Responsible AI: Address bias, explainability, transparency and ethics
Legacy Modernization: Digitizing manual processes before adding intelligence
Culture: Encourage experimentation plus operational discipline
Governance: Ensure quality, security and compliance producing trust
Process Integration: Connect insights with downstream decisions and actions
Without enterprise readiness across these dimensions, the benefits of IoT + AI fail to materialize. A holistic approach is required for intelligence initiatives to fulfill their promise.
Looking Ahead
As AI capabilities grow more powerful and IoT data becomes pervasive across industries, these technologies will feed into each other to drive an explosion of intelligent connected products. Nearly every offline experience from shopping to dining to entertainment will be enhanced by personalized real-time AI algorithms optimized based on situational telemetry analysis. The next generation of products will learn, predict and self-optimize as part of autonomous systems redefining business productivity and processes. Enterprises investing early in IoT and AI will gain competitive advantage as first movers while laggards face business model disruption or extinction from innovative entrants. The future will belong to intelligent connected experiences – with AI + IoT providing the foundation nearly everywhere we live, work and play.