The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%. Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector.
Combining blockchain’s distributed ledger framework with the Internet of Things’ (IoT) proven real-time monitoring and tracking capability is redefining supply chains. Blockchain shows potential for increasing the speed, scale, and visibility of supply chains, eliminating counterfeit-goods transactions while also improving batching, routing and inventory control. Blockchain’s shared, distributed ledger architecture is becoming a growth catalyst for IoT’s adoption and commercial use in organizations. Blockchain and IoT are defining the future of supply chains based on the initial success of Proof of Concept (POC) pilots focused on the logistics, storage and track-and-trace areas of supply chains across manufacturing.
Artificial Intelligence enables marketers to understand sales cycles better, correlating their strategies and spending to sales results. AI-driven insights are also helping to break down data silos so marketing and sales can collaborate more on deals.Marketing is more analytics and quant-driven than ever before with the best CMOs knowing which metrics and KPIs to track and why they fluctuate. The bottom line is that machine learning and AI are the technologies CMOs and their teams need to excel today. The following ten charts provide insights into how AI is transforming marketing.
The leading growth strategy for manufacturers in 2019 is improving shop floor productivity by investing in machine learning platforms that deliver the insights needed to improve product quality and production yields. Using machine learning to streamline every phase of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is now a priority in manufacturing. Machine learning reduces unplanned machinery downtime. The following are ten ways machines learning is revolutionizing manufacturing in 2019.
2019 is a record year for enterprises’ interest in data science, AI, and machine learning features they perceive as the most needed to achieve their business strategies and goals. Enterprises most expect AI and machine learning applications and platforms to support a range of regression models, followed by hierarchical clustering and textbook statistical functions for descriptive statistics. Recommendation engines are growing in popularity as interest grew to at least a tie as the second most important feature to respondents in 2019.
Economic and trade uncertainty is the new certainty. Every manufacturer needs to start taking a more data-driven approach to defining the initiatives and strategies that will keep their businesses growing. The best countermeasures capitalize on and scale the data manufacturers have been accumulating in some cases for decades. The following strategies enable manufacturers to capitalize on the data they’ve been aggregating and analyzing on suppliers, pricing, production and operations, quality, and service. In short, these strategies have an insight track on succeeding during challenging, uncertain economic times by delivering quicker results and immediate payout.
Internet of Things, or IoT, is defined as the network of physical objects, or “things” embedded with electronics, software, sensors, and connectivity to enable objects to collect and exchange data. Many Organizations today show interest in and demand for applying business intelligence (BI) to IoT data, systems, and processes. R&D and Marketing & Sales departments assign the highest levels of IoT importance, as do larger manufacturing, financial services/insurance, and technology organizations. One of the most valuable insights is how critical the role IoT champions or IoT Advocates are to the successful adoption of IoT technologies today.
AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools. Fintech’s traditional tech stacks weren’t designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What’s needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. Here are ten predictions of how AI will improve FinTech in 2020.