With the status of current wind and solar subsidies in the U.S. unclear, the industry needs to brace for impact by making up for the increase in investment risk post-subsidies. A post-subsidy world means the renewable energy sector needs to successfully harness and utilize AI and smart data analytics to maximize investment returns. According to a report by the International Energy Agency, carbon emissions hit a record high in 2018. A new record high is likely in 2019, too. Global energy demand is rising exponentially due to forces like globalization, industrialization and exploding populations. We can't yet offset the full impact of these massive forces with renewable energy, but with the right advancements and integrations of AI and increased investment in renewable energy, we can scale to meet the challenge.

To gauge the complexity of the juggling act utility firms must perform to stay in business, consider two statistics. In the four years to 2018, the number of Britons who switched their energy supplier almost doubled to 5.9 million; at the same time, the contribution of renewables to energy firms' output mix grew from about 13% to just shy of 20%. One represents a fundamental change in consumer expectations of the service they receive while the other highlights the political and environmental pressures being brought to bear on suppliers' operations. To the list of challenges that are adding to the pressures under which utilities operate, add the tightening – and disparate – the grip of regulators, the fracturing of transmission networks and the increasing influence of activist investors. Managing these changing times can be incredibly challenging for established utilities, especially at a time when technology is enabling venture-backed start-ups to move into niche segments of their operations.

Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behaviour by recognising complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The leading software constructs are autonomous and self-learning.

Since the first use of advanced software in asset-intensive industries more than four decades ago, manufacturers have been on a journey to transform their businesses and create added value for stakeholders. Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behavior by recognizing complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences.

In the last few years, many asset-intensive organizations, particularly in the mining, power and utilities, oil and gas, and chemicals industries, have turned to industrial Internet of Things (IIoT) and cognitive technologies to help improve a critical area of their business: equipment reliability.1 Asset performance management (APM) programs, which connect data and trigger actions via systems across the business, can play a major part in driving these improvements. According to a 2018 Deloitte survey, oil and gas leaders rated the big data derived from programs such as APM as the most likely to provide the greatest business value.2 However, when asked about how digital technology can be used most effectively within their companies, those same executives ranked APM below both cost reduction in maintenance and operations as well as improvements in safety.3 This seems to reveal a pervasive and narrow view of APM that may miss the connection between asset performance, broader maintenance and operations improvements, and safety. Merely implementing APM software and digitizing existing processes is not likely to improve core operations and obtain the financial results that executive leaders desire (and investors demand).

Newly launched Alchemy IoT outlines its plans for a new approach to managing industrial assets using artificial intelligence. Alchemy IoT, a provider of IoT asset intelligence for industrial IoT, launched last week with $4 million in funding. According to the company, its product is driven by a'no-code' approach to what it calls "IoT asset intelligence". This, apparently, focuses on simplifying how management, performance and maintenance of industrial assets can be improved using artificial intelligence (AI) and machine learning. The company plans to target small to medium-sized industrial customers with limited technical and data science resources – the sorts of organizations that find it tough to bring complex IoT applications online.

Vlad Lata is Chief Technology Officer and Co-Founder of KONUX the Industrial IOT company specialised in creating customised sensor and AI-based analytics solutions for the industrial world. Founded in 2014 in Munich, KONUX combines Silicon Valley digital thinking with German engineering, and focuses on improving asset availability, network capacity and reducing maintenance costs for their clients; namely rail operators and industrial companies. Heading up the product development for KONUX with a focus on AI and Big Data, Vlad Lata and his engineering department have made huge strides in predictive analytics, smart sensors and offering a complete end-to-end solution for their clients. We're thrilled to hear more during Lata's talk at AI With the Best online conference April 29–30th, and are pleased to have had a chance to interview the KONUX CTO ahead of this event. Q Konux is famed for the highly accurate smart motion sensors paired with AI data analytics -- what skills sets you apart from other engineering firms?