By Heather Douglas
“Recent advances in robotics, perception, and machine learning, supported by accelerating improvements in computer technology, have enabled a new generation of systems that rival or exceed human capabilities. These systems are far more autonomous than most people realize. They can learn from their own experience and take actions never contemplated by their designers. The widely-accepted wisdom that ‘computers can only do what people program them to do’ no longer applies.” Dr. Jerry Kaplan, Artificial Intelligence – What Everyone Needs to Know (published 2016).
Companies, large and small, are wrestling with the question of when to start using artificial intelligence (AI) and machine learning to streamline operations, boost productivity without having to hire more workers, and, in the long run, substantially save costs.
Kaplan, (a widely-known serial entrepreneur, AI expert, co-founder of four Silicon Valley start-ups, and a Fellow at the Stanford Center for Legal Informatics) predicts regardless of how society views these machines – whether as clever appliances or a new form of life – they are likely to play an increasingly critical role. “Advances in the intellectual and physical capabilities of machines will change the way industry works, upend labour markets, reshuffle social order, and strain our companies and public institutions,” he states.
To help propane and fuel oil companies debate when to budget for what could become a costly investment strategy, here are a few questions the Chief Information Officer (CIO) and executive team need to explore.
The first step is to assess your organization’s data. While a comprehensive audit sounds daunting, it is the only way to value the data you already have amassed including: your governance policies and procedures, data storage, research and development intellectual properties, customer profiles, and distribution networks. The second piece is to then evaluate the authenticity of the sources of data collected, how it is used, the volume of data gathered, and the veracity of the data your company collects as well as the information gathered from vendours, consultants, and suppliers.
Companies that have embarked on this journey have discovered it is necessary to commit “an appropriate amount of resources to best-in-class data governance.” They also discovered the productivity increase created value for their organizations.
According to Shelly Palmer, CEO of the Palmer Group (named by LinkedIn as one of the top 10 voices in technology), big-name tech companies like Amazon, Google, and Microsoft have suites of machine learning tools to help with a company’s data analysis. “If a business owner wants to have some fun, go to Google’s AI Experiments page,” he says. “Or, if you want to go a bit deeper into practical experimentation, check out Amazon AI. The more you experiment with technology, the better you will understand machine learning’s potential to significantly increase productivity.”
Business is moving so fast every company continues to look for ways to become more profitable and efficient. In the next 12 to 18 months, technology companies will probably introduce dozens (some predict hundreds) of software programs to keep distributors in touch with their customers. Propane sellers could evaluate commoditized machine learning to build closer relationships with buyers. The average business-to-business selling cycle may take months or weeks, and often generates lengthy and complex email trails. Machine learning can quickly recap the highlights of the conversation and appropriate next steps. This could help a new sales hire get up-to-speed on all client accounts.
Palmer continues. “Exponential improvements in machine learning are pointing a future where systems are trained, not coded. Today, software engineers write programs (code) to instruct computers to perform tasks.” These days are clearly numbered, Palmer adds. “Sooner or later, computer programming will evolve into something more akin to animal training.”
Automation Impacts Skills
Nothing about AI and machine learning changes the fundamentals about labour markets. From an economic standpoint, both are advances in automation. But the actual process by which machines replace human workers is much subtler. In practice, automation replaces skills, not jobs, and correspondingly, what employers need are not workers, but the results obtained by applying these skills. To be successful, makers of robots are not in the market to replace people. Rather, their goal is to provide machines with the requisite skills to perform useful tasks for corporations and individuals.
“The hardware and software that makes some workers more productive, also puts other workers out of their jobs,” says Kaplan, a Fellow at Stanford. “AI technology, for instance, is just another automation. But its potential to rapidly encroach on current workers’ skills is unparalleled in the recent history of technological innovation, with the possible exception of the invention of the computer itself.”
Historically, the jobs must susceptible to automation have been routine. Machine learning means that tasks requiring insight and experience are now possible to automate with the next generation of big data algorithms.
“If energy companies are to successfully deploy the Internet of Things (IoT) technology to drive innovation, efficiency, and increased productivity, they must upskill employees and/or embark on recruitment drives,” says an independent research commissioned by Inmarsat, a British satellite telecommunications company, which found that while the vast majority of energy companies have their sights set on IoT, a significant proportion lack the skills needed to take advantage of the technology.
Market research specialist Vanson Bourne interviewed respondents from 100 large energy companies across the globe and found that while 88 per cent expect to deploy IoT technologies within two years, many currently lack the skills needed to do so effectively. Over a third (35 per cent) of respondents said they lack the management skills to make the most of IoT, while 43 per cent lack the skills to do so at a delivery level – to take full advantage of IoT. Digging deeper into the specific skillsets that are lacking, the research found that 54 per cent have a shortage in cybersecurity and 49 per cent lack technical support, while analytical and data science skills are also in high demand.
Chuck Mosely, senior director for energy of Inmarsat, says, “Whether they work for fossil fuels or renewables, IoT offers energy companies the potential to streamline their processes and reduce costs in previously unimagined ways. Smart sensors, for examples, can facilitate the collection of information at every stage of production, enabling them to acquire a higher level of intelligence on how their operations are functioning and to therefore work smarter, more productively, and more competitively. But fully realizing these benefits depends on companies’ access to appropriately-skilled staff.”
Dr. Kaplan is right. The widely-accepted wisdom that ‘computers can only do what people program them to do’ no longer applies.
#Technology #AI #Propane #Fuel #Oil #Productivity #IoT
