“Improving business through AI“
– this is the motto behind every business AI project. Business AI is mainly aimed at improving efficiency and thus making a qualitative and/or quantitative jump ahead.
The overlap of AI with business engulfs many subtopics. Below is a list of most frequent mentions of our members’ key focus areas, but other topics are also welcomed.
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Auto-decisions
New technologies and machines are crucially important for companies today – especially when they deal with large volumes of data and make repeated decisions. Today’s automated decision-making algorithms can outperform human decision-makers on difficult problems.
In today’s fast-moving world, traditional approaches to corporate management may no longer provide the insight required to make sound decisions and effectively manage risk exposure. Instead, decision makers operate in an increasingly complex, high-dimensional environment that requires additional sources of data to inform their decisions. This complexity makes self-defining data an essential tool for businesses seeking to make better decisions, improve operational performance and support risk management and compliance objectives. From back office functions to the front office and beyond, businesses that embrace automated decision-making based on artificial intelligence are better positioned to compete effectively in the global marketplace.
IN 2018, the European Commission released a detailed report investigating the use of automated decision-making in the labour market. This study is based on data collected through questionnaires sent to more than 150 companies and a series of expert interviews. It analyses how AI is used to decide whether someone should be selected for a job or not and whether they should be hired or fired. The findings shed light on existing practices and the potential impact AI and machine learning may have on current and future employment conditions in the EU. Source: https://ec.europa.eu/info/sites/default/files/independent_study_on_automated_decision-making.pdf
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Virtual agents
Virtual agents and collaborating assistants are substituting human workers in great numbers every day. Virtual agents is a dialogue technology that enables companies to engage customers through natural language messaging that is customized for their brand voice. The technology, known as “virtual agents” or chat bots, enables businesses to interact with customers in highly personalized ways, at scale across numerous channels in a fully automated fashion. This enables customers to interface with a company in a way that is consistent with their prior interactions with them – whether offline or online – across myriad devices. Businesses can leverage this technology to build and maintain their own branded conversational interface with a concierge-like service for consumers – providing existing websites, mobile apps and other digital interactions to leverage the power of automated natural language responses delivered on desktop and mobile. This represents a significant opportunity to quickly respond to consumer needs and create differentiation in the marketplace.
Uber for Business has integrated AI-powered chatbots to its service to help companies manage all their needs around transportation for their employees and customers. Using conversational natural language, bots powered by Watson respond to requests starting with questions such as “Where can I catch a ride?” or “How can I book a ride?” From there, they seamlessly guide users through the booking process to ensure that every trip goes as smoothly as possible.
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Predictive analytics
Predictive analytics is the most widely used information technology AI application by businesses today, with an estimated 60% to 80% of enterprises using it. Current AI tools are well positioned to meet these demands by enabling companies to build and deploy their own predictive analytic applications powered by the powerful predictive analytics algorithms. Tools offered by many software companies and startups enable business users to build graphical predictive analytic models using the familiar tools they are accustomed to using in Excel and other BI and business planning environments.
Predictive business analytics has applications across business scoreboard. Few examples are presented below.
Recruitment
As data plays an increasingly critical role in the recruitment process, current approaches to workforce planning are increasingly ineffective in addressing the talent gap. Such tools can significantly accelerate company’s ability to extract actionable intelligence from their candidate pool and automate complex processes related to finding and screening talent.
Vendor management
Managing multiple vendors and integrating data and analytics into existing enterprise systems create challenges that can slow down adoption and lead to unacceptable rates of failure or return for organizations. Organizations need to integrate analytical capabilities into their business processes rapidly and cost-effectively.
Financial data
Predictive analytics combined with artificial intelligence technology is powering the next generation of information extraction from unstructured data – improving decision-making for banks and financial institutions by helping them understand their customers’ behavior and preferences, anticipate their needs and proactively address their challenges. Today’s customers are evolving quickly in terms of how they interact with financial institutions, due in part to the rapid evolution of technology. To keep up with this evolution, organizations must identify key areas for improvement through expanded analytic capabilities.
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Industry 4.0, smart manufacturing
Industry 4.0, smart manufacturing enable many innovative technologies, such as 5G, to build better optimized, more effective businesses. Today, we are witnessing the transition from an industrial economy to an AI one. This is giving rise to intelligent machines which are capable of learning from their environment to perform complex industrial tasks autonomously. Recent technological advances have heralded a new era in manufacturing, where machines are increasingly able to operate autonomously and collaborate with each other to perform complex tasks. This new era is often referred to as Industry 4.0. These intelligent machines, working for the same goal, are also called ‘smart factories.’ Smart Manufacturing focuses on moving manufacturing processes beyond manual operations to integrated systems where machines communicate with each other via networks. This results in faster production, reduced waste and greater quality. Industry 4.0 is underpinned by the emergence of digital technologies such as the Internet of Things, cloud computing platforms, big data analytics, 3D printing, robotics and smart sensors. Together, these technologies have the potential to completely transform manufacturing and drive productivity growth in the long term.
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Competitive advantage from using AI
Artificial intelligence and automation are quickly overtaking the business operations, and there’s no stopping it. And while most business managers have at least a vague idea of what AI actually is, most businesses have no idea just how pervasive the technology is, even in its most basic forms. These businesses are missing out on enormous benefits that AI offers, and, as a result, their business profits and sales figures are suffering.
We are interested in finding out – what is the actual competitive advantage for businesses from using AI. considering both quantitative and qualitative gains.
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Small and medium-sized enterprises and AI
Small and medium-sized enterprises (SMEs) Artificial intelligence and automation are quickly overtaking the business operations, and there’s no stopping it. And while most business managers have at least a vague idea of what AI actually is, most businesses have no idea just how pervasive the technology is, even in its most basic forms. These businesses are missing out on enormous benefits that AI offers, and, as a result, their business profits and sales figures are suffering.
We are interested in finding out – what is the actual competitive advantage for businesses from using AI. considering both quantitative and qualitative gains.
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The future of work
The apsects of artificial intelligence are difficult to navigate. On one hand, AI is revolutionizing the workplace by automating tasks that used to require human intelligence. On the other hand, the “smart” robots that are replacing humans in factories and assembly lines sound more “evil” than “benevolent.” So, can artificial intelligence really replace human labor, or will the technology become something that enslaves us instead?
We are interested in finding out – how to effectively apply AI to enable different aspects of the future of work.
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Empowering companies with Big data
The business environment has changed – we are in a new data paradigm. The data is becoming more important than artificial intelligence algorithms. The effectiveness of AI depends on the quality of the data. The main value of businesses is going to be in their data assets. The way data is organized and processed for business needs is determining who will prevail in the era of AI. Businesses need to automate streaming of structured and unstructured data. Each organization needs a data command center where they can have a continuous input stream of data, and can analyze large amounts of data as they go.
We are interested in finding out – if companies are really using their data assets and what they need to do to make effective use of big data.
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AI and business ethics
New technologies and machines are crucially important for companies today – especially when they deal with large volumes of data and make repeated decisions. However, these automation capabilities can also lead to discriminatory practices and unfair treatment in the workplace. With Artificial Intelligence (AI) being one of the top five technologies, machines capable of ‘learning’ and making complex decisions have great potential to affect the way businesses operate. This digital transformation brings along many secondary changes and raises important questions about fairness, legality and ethical implications.
We are interested in finding out – how do companies tackle ethical issues with respect to AI in the factory floor, AI in their products and services; what are the effective strategies to manage AI business ethics.
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Legal aspects of AI in business
When implementing artificial intelligence (AI) into business, there are critical legal aspects thaat need attention. First, businesses need to ensure that AI is not being used to discriminate against employees or customers. Additionally, any data collected by AI must be properly secured to protect the privacy of individuals. Eventually, businesses should consider the potential liability risks associated with AI and put in place appropriate safeguards.
We are interested in finding out – to what extent have companies taken these legal considerations into account. What legal issues have arisen and how companies have managed these? How much of an obstacle are legal considerations when going forward with AI implementation?
The list of topics is not limited to the afore mentioned.