Artificial intelligence and automation are leading a sea change in how business is conducted across industries. We look at some key areas where technology is helping this digital transformation take shape for the better.
Technology continuously changes the way we interact with the world around us. From our work to our homes, AI and technology are affecting our lives in ways we never would have thought imaginable. From ordering groceries with Alexa to procuring office supplies via conversational AI chatbots, the possibilities seem endless. Technology is making business processes faster and eliminating monotonous tasks such as recording minutes in a meeting, responding to helpdesk requests, and even marketing data analyses.
As an employee in this new age of technological advancement, one has to be prepared to work seamlessly with the features of artificial intelligence that now affect everything from hiring to improving its productivity. There are numerous software applications now available that make the use of AI in the workplace more human; however, understanding how technology is improving office operations at all levels will make for a better employee.
Artificial intelligence in HR
Many businesses are now using HR chatbots that are capable of sifting through thousands of applicant resumes for job openings posted for an organization. It’s an intimidating thought to consider that a machine may make or break one’s chance at an interview. How do you prepare your resume for a robot after you’ve sent out a hundred resumes and aren’t sure your cover letter is catchy enough for a human being to like your experience?
A few tips from Inc. are to use standard fonts, avoid acronyms, and offer data over storytelling. Some may think, “well, this is just lazy of the company.” In reality, though, this process frees up the human resources staff to do much more. By filtering out resumes lacking relevant experience, necessary skills, and sufficient job history, human resources staff have the time to find the best talent available and more time to onboard that talent once they have been hired.
Virtual agents & AI in the office
Now that you have the job, you will need to integrate into the company and learn about its rules and processes. Usually, this step takes a lot of time, and needs to be repeated every time a new employee joins. Instead, you can use an employee onboarding chatbotto get access to all the information you need, directly from your workplace.
Once you’ll be ready, you won’t need to convince another AI device to consider your skills. Instead, you’ll utilize AI to enhance those skills and streamline your assigned projects. Have you ever tried to find a piece of information on the computer, but ended up completely lost in a sea of not-quite-right data? This can be devastating to employee productivity. Many companies use multiple automated platforms for different aspects of the business. One for training, another for a benefits repository, another for time entry, and yet another for pay stubs and requesting time off. With all of this information housed in different software systems, things can get messy fast. By using an all-in-one human capital software businesses can consolidate these systems into one platform—eliminating the clutter—to create a fluid user experience.
Employees can also utilize a virtual agent when these processes are consolidated into one platform. Through advanced natural language technologies and proprietary algorithms like Automatic Semantic Understanding, a conversational AI platform can easily understand the nuances of human language via a familiar chat interface. If the business hasn’t completely consolidated its data to one cloud platform, the virtual agent can be used across multiple platforms, such as reading and writing SMS texts, scheduling virtual meetings, and even to order supplies from outside vendors. An employee can also use the virtual agent to find a colleague, check their payslips, request time off, and even initiate promotions.
Transforming the business of healthcare
AI is making its way into business processes in the healthcare industry as well. In the field of radiology, AI has proven to be advantageous in the diagnosis of malignant tumors. The BBC states that an “Algorithm can quickly scan billions of rows of data, identifying patterns in the scans,” making AI much faster at spotting irregularities than a human could. This application and its speed could mean a lifesaving diagnosis for patients that a radiologist may have overlooked based on their limited pool of knowledge or a time crunch to read many images before their shift is over.
Natural language processing (NLP) will also play a role in the future of healthcare and will transform the abilities of healthcare workers. Thomas Davenport of Babson College writes that NLP will be able to “analyze unstructured clinical notes on patients, prepare reports (eg on radiology examinations), transcribe patient interactions and conduct conversational AI.” The ability to document clinical notes and transcribe patient interactions will save healthcare workers countless hours and will allow them to utilize their precious skills to treat more patients, rather than to pour over potential diagnoses.
Another unique case of AI in healthcare comes from Finland. The HUS Helsinki University Hospital recently deployed a healthcare chatbot to assist young people with important questions around mental health, anxiety and depression. Called Milli, the virtual agent provides 24/7 access to assistance that may not otherwise be available to people who need it and was recently awarded the ‘Special Act of the Year 2019’award by the Finnish Association for Special Education.
The potential for AI to help medical professionals save more lives is endless. Healthcare workers could even utilize translation abilities in NLP to break down language barriers between patients and providers, connecting more individuals to the care and specialty services they need—regardless of which languages they can use to communicate.
Machine learning
By automating repetitive tasks such as filtering submissions on a user generated content site or sifting through data to create personalized marketing strategies, companies can reallocate high-quality human labor to a more nuanced or time-consuming project that cannot be automated. In a recent interview, Adam Geitgey, software engineer and consultant, explains the benefits of using machine learning when “the process is a little too complicated to automate with traditional programming.” He uses the example of an online business that lets users upload content. Machine learning takes over the repetitive task of filtering out content that isn’t appropriate for the site’s audience—something that would be far too time-consuming for a singular human operator to undertake.
Personalized marketing campaigns are another avenue in which machine learning is deployed in the workplace. By utilizing machine learning to filter through customer data to identify trends, marketers save an immense amount of time. This also allows marketing professionals to generate personalized marketing and promotional materials for tech-savvy users—feeding it back into a chatbot marketing agent and therefore elevating the authenticity and potential for success when moving customers through the sales funnel. This not only makes the work of marketers easier, but lets them spend more time on valuable creative tasks.
Advancements in artificial intelligence have changed many aspects of the way we work. The ever-evolving presence of technology in the workplace will surely continue to modify our working environment and make work more human. Although a common misconception about AI is that it exists to take human jobs, and in some cases it will, it seems that AI will instead take jobs that are entirely nonhuman, enabling us to do the creative and engaging work that lends us greater value and fulfillment.
Large Language Models
Large language models (or LLMs) are AI systems that use deep learning techniques to process large amounts of text data, such as books, articles, and other documents. These models learn patterns and relationships within the data and can generate human-like language responses based on the input they receive.
The development of large language models has had a significant impact on the way we work, particularly in areas such as natural language processing, text analysis, and machine learning, allowing for more efficient and accurate processing of large volumes of text data, enabling tasks such as language translation, sentiment analysis, and content creation to be automated.
Moreover, the use of large language models has also opened up new opportunities for businesses to improve customer service through chatbots, virtual assistants, and other conversational interfaces. These models can understand natural language queries and provide relevant responses, enhancing the user experience and reducing the workload on customer service teams.