The Impact of Artificial Intelligence On Knowledge Workers

Uploaded on 2023-07-25 in Education & Research

The Impact of Artificial Intelligence On Knowledge Workers

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Until recently, the impact of Artificial Intelligence (AI) on jobs has been abstract for most workers, something that seemed to be out on the distant horizon. But the genie is out of the bottle, and there’s no putting it back as the current acceleration of applications around so-called Generative AI is showing us how quickly and broadly our work will change.

AI is making waves in product and service innovation, creating new product designs, generating marketing content, and even developing new business models.

“Now, a version of this idea is making a comeback in some companies, and we expect to see it in more. It will require not only an appreciation and understanding of AI, but also a renewed appreciation of business processes as a structure for improving work,” says Harvard Business Review. “As AI emerges as a universally applicable, general-purpose technology, it appears increasingly possible that it can enable the kind of radical redesign of business processes.”

Imagine a fashion company using a generative AI model to create new clothing designs based on current fashion trends, or a marketing company using a text generation model to create advertising copy. The possibilities are now endless.

But it's not just about innovation. Generative AI is also improving business operations, automating routine tasks, improving decision-making, and optimising processes.

A logistics company could use a generative AI model to optimise its delivery routes, while a manufacturing company could use a generative AI model to improve its production processes.

Types Of AI

AI is quickly emerging as one of the primary disruptors of the modern workforce. Increasing advancements in artificial intelligence (AI) technology lead to many knowledge workers being displaced.

While automation has been a part of the economy for decades, the emergence of AI has drastically changed the landscape and implications for knowledge workers. Understanding the types of AI and the implications for employment is key to understanding the potential of this technology.

In its most basic form, AI is the development of computer systems that can simulate and respond to human behavior. AI writers are one example of this technology, with these AI systems being able to generate content based on human input. AI writers have been used by various organisations, such as publishers, newspapers, and news organisations, to produce written content faster than ever before.

While these systems are limited in their ability to produce high-quality content, they are a valuable tool for content producers who need to generate content quickly.

Another AI technology helping to automate knowledge work is natural language processing. Siri, the automated assistant on iPhones that appears to understand and respond to spoken requests, is perhaps the most widely known example of this technology.

IBM’s Watson, which combines technologies for natural language processing, hypothesis generation, and evidence-based learning, all AI technologies, may have a greater impact on automating knowledge-intensive tasks rang- ing from medical diagnosis to responding to call center inquiries.

In pilots with call centers, IBM found that it could reduce the amount of time operators spend looking for information to answer inquiries by half.

The US military has long conducted research and development on AI and continues to find new applications for it.The Navy, for instance, is spending over $10 million to acquire and deploy AI technologies to “enhance, automate, and improve business processes, resource use, decision making, and interoperability” of some of its key systems, weapons, and activities.

AI also has implications for the medical field, which has been used to aid diagnostics, research, and analytics. AI systems can detect and diagnose diseases and conditions, develop personalised treatment plans, and monitor patients’ progress.

These systems are not limited to just medical applications, with AI being used to monitor security systems and supply chains and for customer service and support.

AI is being used for customer service and marketing, with virtual agents, chatbots, and other AI systems used to interact with customers and handle customer inquiries. These systems can understand customer requests and provide precise answers, making them a powerful tool for customer service and marketing teams.

AI can also analyse data and optimise processes in various industries, from manufacturing to finance. AI’s ability to process large amounts of data quickly and accurately makes it an ideal tool for businesses to understand better and their customers, streamline their processes, and make better decisions faster.

Finally, AI is employed in several creative fields, such as photography, music, and film. AI can generate creative works often indistinguishable from those created by humans, making it a powerful tool for creative professionals.

Knowledge Workers

Knowledge management is critical for organisations to succeed in today's information-driven world. The integration of AI has the potential to revolutionise knowledge management practices by automating tasks and creating new knowledge.

Knowledge workers are workers whose main capital is knowledge. This means professionals whose primary job responsibilities involve creating, analysing, and managing information or knowledge. These workers rely on their cognitive skills, expertise, and experience to solve problems, make decisions, and generate value in various industries.

Knowledge workers depend on information to do their jobs well. But as company data stores grow, knowledge workers too often have trouble finding and organising the information they need.

Their workflows are frequently interrupted by searches for better information, making it difficult or impossible to maintain a state of flow in which they produce their best work. Worst of all, workers are regularly forced to make suboptimal decisions on bad data.

Companies are digitising and automating their operations, displacing certain roles held by knowledge workers. AI writers are now part of organisations to create content for various purposes such as marketing, SEO, and customer service.

AI Reduces the Cost of Creating Content

Companies can save costs for paying writers and editors by automating the content creation process. AI can also generate high-quality content that can be used in various industries. For example, AI writers can create press releases for a company to distribute to the media or blog entries to engage its customers. And now AI is in offices, laboratories, and likely even your home and from medical diagnostics to mining and exploration, now many industries are using AI to make their workers more effective.

New AI capabilities that can recognise context, concepts, and meaning are opening up surprising new pathways for collaboration between knowledge workers and machines. Experts can now provide more of their own input for training, quality control, and fine-tuning of AI outcomes.

AI has moved from the realm of science fiction and leaped into practical reality. Though technological advancements push the boundaries of what’s possible every day, government leaders still face significant challenges in adopting and scaling AI. Since its ascendance roughly a decade ago, the neural-network technology behind artificial intelligence has transformed everything from email to drug discovery with its increasingly powerful ability to learn from and identify patterns in data.

Knowledge workers are active across many industry sectors, anywhere, in fact, where a person needs to use their intellect and judgment to synthesise information and make decisions. AI is proving to be a help to knowledge workers in numerous ways, across a wide range of sectors.

Stating the obvious, knowledge workers need insights to work with. This only exists when raw data is put through some sort of filter, but processing data can take a human much longer than it can take a machine, even when they’re working with coherent data sets.

When more data sets are added in, with variables of differing nature, perhaps also with weighting, the human brain can become overwhelmed.

Not only are computers faster at working with big data, but they also don’t make mistakes due to oversight or fatigue and and can keep going 24 hours a day. With data analytics, machines can identify trends, spot anomalies, and understand nuances, and bring to our attention things we might easily dismiss or overlook. They can flag up the expected, the unexpected, and the unusual.

The key benefit of industrialised AI is it helps knowledge workers spend more time acting on knowledge, or “insights”, and less on actually discovering that knowledge in the first place. That way, people can optimise their use of time. We look at four very different industry sectors and show how AI helps knowledge workers in them do a better job.

Medical Diagnosis

Consider medical diagnosis, where AI is likely to become pervasive. Often, when AI offers a diagnosis the algorithm’s reasoning isn’t obvious to the doctor, who ultimately must offer an explanation to a patient, the black box problem. Highly trained specialists need to analyse increasing volumes of data to help diagnose more and more patients with potential illnesses. Indeed, they have a data mountain to climb. The last thing medical specialist want to do is miss out on opportunities to help people as they sift these mountains of data.

In many areas of diagnostic medicine, AI is taking on the heavy lifting. It can do this by reviewing large amounts of data at speed, alerting professionals to anomalies and to specific characteristics it has been asked to identify.

Google Brain has developed a system that opens up the black box and provides a translator for humans. For instance, a doctor considering an AI diagnosis of cancer might want to know to what extent the model considered various factors she deems important, the patient’s age, whether the patient has previously had chemotherapy, and more. At the specialist London hospital, Moorfields, a team has developed a way to use AI to assess the severity of a form of age-related macular degeneration(AMD), as well as how it’s progressing and responding to treatment. Manually doing this work can take up to an hour per eye, and is prone to human error.

The AI can match, and even outperform, predictions made by specialists in a fraction of a second. This is one of a number of ways Moorfields Eye Charity is researching the use of AI.

A team, led by Dr Konstantinos Balaskas, at Moorfields Eye Hospital Ophthalmic Reading Centre and Clinical AI Hub has developed a fully automated, deep-learning model (algorithm) that can detect and quantify geographic atrophy (GA) using standard eye scans. Developed entirely in-house by the AI team at Moorfields, this will be hugely beneficial to clinicians caring for patients with GA, providing a reliable and fast way to assess the severity of geographic atrophy, how quickly it progresses and how well it is responding to treatment.

Personalised Education

Education is about the giving and receiving of knowledge. But individuals don’t all learn in the same way or at the same speed, and some of us need more support or nudging than others. At the higher levels, in particular, people can want to be free to learn at their own pace. Educational institutions need to work differently to support and help them.

AI can be used to deliver educational content at different paces, and in different forms. Students can repeat learning exercises in diverse ways to help them fully understand concepts or complete learning modules. AI can help identify students who might need support and provide useful nudges.

Georgia State University has been using an AI-enhanced messaging tool called Pounce since 2016. Georgia State’s pioneering, artificial intelligence-enhanced chatbot “Pounce” is well established as an institutional tool for helping incoming students navigate the thorny world of finances, registration and just getting started in college.

Now, Georgia State is showing student performance jumps when classes employ the chatbot to keep them connected. Receiving direct text messages about their class assignments, academic supports and course content increased the likelihood students would earn a B or higher and, for first-generation students, increased their likelihood of passing the class. First-generation students receiving the messages earned final grades about 11 points higher than their peers.

In 2021 the chatbot was integrated into course content, including so it could issue reminders to complete work by deadlines, and to take practice tests. Students getting this help turned in better grades. Using AI in this way helps teachers give their students the best support and assistance.

Mining & Exploration

Mining involves complex processes and data analytics to identify the best localities in which to work. It also requires the use of precision equipment, which needs to be kept at peak performance and must maintain the highest possible safety and compliance standards.

Predictive maintenance can be used to help diagnose equipment and ensure parts are replaced before they break and call work to a halt. AI can also be used to analyze different data sets and suggest the most likely locations to mine. The technology might also be able to analyse the environmental conditions inside mines to support compliance, and play a role in maximizing yields by sorting the materials mined.

A multi-national resources company, which manages freight of more than $1 billion annually for shipping across the world, wanted to enhance logistics with analytics capabilities. They tapped into Infosys Nia. This is an AI platform that helps companies manage their logistics, and combines data in numerous formats to help improve the success rate of discovery and exploration. It can be used to provide humans such as geologists and engineers with detailed information they can analyse further to help do their own jobs more efficiently.

City Planning

Cities and towns are complex, and people need to be able to move around. The right facilities, housing, work, leisure, need to be in the right places, too. Local governments are becoming increasingly aware of the need to reduce car travel and pollution and to increase access to open spaces and entertainment locally. Urban planners have their work cut out comparing variables and finding the solutions that work best.

AI-based tools can be used to model different scenarios for any particular area plan, taking into account environmental factors like heat, wind, or rainfall, and making the tweaks necessary to minimize negative environmental effects. In major cities, AI can analyse data on real places to support further development, such as transport planning, and provide analytics humans can use to better allocate resources.

Transport for Wales has worked with a third-party AI specialist, Optibus, to analyse bus use, demand for public transport, and movement patterns. Analysing this data has allowed TfW to improve bus schedules, punctuality, and journey times. Using the Optibus software, TfW worked with all 22 local authorities to design a better public transport network with the aim of improving the quality of service and making buses more reliable, especially in rural areas.

Reduction In Job Opportunities

The advance of AI is undoubtedly a major development in the world of technology and telecommunications. While it has been used to automate mundane tasks and increase efficiency, the potential of this tool has quickly become apparent, and it is now being used to take on more complex tasks, such as knowledge work.

Knowledge workers use collected information to make decisions and manipulate complex systems. This work often requires creative thought, critical thinking, and problem-solving abilities. Unfortunately, due to the advancements in AI, many of these tasks are now being handled by AI-driven software. This results in a decrease in job opportunities for knowledge workers.

One of the biggest implications is a loss of job security. Without a reliable source of employment, knowledge workers may struggle to make ends meet and may have to take on roles that do not require their skill set or provide the same level of job satisfaction.

Additionally, this will reduce the number of professionals in the field, as many will be unable to compete with the speed and efficiency of AI systems. Furthermore, this decrease in opportunities will also affect the wage gap between AI programmers and knowledge workers. Highly skilled programmers often develop AI systems, and their salaries will naturally be higher than their knowledge-worker counterparts. This will lead to a further wage gap between those capable of advanced programming AI and those not.

Although the implications of the development of AI-driven software are far-reaching, it is still important for knowledge workers to remain optimistic about their future. Many highly skilled jobs still require creative problem-solving, and AI will never replace the human touch. Knowledge workers can find ways to adapt and continue to secure jobs. All it takes is a bit of creativity and the willingness to evolve.

Conclusion

Overall, the implications of AI displacing knowledge workers are far-reaching and complex. AI has the potential to significantly alter the role of knowledge workers and how they are compensated, which could have a significant impact on society.

There will need to be careful consideration of the implications of AI for society if we achieve a successful, sustainable transition to an AI-driven economy.

As AI technologies become more advanced, the disruption to traditional knowledge workers will become increasingly visible. This is not to say that AI will necessarily replace knowledge workers entirely, but that knowledge workers will need to adapt to the changing job market and find ways to integrate AI into their work.

AI writers are changing the narrative of the modern knowledge worker, providing a versatile and efficient tool to help knowledge workers stay competitive and make their jobs more productive.

AI writers can potentially make knowledge workers more effective and efficient, saving time, money, and energy. In the future, AI writers and other AI technologies could be seen as invaluable assets to knowledge workers and their businesses, allowing them to stay up-to-date and maximise their asset utilisation.

The ultimate objective should be to reshape roles and tasks to gain AI efficiencies while preserving and discovering more meaningful, personal human interaction. This is not about choosing between AI and human effort potentially ending up in millions of layoffs, but about learning to leverage both for more significant, fulfilling outcomes, creating value for organisations and society.

This transition will require a potential transformation of work culture, organisational structure, and leadership model, but the promise of more harmonious, meaningful work combined with productivity gains is well worth the effort.

As organisations continue to explore and apply AI in their knowledge management strategies, it is essential to exercise caution, critically evaluate information, and seek validated sources where necessary. With careful implementation and discovery, AI can shape the future of knowledge sharing and management.

Take the next step by leveraging AI for knowledge management. Identify areas where AI could impact your organisation and consider running some checks and review your firms experience.

References:

HBR: ITPro: Deloitte Deloitte HBR:

QuantaMagazine: Time: Moorfields.nhs

Georgia State Univ: Forbes: Microsoft:

Business901: Marketing AI Iinstitute.com

lSE: DGgen: aimmai:

Prashant Gupta / LinkedIn

Image: qimono

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