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How Can Line Managers Leverage AI Amidst Its Risks?

It will not have escaped your attention that Artificial Intelligence (AI) is becoming more widespread and is being adopted by more and more businesses in industries of all types.

There has been a lot of discussion about the speed at which AI is being developed and speculation about how it may threaten the roles of some individuals. Elon Musk, among other tech giants, sparked concern when they called for a pause in the training of AI systems as they posed a “profound risk to society and humanity”. Without verified evidence, it is difficult to predict how AI will continue to develop and what safeguards will be put in place but one thing that we know for certain is that Artificial Intelligence is here and it is not going away! It is therefore important that as Line Managers we become familiar with the different types and forms and uses of AI, how it can benefit us, and some of the considerations for managing risk before unleashing its full potential.

In this article, I define the common types of AI and their potential applications and then provide some insight into how the different forms of AI can benefit the role of a Line Manager. I then go on to explore some of the known risks so that Line Managers can adopt ways to mitigate and control how AI is used.

AI and Brain Illustration

The Different Types of AI

Narrow AI (Weak AI)

Narrow AI refers to AI systems designed to perform specific tasks or functions within a limited domain. It excels at narrow, well-defined tasks but lacks general human-like intelligence. Some examples include:

Speech Recognition Systems

Used in voice assistants like Siri and Google Assistant, as well as transcription services.

Line managers can benefit from speech recognition systems in their work by using voice assistants to manage schedules, set reminders, and access information hands-free, improving efficiency and productivity.

Image Recognition Systems

Used in facial recognition, object detection, and self-driving cars.

Line managers may utilise image recognition systems to automate tasks like inventory management, quality control, and visual data analysis, allowing for faster and more accurate decision-making.

Recommendation Systems

Line Managers may be familiar with these as they are used in personalised product recommendations on e-commerce platforms or content suggestions on streaming platforms.

Line Managers can leverage recommendation systems to optimise employee training and development plans, identify suitable project assignments, and improve decision-making by receiving personalised insights and suggestions.

General AI (Strong AI)

This form of AI is currently hypothetical and does not yet exist.

General AI refers to AI systems that possess human-level intelligence and can perform any intellectual task that a human being can do.

As strong AI does not yet exist, its impact on Line Managers cannot be determined at present. However, if achieved, it is thought that it could potentially revolutionise various managerial tasks by emulating human-level intelligence across multiple domains.

Machine Learning (ML)

Machine Learning is a subset of AI that focuses on algorithms and statistical models that allow systems to learn and improve from data without being explicitly programmed. Applications of ML include spam filtering, fraud detection, natural language processing, and medical diagnostics. ML can be further classified into:

Supervised Learning Models

SLMs are trained using labeled datasets and they can make predictions or classifications based on new, unseen data.

Line Managers can benefit from SLM models trained on historical data to predict outcomes, such as sales forecasts, demand planning, and employee attrition rates, aiding in decision-making and resource allocation.

Unsupervised Learning Models

ULMs learn patterns and relationships from unlabelled data, aiming to discover hidden structures or clusters within the data.

Line Managers can utilise unsupervised learning algorithms to analyse large datasets, identify patterns, and gain insights into customer behaviour, employee performance, or operational inefficiencies, leading to data-driven decision-making and process optimisation.

Reinforcement Learning Agents

RLAs learn through trial and error interactions with an environment, receiving rewards or penalties for their actions.

Line Managers may employ reinforcement learning algorithms in optimising resource allocation, scheduling, and task assignments, ensuring efficient utilisation of resources and improving overall team performance.

Deep Learning

Deep Learning is a subset of ML that involves neural networks with multiple layers, enabling systems to automatically learn hierarchical representations of data. Deep Learning has found applications in various domains, such as:

Computer vision

This enables object recognition, image and video analysis, and autonomous driving.

Line Managers in industries such as manufacturing or retail can utilise computer vision systems to automate quality control inspections, monitor safety compliance, and analyse visual data for insights on process improvements.

Natural Language Processing (see definition below)

Line managers can leverage NLP to automate routine communication tasks, such as reviewing and summarising documents, analysing customer feedback sentiment, and assisting with employee queries, improving communication efficiency.


Deep learning algorithms can be applied to medical imaging analysis, such as interpreting X-rays, MRIs, CT scans, and pathology slides.

Line Managers in healthcare settings can leverage computer vision systems to ensure accurate diagnosis which is not dependant on human interpretation of results which are open to error. This benefits the department they are overseeing and also patient outcomes.

Natural Language Processing (NLP)

NLP focuses on enabling computers to understand, interpret, and generate human language. NLP applications include:

Chatbots and virtual assistants

Chatbots and VAs can be used for responding to user queries, providing customer support, and performing simple tasks.

Line Managers can utilise chatbots or virtual assistants like ChatGPT to handle routine employee queries, provide on-demand information, and assist with basic HR tasks, enabling them to focus on more strategic aspects of their role.

Sentiment analysis

NLP is capable of analysing text to determine sentiment, opinions, or emotions expressed.

NLP-based sentiment analysis can help line managers gauge employee satisfaction, sentiment, and engagement levels by analysing feedback, surveys, or social media posts, allowing them to take appropriate actions to address concerns and improve team morale.

Language Translation

NLP is also capable of translating text or speech from one language to another.

NLP-based language translation tools can assist line managers in overcoming language barriers, facilitating communication with employees or stakeholders from diverse linguistic backgrounds.


This form of NLP enables computers to understand, contextualise, interpret, and generate human language in the form of text-based conversation involving more than one command prompt.

Line Managers can benefit from the ChatGPT tool in several ways such as providing complex information, and drafting text based on a series of commands if incorporated into internal IT systems it can provide and present data, analyse trends, and flag any anomalies or peaks and troughs such as employee absence or breaches of KPIs.

Expert Systems

Expert systems are AI programs designed to replicate the decision-making abilities of human experts in specific domains. They are often rule-based systems that use knowledge bases and inference engines to provide expert-level advice or solutions.

Medical diagnosis - Expert systems can assist in diagnosing diseases based on symptoms and medical history.

Financial planning – Expert Systems can provide personalised investment advice and financial recommendations.

Quality control – Expert systems can identify defects and anomalies in manufacturing processes.

Expert systems can provide line managers with access to domain-specific expertise, aiding in decision-making processes related to complex tasks, such as financial analysis, project management, or risk assessment. This form of automation can speed up the process and free up resources for other more labour intensive tasks.

The Impact of AI in a wider context

It is important to note that the impact of AI on Line Managers may vary depending on the industry, organisational context, and specific use. The examples provided above highlight some potential applications of each AI form, but their actual impact will depend on the successful integration and adoption of AI technologies in the workplace. The wider and more general benefits of AI include:

Automation of routine tasks

AI technology can automate repetitive and mundane tasks, allowing line managers to focus on more strategic and value-added activities. This can free up their time to concentrate on decision-making, problem-solving, and employee development.

Enhanced decision-making

AI systems can process vast amounts of data quickly and provide valuable insights to line managers. With AI-powered analytics and predictive modelling, managers can make data-driven decisions, identify trends, and anticipate future outcomes with greater accuracy.

Improved productivity and efficiency

AI tools and platforms can streamline processes, optimise workflows, and improve operational efficiency. Line managers can leverage AI-powered project management tools, collaboration platforms, and intelligent scheduling systems to enhance productivity within their teams.

Augmented performance management

AI can assist line managers in performance management by providing objective performance evaluations based on data analysis. AI algorithms can identify patterns, track performance metrics, and offer feedback on individual or team performance, helping managers make informed decisions regarding performance appraisals, training, and career development.

Enhanced employee engagement

AI-powered chatbots and virtual assistants can assist line managers in responding to employee queries, providing real-time feedback, and delivering personalised learning and development opportunities. This can lead to improved employee engagement, satisfaction, and retention.

Are there any negatives or things to watch out for?

There will always be some tension and speculation about how AI might one day make humans obsolete in certain roles. As we increase the use of AI it makes sense that some functions will be more efficiently undertaken with AI. However, the common understanding at present is that there are far too many risks associated with AI currently to rely on AI without a human overseeing the output and adopting the results of AI without some form of checks in place. Here are some of the considerations for Line Managers as they start to dip their toe in the water and practice using forms of AI in their work.

Bias and discrimination

AI systems can inherit and perpetuate biases present in the data they are trained on. If the training data contains biases related to race, gender, or other sensitive attributes, AI algorithms may produce biased outcomes, leading to discriminatory practices or decisions. It is crucial to ensure proper data representation and algorithmic fairness to mitigate this risk.

Lack of transparency

Some AI models, such as deep learning neural networks, can be complex and operate as "black boxes," making it challenging to understand how they arrive at specific decisions or recommendations. Lack of transparency can hinder accountability, raise ethical concerns, and make it difficult to identify and rectify potential errors or biases in AI systems.

Data privacy and security

AI often relies on vast amounts of data, including personal and sensitive information. The collection, storage, and use of this data raise concerns regarding privacy and security. Inadequate data protection measures can result in data breaches, unauthorised access, or misuse of personal information, leading to legal, reputational, or financial risks.

Dependency and job displacement

Over-reliance on AI systems can lead to reduced human involvement in certain tasks or job roles. While AI can automate repetitive and mundane tasks, it may also lead to job displacement and necessitate workforce restructuring. It is crucial to consider the socio-economic impact and ensure proper reskilling and upskilling opportunities for affected employees.

Ethical dilemmas and decision-making

AI systems may face ethical dilemmas when confronted with complex situations that require subjective judgment or consideration of moral values. The ability of AI to make ethical decisions or evaluate the consequences of its actions is still limited. Line managers and organisations need to be cautious and ensure that ethical frameworks are in place to guide AI decision-making.

Overreliance on AI and reduced human skills

Relying heavily on AI systems can lead to a diminished reliance on critical human skills, such as critical thinking, creativity, and emotional intelligence. Excessive dependence on AI for decision-making may erode human expertise and hinder the development of essential skills necessary for complex problem-solving and innovation.

Technical limitations and errors

Perhaps one of the biggest concerns is that AI systems are not infallible and can make errors or provide inaccurate results, especially in complex or novel situations. The performance of AI models can be limited by the quality and representativeness of the training data, algorithmic limitations, or adversarial attacks. Line managers should be aware of these limitations and exercise caution when interpreting and acting upon AI-generated outputs.

Adaptation and upskilling

Line managers will need to develop a basic understanding of AI technologies and how they can be integrated into their work. This may require acquiring new skills, such as data analysis, algorithm interpretation, and AI governance. Managers should be open to learning and collaborating with AI systems to leverage their potential effectively.

Key Takeaways:

1. AI can bring significant benefits to line managers, including efficient communication, task automation, handling employee inquiries, decision support, and insights.

2. VA and ChatGPT can assist line managers by providing instant responses, automating routine tasks, and improving employee engagement and satisfaction.

3. However, there are risks and downsides to consider, such as bias and discrimination, lack of transparency, data privacy and security concerns, job displacement, ethical dilemmas, overreliance on AI, and technical limitations.

4. Responsible AI practices, including addressing biases, ensuring transparency, safeguarding data privacy, considering socio-economic impacts, and fostering human-AI collaboration, are essential to mitigate risks and maximize the benefits of AI.

By understanding the potential benefits and risks associated with AI, line managers can make informed decisions about its integration, use AI as a tool to augment their capabilities, and establish responsible practices to mitigate potential downsides.


Disclaimer: This article was generated with the assistance of ChatGPT but was heavily edited and stylised by the author at LMA. No sources have been captured during the content generation. When asked, Chat GPT stated:

As an AI language model, my responses are generated based on a mixture of licensed data, data created by human trainers, and publicly available data. I have not been directly trained on specific sources or have access to proprietary databases. I should note that while I strive to provide accurate and up-to-date information, my responses may not always reflect the most current research or developments in the field of AI. Therefore, it's always a good idea to consult multiple sources and refer to peer-reviewed studies or expert opinions for comprehensive and reliable information.”

So what do you think of this attempt at using ChatGPT to augment the task of researching Artificial Intelligence, its uses and benefits, and associated risks?

As an author, I have found it helpful to create structure but my normal research methodology is probably quicker and more easily verified than using this tool.


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