AI in the workplace:
10 key use cases, benefits, and challenges

AI in the workplace: 10 key use cases, benefits, and challenges

September 12, 2023

10 applications of AI in the workplace

From recruiting and onboarding to business process automation, companies are increasingly propelling the adoption of AI in the workplace and its influence on our careers and the labor market.


The effects of AI begin even before we set foot in a new office. For example, predictive analytics software powered by machine learning algorithms can segment and filter suitable candidates based on resumes and relevant metrics, matching their job experience with the required profiles. For instance, the recommendation systems of major job search social media leverage ML, including LinkedIn’s engine and its Recruiter feature for candidate ranking.

RecruiterSearch criteriaRecruiter frontendRecruiter actionsRecruiter backendOnline machine learning modelSearch indexRanked candidatesRealtime updatesRanking featuresQUEUEExtract transform loafLogged search resultLogged actionsLabel data generationModel trainingRecruiter contextLinkedIn membersStandardi-zed entitiesOffline batch processingIndex build

Scheme title: LinkedIn’s recommendation system architecture

Data source: The AI Behind LinkedIn Recruiter search and recommendation systems

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Benefits of AI in the workplace

Adopting artificial intelligence and related technologies in a business scenario can benefit both your staff and the organization as a whole, resulting in enhanced performance, cost optimization, new job opportunities, and a better work experience.

Increased productivity

Deloitte’s 2023 Global Human Capital Trends Report highlights that artificial intelligence and machine learning will contribute to a 37% increase in labor productivity by 2025. In fact, data-driven decision-making will enhance human impact at work and organizational performance.

Cost reduction

According to Bain & Company’s 2023 The Augmented Workforce Report, intelligent automation will lead to a greater focus on higher-value work (including problem-solving, creativity, and interpersonal communication) and a 21-30% cost reduction.

New job positions

The World Economic Forum estimated that by 2025, AI-powered automation could create 97 million new jobs (data analysts and scientists, business development professionals, digital transformation specialists, etc.), compared to the 85 million lost (such as accountants or data entry clerks).

Employee satisfaction

In its 2022 State of AI in the Enterprise Report, Deloitte pointed out that 82% of respondents expect AI to enhance job satisfaction. Specifically, many AI adopters see value in leveraging automation to reallocate workers from repetitive tasks to creative activities.

Bias-free decisions

Adopting AI-powered analytics helps minimize human bias and its impact on workplaces and careers. From automated candidate screening to accurate, data-driven staff performance assessments, professionals can expect a fairer approach to recruitment and talent management.

Accuracy and compliance

While AI's reliance on data (including sensitive information) can raise concerns among regulators and should be addressed carefully, its adoption can be beneficial for corporate compliance. For example, AI-based RPA can increase reporting consistency and accuracy by about 80%, according to Deloitte, while lowering the number of employees who have access to sensitive information.

Risk mitigation

AI safeguards your workforce from operational risk in industrial scenarios via anomaly detection and predictive maintenance, minimizing the likelihood of failures and consequent disasters. It can also identify signs of fatigue or psychological discomfort to trigger targeted support initiatives.

Inclusive workplaces

AI fosters workplace diversity and inclusivity by enabling data-driven skill assessments and blind hiring. For instance, AI-based talent intelligence platforms can rank candidates based on their actual expertise while masking identifiable attributes and protected traits (age, gender, disabilities, etc.).

AI implementation roadmap in the workplace

Organizations implementing AI in their workplaces usually go through these key steps, which can vary based on their business scenarios, selected use cases, and technologies involved.


Business needs analysis

Assess business needs and expectations via discovery workshops, interviews, and process observations

Audit the existing technical environment

Highlight the project's scope, objectives, deliverables, and timeframes

Define the future solution’s functional and non-functional requirements


Initial data analysis

Carry out an exploratory analysis to map and assess available corporate data sources

Identify external data sources, such as public databases


Solution design

Design the AI solution’s architecture, main modules, and features

Define a project plan, budget, and timeline

Identify a suitable tech stack based on the technical and business evaluation

Optionally deliver a PoC to ascertain the AI solution’s feasibility, financial viability, and potential limitations


Building the AI solution

Carry out data pre-processing, including data cleansing, annotation, and transformation

Outline the solution’s assessment criteria

Train one or more AI models via supervised, unsupervised and reinforcement learning to achieve the desired output


Integration and rollout

Integrate the AI model into the solution to power its features with the model’s output

Deploy the product to the target environment (on-premise or in the cloud)



Retrain the AI model with new data over time to enhance the accuracy of its output

Perform ongoing maintenance, fixes based on user feedback, and upgrades with new features

Challenges and guidelines for adopting AI in the workplace

Despite the payoffs unlocked by the use of AI in the workplace, adopting and scaling it across your organization can involve a range of business and technical challenges. Here are a few tips to mitigate such issues while maximizing the value of AI for your workforce.




Ethical risks

Scheme title: Barriers and risks when adopting AI in corporate scenarios

Data source: — Deloitte’s State of AI in the Enterprise, 2022 — AI for work relationships may be a great untapped opportunity, 2022

Use case identification


Implementing AI solutions can be very impactful but also time-consuming and financially demanding due to their complex architectures, processing power requirements, and long algorithm training times. Deciding whether AI represents a better option for your use case over conventional technologies is essential for making its adoption worth the effort and investment, maximizing ROI and ensuring stakeholders’ and executives’ buy-in.


It makes sense to implement AI when facing serious inefficiencies affecting key business processes, which cannot be solved with "traditional" software solutions. Investing in AI can also be a good choice when it comes to improving the most profitable corporate functions depending on your industry. For example, McKinsey mentions supply chain management for retailers, product development for high-tech corporations, service operations in telecommunications industries, product assembly in the automotive sector, and risk management for financial services.

AI and the future of work

In recent years, together with other technological advances, AI has reshaped our physical and digital workplaces. However, its adoption may raise concerns due to potential skill gaps, along with the tension between regulations and AI’s need for big data. To address these and other implementation challenges, rely on Itransition's expertise in AI consulting and development.