AI in the Workplace and How It Supports Us

Over the last decade, there was significant fear that Artificial Intelligence (AI) will replace all humans at the workplace. However, those fears were totally unfounded, and today we work more efficiently and effectively, side by side, with smart algorithms.

According to IDC, enterprises across the planet will spend approximately $656 billion this year on future-of-work technologies. The bulk of that money will go towards AI-powered software for enterprise applications. As most of us will work remotely in the coming years, the industry will also focus more on overcoming challenges related to collaboration and productivity.

Let’s look at how AI is already supporting us at work, transforming how we get things done.

1. Improve Digital Workplace Collaboration

The pandemic accelerated digital transformation initiatives. Today, most of us work in hybrid workspaces where employees seamlessly collaborate both virtually and in person.

In this scenario, AI has the potential to enhance each job function. For example, smart algorithms can quickly sift through thousands of applications and shortlist appropriate candidates to support Human Resources (HR) professionals. This approach helps save a significant amount of time and resources while boosting employee morale.

When everyone within the organization uses AI-powered tools and works together in a highly intuitive and collaborative environment, businesses can make better decisions and make them much faster.

According to a survey of 2,000 managers, as many as 59% reported better collaboration at the workplace after AI adoption. Another 78% saw significant improvement in decision-making and efficiency.

2. Enables Efficient Knowledge Management

Employees want easy access to the right information and at the right time to get their work done. Manually trawling through data to find what they are looking for is time and resource-intensive. However, with the help of AI and automation, we can automate much of that process at scale.

The key here was to achieve true automation, efficient knowledge management, and knowledge sharing at scale without introducing complexity. That is why it took decades for the concept of knowledge management to come to fruition. In years past, the idea of knowledge management worked in practice, but it was impossible to scale.

3. Automate Tedium Routine Tasks

AI in the workplace frees humans up to do more meaningful work. Over the years, this has been the most popular use case for AI adoption, especially, Robotic Process Automation (RPA).

RPA isn’t the same as traditional automation. Traditional automation involves software that performs the same task repeatedly without any variation. In contrast, RPA leverages Machine Learning (ML), and the software continuously learns how to execute the job, make the process more efficient, and complete it faster.

Traditional automation protocols work quietly in the background on specific platforms. However, RPA is on the frontlines at a user interface level and personalizes its interaction on an individual level. You can also easily integrate RPA into multiple solutions common to enterprise environments.

For example, RPA is now a standard in customer support verticals. Intelligent algorithms help customer services professionals and tech support teams by handling simpler and more straightforward requests like resetting a password. When you embed AI and ML into customer service verticals, support staff are free to take on more complicated problems.

RPA is also rapidly becoming part of the onboarding process in many organizations. In this scenario, RPA will send out hiring letters and related documentation, input new employee details, and maintain the HR database, streamlining workplace experiences right from the start.

4. Streamline Maintenance Protocols

The Internet of Things (IoT) is making autonomous maintenance a reality. With smart sensors setting the baseline for asset performance indicators, intelligent algorithms help determine the upper and lower limits for each.

Predictive maintenance protocols help companies save big. According to McKinsey & Company, AI-powered predictive maintenance can improve availability by up to 20%. At the same time, predictive maintenance also helps reduce inspection costs by 25% and annual maintenance fees by up to 10%.

Autonomous floor cleaners can support maintenance staff using sensors to navigate through pre-programmed paths within facilities. As these robots are connected to the cloud, you also have easy access to operations data, space utilization data, and machine-generated analytics reports.

When AI takes over floor cleaning and disinfecting tasks, maintenance teams will be free to focus on more strategic tasks. For example, they can spend their time engaging in capital planning and maintaining building systems.

As these autonomous machines can clean and disinfect large spaces, maintenance teams are free to take on more strategic tasks like maintaining building systems and capital planning.

Furthermore, by comparing current and historical data, these machines will inform facilities teams whenever the asset is heading towards failure. This approach ensures that the device is repaired or serviced before it becomes an issue. As such, it also helps avoid potential downtime and delays in delivering timely maintenance services.

Tip:

Got your own AI project idea? Use our crowd for specific and individual AI Training Data.

Datasets for Machine Learning

5. Demystify Customer Behavior and Scale

For many years, businesses struggled to understand their customers. However, enterprises can finally demystify customer behavior with the oceans of data generated by humans every second of the day.

When companies combine customer data and AI, they can quickly identify new opportunities to scale their operation. For example, whenever Amazon suggests a product you may like, that’s AI in action trying to cross-sell. By understanding the target market better, AI can also help retailers introduce their products to a broader audience.

6. Optimize Back-Office Processes

Digitization negates the need to hold on to tons of office documents. By leveraging end-to-end back-office automation powered by RPA and Natural Language Processing (NLP), organizations can also improve and increase their analytics capabilities. This approach helps eliminate paper waste while optimizing audit and historical data tracing protocols.

In this scenario, autonomous back-office systems can access applications, understand growth potential, eliminate overheads, improve public service outcomes, and much more. The key benefit is that these advanced back-office systems incorporate feedback loops to better understand service levels and enhance performance at a procedural level.

7. Make Conferences and Workshops Matter

AI in the workplace can help optimize video conferences and training workshops. This approach will help management better connect and communicate with their staff. For example, the Microsoft Teams bot already monitors audience reactions in real-time during video conferences. By analyzing facial expressions and gestures in real-time, organizations can adapt their messages and take staff training workshops to the next level.

The above is just the tip of the iceberg. Over the next few years, we can expect AI and ML to become far more advanced than any of us imagined. However, to get there, intelligent algorithms will need to work continuously with the right kind of data types that are both highly representative and inclusive. At the same time, organizations must continue to train their staff and encourage skills and knowledge transfers to keep them calm and engaged in a rapidly changing environment.

Andrew Zola

Ein Kommentar

kids teaching 03.06.2022, 16:40:12 Uhr

Post is very useful.Thank you this usefull information.
kids teaching world provides a great way to teaching kids with the help of visualisation and sound.