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Making predictions about the future of artificial intelligence is tricky, but there are certain trends that continue to hold strong. This articlefocuses on these lasting elements, amidst the rapid advancements in the field in 2024.  

We look at the core developments that are shaping AI's future: the steady improvement of machine learning techniques, the increasing presence of AI in everyday technology, and the ongoing need for ethical guidelines as AI systems become more complex. This article aims to provide a clear view of what's staying the same in AI, offering a grounded perspective in a field where change is the only constant.  

Table of contents


AI Integration in the Workplace 

The recent boom in Artificial Intelligence technology and innovations has had a positive impact on workplaces all around the world. Both small and large businesses have implemented AI tools for multiple purposes, such as automating routine tasks, streamlining workflows, and optimizing operations. All this has ultimately reduced costs, boosted productivity, and even created healthier and safer workplaces. 

A survey done in 2023, found that 55% of the companies surveyed had invested in AI to integrate it into their workflows during that year, and 58% will continue to do so in the future. This only elucidates the relevance of AI in today’s working environment, not as a substitute for human workers but as a colleague who will coordinate and cooperate with humans to enhance their skills. 




Source: PWC

The common uses of AI in the workplace include the automatization of repetitive tasks, which ultimately saves time and effort that can be redirected to non-automatable tasks. Customer service can also be enhanced by the implementation of AI-controlled chatbots that are available 24/7, leading to higher customer satisfaction. On the other hand, AI can also gather data from the company to determine the functionality of workflows and processes, detect weaknesses, or predict probable outcomes during a certain operation.  

AI technologies can also help to enhance the safety of a workplace, both in terms of monetary and human resources. PayPal, for example, has implemented machine learning in its website to help detect fraudulent activities. Since there are millions of transactions occurring through the website every single day, it would be impossible for a team of human workers to verify the validity of every transaction. As such, AI technologies are being used to detect patterns in the user’s activity, raising alarms whenever a suspicious activity is flagged. This has helped to protect the user’s money as well as the company’s. 

The use of chatbots in customer service has also been widely employed by several companies due to their cost-efficiency, the instant support they provide, their multi-tasking capabilities, and their ability to gather data useful for the company, such as frequently asked questions, common complaints, and areas for improvement. Companies as different as Amazon, Marriot International, and H&M have successfully used this technology to provide support to customers and keep them happy. 

In factory industries, AI can be used to monitor processes and to return and distribute information in an understandable way. For example, General Electric has been using its Brilliant Manufacturing Suite across its 500 factories to provide a panoramic view “of all production activity for all factory personnel via real-time”. This results in process and production data that flags weaknesses and strengths that could then be revised. 

As can be seen by these examples, AI has come to stay and become a powerful ally in a lot of companies, ranging from large factories to small businesses. Nowadays, due to the technological dependency of many processes, a lot of them can be automated. This does not necessarily mean that human beings will lose their jobs. According to Erik Brynjolfsson and other experts from Standford University, “If we embrace [AI], it should be making our jobs better and allow us to do new things we couldn’t have done before. Rarely, will it completely automate any job ─ it’s mostly going to be augmenting and extending what we can do.”  

On the other hand, new skill requirements related to technology will be required for jobs that are not technologically driven. This means that former workers might need to be reskilled or upskilled through retraining and that new specialized jobs such as AI-driven healthcare professionals or bankers that focus on AI-driven financial solutions will arise. AI will bring new opportunities to those companies and workers willing to embrace it.


AI-Driven Productivity Tools  

Nowadays, Artificial Intelligence is already an important part of our everyday lives and there are already several AI-driven tools that, implemented in the workplace, can enhance personal and organizational productivity. 

For example, apps like Any.do or BeeDone help to organize the tasks of a day and to figure out steps to get these things done. They create a viable plan that makes execution easier and optimizes processes as well as sets reminders. This can be used for personal use for everyday tasks such as doing laundry or tidying up a room, but also for work-related tasks such as replying to e-mails or talking to a client. 

Other apps that have been very popular this last year include AI chatbots such as Chat GPT or Claude 2. These programs implement Large Language Models (LLM) to provide a user-friendly interface for anyone who can use a computer or a smartphone. They can be used as productivity tools as they can save time in research or content creation. 

Furthermore, amongst the wide variety of AI applications, there are several of these that can be implemented in the workplace for project management, as well as to ease communication and collaboration. 

Asana, for example, is a software that creates a shared virtual space for the organization and collaboration of a whole work team. It can use common boards that prioritize tasks and organize the information in a clear way. Additionally, this software uses historical data from the business to identify risks or to create goals for a certain project. It can also be asked questions that are answered according to the information gathered by the platform. 

Other tools that can be useful for communication and collaboration are note-takers based on AI. These tools can generate automated notes from meetings or conversations so that they can be available for future reference, allowing for better real-time conversations with fewer distractions. Some of these, such as Fireflies, can even summarize the contents of the meeting and generate related text. In this way, information is easily accessible and plenty of misunderstandings can be avoided. 

A last example of these would be AI apps that manage emails both between workers in a company and between the company and the clients. Nowadays, a lot of relevant exchanges are done through electronic mailing platforms. These AI tools are capable of automatically categorizing emails, prioritizing important messages, and even generating drafts that ultimately would be revised by the user. Examples of these are SaneBox, Mailbutler, and EmailTree. 

As can be seen by all these examples, implementing AI tools in the workflow of any company provides undeniable benefits such as increased efficiency and productivity, an improved customer experience, and even better collaboration and communication between employees either with other employees or with clients. However, one must take into account that these tools are still relatively new and as such, still have some limitations, such as a lack of total accuracy due to biases that could be found within the data as the AI learns the processes of a company. Also, it is relevant to consider the initial costs and technical challenges that might arise from implementing the tools in a new workplace, which include, of course, some time and resources spent on the training of employees on the use of these tools. 





AI in Decision Making and Strategic Planning  

AI in the workplace not only benefits productivity, communication, or collaboration but can also participate in the decision-making process and the planning of strategies for the long and short term of a company, taking into account data that might escape the human eye. 

In today’s business environment, AI algorithms are being utilized for data analysis. What this implies is that algorithms can be trained to process relevant data sets from the past or current activities of the company (which entails things like production, management, retail, and customer habits, among others) at a speed greater than any human could. Depending on the need, the algorithm can help identify problems with a certain product or detect areas of opportunity within a certain workflow. These areas of opportunity might include trends, risks, or openings in business strategies that can then be tackled through an informed decision.  

Furthermore, algorithms can also return predictive data due to their ability to learn from previous experiences and their access to multiple databases. This predictive data can include conversion rates, possible leads, forecasts of revenue, or customer churns. Also, by processing information such as revenue, expenses, or customer habits, an algorithm can forecast a trend in a product during a particular period. With this information at hand, the company can plan accordingly and strategically, depending on what their aim is. Because of this, AI is a powerful colleague in the decision-making process, one that will certainly help a company reduce risks during the decision-making process. 

Of course, AI-assisted decisions must also consider several factors that AI might have passed over due to its possible biases or the lack of training in matters such as ethics or accountability.  

For example, the Australian Computer Society (ACS), believes that an AI application should consider human concepts and values before making a decision. Among these we can count the following:

  • Environmental and social wellbeing 
  • Human rights 
  • Fairness (non-discrimination) 
  • Privacy protection 
  • Safety 
  • Transparency 
  • Contestability 
  • Accountability 

To ensure that these concepts are part of the decision-making process, it is essential to understand that collaboration between humans and machines is still necessary. As of now, leaving an important decision solely to an AI application could have disastrous consequences for a company (for example, if the AI suggests a strategy that endangers the life of a worker). 

As we have mentioned before, AI’s job is not to take humans out of the loop but rather to become colleagues with them and enhance human’s skills. In this case, AI provides helpful information that will help the company or organization to create a business strategy according to their needs and goals. 

Learn more about how AI works in our article about AI algorithms work in our full article here. 


Top 10 AI Trends Boosting Productivity in 2024  

Autonomous agents and bots  AI-powered analytics for real-time insights  
Virtual and augmented reality application Edge computing and AI  
Personalized AI assistants AI in supply chain optimization and logistics 
Natural Language Processing  Blockchain and AI integration 
AI in energy management Machine learning algorithms 


As technology evolves and AI applications become more and more complex and capable, new trends and uses arise that can be implemented in the workplace to boost productivity. In 2023. we witnessed the undeniable popularity of generative AI applications such as ChatGPT or DALL-E 2 as well as smart assistants such as Amazon’s Alexa or Microsoft’s Copilot. However, 2024 promises more developments in different areas of AI. Here are the top 10 AI trends that will boost the productivity of companies in 2024: 

Autonomous agents and bots for customer service and support 
While autonomous chatbots are already a reality, new technologies and smarter algorithms point toward better AI agents that allow for more efficient customer service and shorter response times. These chatbots can be customized to meet specific needs in a user-friendly way. Ultimately, the use of these agents can result in better support for the customers and, therefore, a higher rate of client satisfaction, which could lead to more clients and better revenues.  

AI in supply chain optimization and logistics management 
AI can be implemented in the optimization of product and service workflows in a company. While it can help to automate certain processes, such as inventory management or quality checks, it can also help to review the supply chain and detect its areas of opportunity. A good implementation of AI in supply and logistics can lead to decreasing operational costs, shorten delivery times, or even improve the routes of transport.  

Natural Language Processing (NLP) for enhanced communication tools 
NLP is a kind of algorithm that allows computers to process, interpret, and produce coherent and cohesive human language. NLP is one of the pillars of AI tools in 2024 because it can be used to enhance communication in a company. Voice assistants like Siri or Google Assistant use NLP to communicate with humans, but this technology can be used to create summaries of large texts, to analyze likability or sentiment in social media, to create recommendation systems based on patterns of text from a certain user, or even to translate passages in different languages, allowing for global communication. 

AI-powered analytics for real-time insights and performance tracking
Being always aware of the performance of a product, a social media post, or even a worker is pivotal to the proper functioning of a company. AI can be trained to gather data from several sources or databases, according to the specific needs of the company. Relevant information such as units sold, conversion rates, or delivery times can be tracked by the AI, which will provide real-time insights about the performance of certain products. This will ultimately result in data-driven decisions that might reduce all kinds of risks within the work processes. 

Virtual and augmented reality applications for training and development 
Virtual and augmented reality are another type of technology that, enhanced with AI, can result in great benefits for productivity. VR and AR offer a lot of advantages when applied to training employees as they can provide realistic learning experiences without the costs or risks that might arise during real-life training. Different scenarios can be emulated to bring hands-on experience to the trainees regarding difficult situations. Besides, VR and AR applications can gather data on the trainees’ performances and suggest proper feedback. 

Blockchain and AI integration for secure and efficient transactions 
A blockchain consists of a “shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network” (IBM https://www.ibm.com/topics/blockchain). It is used in cybersecurity to keep transactions secure and efficient. A blockchain can be enhanced with AI for better results, as it can offer intelligent threat detection, automated secure transactions, or enhanced transparency of money handling.  

Edge computing and AI for faster, localized decision-making
The concept of edge computing is relatively new and refers to a distributed computing framework that locates applications closer to data sources (such as IoT devices) in a more local manner rather than cloud-based. Implementing AI to edge computing allows for bandwidth efficiency and enhanced privacy and security, which leads to faster decision-making due to the speed of accessibility of information in a localized way. 

AI in energy management and sustainability efforts 
As companies around the world become more conscious of the impact of pollution and natural resources, the concept of “sustainability” becomes increasingly relevant within production and management workflows. In 2024, AI can help a company to measure energy consumption and to create more efficient systems that reduce it. Furthermore, it can help to identify opportunities for decreasing waste of important resources like water during certain processes. Some AI models can also help minimize carbon emissions or forecast weather so that renewable energies such as wind or solar power can be integrated effectively into processes that need them. 

Personalized AI assistants for time management and scheduling 
Personalized AI assistants have become a must-have for several companies, as they can be applied to management, but also at an individual level. These AI assistants can automate schedules and organize meetings based on availability. The invitations to these meetings can also be sent automatically. More and more of these AI tools are being launched every so often, helping many users to keep track of schedules and daily tasks. 

Machine learning algorithms for predictive maintenance and quality control 
Machine learning has allowed computers to be able to predict, based on several known factors and past experiences, when a machine will need maintenance. In this way, maintenance departments can get ahead of the problem and tackle the issue before it occurs, decreasing downtime. This also helps to increase the lifespan of equipment which reduces costs in repairs or replacements. 

If you want to learn more about Machine Learning and its capabilities, read our full article here.

Also, discover 5 examples of this technology that you probably didn’t know about in our article here.