AI productivity

AI productivity refers to the use of artificial intelligence (AI) to enhance efficiency and effectiveness in various tasks and processes across industries.

This can include automating routine tasks, analyzing data quickly, optimizing workflows and providing insights that help in decision-making through the integration of AI tools. New technology advancements, like generative AI are changing the landscape for AI and its role in the workplace and overall productivity gains.

The potential for AI is still highly anticipated as up to 300 million full-time jobs have the potential to be replaced by these new technologies, according to a report from Goldman Sachs.¹

For individuals and organizations, AI tools can streamline operations, reduce errors, and free up team members time for more strategic activities. Examples of AI features include the use of AI for project management, customer service automation, data analysis and original content creation. Generative pretrained transformer (GPT) is a chatbot that uses AI and is built around large language models (LLMs). They are also an increasingly important application of machine learning (ML).

The goal of AI productivity is to improve output and outcomes while minimizing resource expenditure and ultimately boosting the user experience. A study conducted by the National Bureau of Economic Research backs this, finding that access to AI assistance like GPT increased productivity of customer support agents by 14%.²

The introduction of AI productivity tools in the rapidly evolving world has changed how businesses are run and how people perform everyday tasks. The AI movement isn’t just about getting things done faster, it’s about being smarter and working more efficiently. What makes up most AI productivity are AI productivity tools, which are software applications that use AI to help individuals and businesses complete tasks. These AI productivity tools come in web-based and in app form. By using machine learning and natural language processing to do things like automating citations, working from prebuilt templates and writing code.

These types of software applications range in uses and complexity. Some are smart assistants that can sort through emails in just a few clicks. While other solutions use algorithms and metrics to predict the code that needs to be written or give tips to complete tasks. Some of the most popular tools used in business today are as follows.

1. Grammarly

2. Notion

3. ChatGPT

4. Claude

5. Asana

6. Otter.ai

7. watsonx Assistant

8. watsonx Orchestrate

9. Midjourney

10. watsonx Code Assistant

11. Microsoft Copilot

Grammarly is an AI cloud-based writing assistant that is used for grammar optimization, punctuation, spelling, among other writing aspects. The tool can be used for anyone looking to improve copywriting, long-form writing or general everyday writing.

Notion, similar to Grammarly, is a writing and note-taking tool that recently introduced its AI version. Notion AI is a collection of AI-powered tools that can autofill summaries, answer questions, and translate words into multiple languages.

ChatGPT was created by OpenAI, based on GPT-4 architecture and is trained on large amounts of text data to assist in writing essays, answering questions, critiquing writing and more. The premium version of ChatGPT can even do image generation and voice inputs.

Claude is another AI assistant that can summarize meetings, answer questions, and write code. It’s powered by LLMs and is another popular productivity app used by individuals today to assist in writing social media posts, such as Linkedin posts or Instagram captions.

Asana is a project management tool that helps organizations manage tasks and can integrate with multiple apps, such as Microsoft teams, Gmail, iOS and Outlook. Asana AI uses AI to automate tasks and create summaries, saving teams time and money.

Otter.ai is a transcription tool that summarizes recorded calls and helps users transcribe conversations from speech to text in real time.

Watsonx Assistant™ is a conversational AI solution that empowers employees within an organization to build AI agents and AI chatbots. The tool can be integrated with multiple apps and designed for nontechnical builders.

IBM® watsonx Orchestrate™ is a generative AI and automation solution that can automate tasks and simplify complex processes. The tool offers prebuilt apps, skills and assistants to help members of an organization perform tasks.

Midjourney is an AI-image generation tool that creates visuals from text prompts. It’s used by artists and designers to assist in creating unique work.

watsonx™ Code Assistant™ uses gen AI to generate net new code and translate code from one language to another or refactor legacy code. The tool helps developers and IT operators speed up application modernization efforts.

Microsoft Copilot is an AI-powered tool that uses LLMs and the organization’s data to help users with productivity and creativity. Copilot can suggest new ideas and automate tasks, such as email writing and summarization.

AI productivity tools that are revolutionizing the approach to tasks from brainstorming to customer support. These major shifts in technology are enabling teams to generate ideas and resolve issues more efficiently. By using advanced algorithms, these tools help reduce the perplexity often associated with complex decision-making processes, allowing for clearer insights and better outcomes.

The integration of AI into everyday workflows can significantly boost productivity, transforming how individuals and team members collaborate. Some key benefits include:

1. Enhanced time savings
2. Increased efficiency 
3. Enhanced decision-making 
4. Improved accuracy 
5. Better personalization
6. More scalability

AI can automate repetitive tasks, freeing up time for employees to focus on more strategic initiative and creative brainstorming. An example comes from the 2023 Forrester Consulting Total Economic Impact study, which found a 30% reduction in interaction handle time for chatbot-augmented service agents with IBM Watson Assistant.³ The improvement is valued at USD 2.4 million over a three-year period, according to the study.

The AI tools streamline workflows, allowing teams to manage projects and tasks more effectively, which leads to quicker project completion. Separately, the added efficiency and reduction of errors can lead to significant cost savings over time, allowing for more effective resource allocation. An example of efficiency is IBM watsonx Orchestrate, specifically within the procurement world. watsonx Orchestrate uses several solutions for procurement, including Ask Procurement, contract management and procure to pay and order management.

AI analyzes large datasets, providing insights that help organizations make informed decisions based on real-time information. It can also enhance communication among team members, fostering a more collaborative environment that encourages idea sharing and teamwork. The watsonx Orchestrate solution is also an example of improved decision-making. With the watsonx Orchestrate capabilities, teams can automate tasks and simplify complex processes, which will save teams time and resources.

AI tools can minimize human error in data entry and content generation and help ensure higher-quality outputs. The technology can reduce the need for revisions and foster more efficient task management. watsonx Code Assistant and the IBM Chief Information Officer (CIO) organization is a great example of what accuracy can do. With watsonx Code Assistant for Red Hat Ansible Lightspeed, 60% of Ansible Playbook content was automatically generated by watsonx Code Assistant.

Many of the AI tools on the market, including generative AI tools, harness advanced technology to adapt to individual preferences, offering customized recommendations that improve the user experience. ChatGPT is a good example of personalization as it recently included a personalization feature, aimed at preventing users from having to repeat common instructions from one task to another.⁴

AI solutions can adapt and grow with an organization, accommodating increased workloads while maintaining high productivity levels and mitigating time-consuming tasks. An example of a tool that is highly scalable is Notion AI. The solution has seen such data growth that they expanded their database infrastructure to a more complex sharded architecture.⁵ The solution “maintained a total of 480 logical shards while helping ensure long-term scalable data management and retrieval capabilities,” said Notion in a post on its website.

AI productivity doesn’t come without its challenges. But with those challenges there are possible solutions.

Therefore, AI tools often require access to large amounts of data, and concerns about data privacy and security become paramount. Organizations must help ensure that sensitive information is protected, which can complicate AI implementation. Compliance with regulations, such as GDPR, adds an additional layer of complexity.

A potential solution is to create an ethical framework that has clear security policies and standards. Organizations should only be using data that is needed to create AI and help ensure it is securely handled and managed.

Many organizations struggle to integrate AI productivity tools with their existing systems and workflows. This can lead to disruption and inefficiencies during the transition period. Without seamless integration, the potential benefits of AI might not be fully realized.

A way to mitigate any integration issues is to establish common standards that are consistent throughout the organization. Organizations should also implement data governance frameworks and use APIs to connect data and handle data transformation.

AI systems can inadvertently perpetuate biases present in the training data, leading to skewed outcomes and unfair practices. This bias can affect decision-making processes, particularly in areas like hiring and customer service. Organizations must actively work to identify and mitigate these biases to help ensure fair and equitable use of AI.

A possible solution to this challenge is to have diverse data that represents a wide range of individuals from all different backgrounds. Developers should be reviewing the data carefully and not inputting too much, as it overwhelms and can potentially make the model more biased.

Employees might be resistant to adopting AI tools due to fear of job displacement or discomfort with new technology. This cultural resistance has the potential to hinder the successful implementation of AI productivity solutions. Therefore, organizations need to invest in training and change management strategies to encourage acceptance and engagement with AI technologies.

An approach to mitigate this challenge starts with organization leaders creating a culture of being open to change and open to new ideas. The top-level executives need to communicate these changes early and often and listen to employees opinions in the workspace.

AI solutions are and will continue to shape the way businesses are run. The economic benefits from AI are becoming more apparent and executives are understanding the potential of these new advancements. A recent report from the IBM Institute for Business Value found executives report using a range of transformative tools, including AI and automation technologies to better their workflows and deliver more efficient insights.⁶

Over the next two years, they expect a significant increase in extreme automation, powered by AI and machine learning, according to the executives interviewed in the survey. They also expect these digital labor-enabled advances to increase by 20% in the next two years.

Customer service: Organizations can use AI productivity tools to analyze customer calls and automate responses to more repetitive questions. These AI solutions can increase customer service productivity by providing 24/7 support for customers and personalize the customer experience by analyzing customer behaviors and personalize services.

Human resources: Generative AI tools are being used across industries to enhance human resource capabilities, such as recruitment and performance management processes. HR leaders can use AI to measure employee engagement by analyzing survey data and to parse through resumes when seeking job applicants.

Content generation: AI tools that can create written or visual content can be useful to organizations seeking to keep their brand voice consistent. AI software, if given specific and consistent prompts, should be able to produce content that is aligned throughout the entire organization no matter which department it is being produced by.

Task automation: One of the biggest use cases for AI productivity tools is in task automation across industries. No matter what the business is or what goals that it has in mind, there are likely mundane tasks that are taking employees far too much time. Which is where task automation comes into the fold and AI tools can take the burden from employees so they can work on more pertinent tasks.

Data analysis and reporting: AI solutions can enhance data analysis and reporting by automating the extraction of large datasets, saving developers valuable time and resources. It can identify trends and patterns that might not be immediately apparent, providing deeper insights for informed decision-making. In addition, AI solutions can generate comprehensive reports in real-time, allowing stakeholders to access up-to-date information quickly. This not only boosts productivity, but also improves accuracy, saves costs and reduces human error in data interpretation.

Research: AI solutions can streamline the research process by quickly analyzing vast amounts of literature and data, allowing researchers to focus on more important tasks. It can assist in identifying relevant studies and extracting key findings, significantly reducing the time spent on manual searches. Separately, AI algorithms can generate hypotheses and predict outcomes based on existing data, fostering an inclusive and innovative approach to problem-solving.

Project management: AI-based tools can transform project management by automating routine tasks, including scheduling, resource allocation and progress tracking, which can lead to greater efficiency day-to-day and year-over-year. It can analyze project data to identify potential risks and bottlenecks, enabling proactive decision making to keep projects on track. By formulating smooth workflows and enhancing visibility, AI can help project managers optimize performance and achieve project goals more effectively.