Simplify Work With AI: Generative Tools for Productivity

AI tools such as generative text, chatbots, and data visualization are helping workers across industries streamline their workflows. But keeping up with all these new tools can be challenging.

One way to ensure you get the most out of these tools is by starting small. Try incorporating AI into low-stakes projects or tasks to gain comfort with the technology.
1. Generative Writing

Generative writing helps with the creation of content like copy, images and music. It can also be used for brainstorming and idea exploration. Happy Horse can even help with fact-checking and research. However, generative AI is not intended to replace human writers; it's a tool that supports productivity by helping people get more done without the time constraints of researching, planning and writing.

For example, Microsoft's generative AI writing tool Copilot works within several of its Office 365 tools to help users draft documents, create slideshows and compose emails with a simple prompt. It can pull information from sources such as OneNote, PowerPoint and Outlook and even make sense of complex data to create visualizations. It can also search the web for relevant information and create an outline to help users organize their thoughts. The McKinsey Global Institute estimates knowledge workers spend a fifth of their workweek searching for and gathering information; if generative AI could take on some of these tasks, it would free up people to focus on more value-add activities.

Using generative AI to generate content is becoming easier as vendors incorporate this technology into their existing tools and platforms. But it's important to have a clear policy on how to use these modules so they aren't used inappropriately (e.g., paraphrasing versus plagiarism). It's also a good idea to start with more constrained uses of generative AI and internally-focused tasks that are easy to monitor.

Software developers are using generative AI to document code, fix bugs and write boilerplate for new features and projects. Github Copilot, Tabnine and Magic AI are examples of generative AI tools that help developers work smarter and faster. In the future, generative AI will become embedded in every aspect of a developer workflow, from creating backend logic to providing a more curated experience when answering questions about the product.
2. Generative Design

Using the same technology that powers headline-grabbing AI art generators, generative design is a computer program that creates unique, outside-the-box solutions for architecture and interior layouts. It’s also a powerful tool for engineering and industrial manufacturing projects, such as the design of components that can reduce weight, improve performance, or fit in smaller spaces.

Generative design software generates a range of designs that meet specified constraints, and engineers can choose from these options to find the best solution. This reduces iteration time, which helps companies save on both human resources and time spent on costly mistakes.

For example, the automotive industry uses generative design to design lightweight components and fuel-efficient cars that are both functional and affordable. Generative design is also used in aerospace, where it can generate new, more compact designs to reduce the size and weight of aircraft.

However, generative design isn’t without its drawbacks. It requires expertise to set up, train, and work with generative algorithms, and it can be difficult to get them to function correctly. In the wrong hands, a generative design process can be clunky and inefficient, and designers might spend more time battling with the program than actually designing.

In addition, generative design may not understand a design brief in the same way that a human designer does. This can result in poor-quality output that doesn’t meet design goals.

While generative design works well with additive manufacturing (AM), it can also be used in traditional subtractive processes like CNC machining and casting. For more information about generative design, watch this webinar featuring Formlabs Product Marketing Lead Jennifer Milne. She’ll provide a simple overview of the concept and demonstrate how to use Fusion 360 to produce a lightweight bracket.
3. Generative Research

User interviews are a great way to gather qualitative feedback from your customers. They can help you uncover specific problems, determine potential solutions, and more. Generative research is a similar type of qualitative research that can be useful when exploring an unexplored problem space or identifying new product opportunities. It can also be used to validate a solution or test if a product meets its users’ needs.

It can be helpful to define your research goals, timeline, and resources before you start a generative research project. This will ensure that you’re using the right research method for your particular research needs. For example, if you’re looking to understand your customers’ lifestyles and what drives them to make purchase decisions, generative research may be the best approach.

You can use generative research to collect a wide variety of data about your customers and their behaviors, and then organize it into well-defined categories. This will make it easier to find and access the data you need when writing reports, articles, and other types of content. This can be particularly useful when researching a topic with numerous sources, or when working with data that isn’t readily available in one format.

Generative AI tools can help simplify your work by automating repetitive, manual tasks. For example, generative AI for software development can automate routine tasks like auto-filling standard functions, completing coding statements as the developer is typing, and documenting code functionality in a given standard format. This frees developers to focus on other business challenges and fast-track new software capabilities. These kinds of generative AI tools can be used to speed up the development process without sacrificing quality.
4. Generative Scheduling

The promise of AI is that it can eliminate monotonous tasks to allow workers to focus on more interesting and meaningful work. However, concerns remain that it could replace jobs entirely—which means reskilling and adjusting to new working habits is vital. This is where generative tools can be especially helpful.

Rather than using deterministic algorithms—fixed rules built by developers—generative AI tools use a combination of data and the underlying model to create different outcomes. This makes generative AI the most versatile of all AI tools for productivity, with the power to create new content based on existing information.

For example, a machine learning-powered tool called Viva Sales uses generative AI to help salespeople land more deals. This includes generating tailored customer emails, scheduling 1:1s with interested prospects, and even providing insights about customers to give salespeople the context they need to make informed decisions.

This type of generative AI is also used to improve processes for other types of businesses. For example, a construction optioneering tool that utilizes generative AI, like ALICE Technologies, offers multiple schedule possibilities and “what-if” scenarios for construction projects to reduce time and labor costs.

Another great way generative AI can simplify work is through chatbots that assist with customer service and other repetitive tasks. This frees up human resources to focus on more important, strategic work and helps drive business growth. AI-powered chatbots can also be used for meeting scheduling, sending reminders, and recording and sharing meeting minutes—all of which increase productivity and help prevent misunderstandings or missed deadlines. In addition, generative AI can be used to generate a variety of reports, including progress and KPIs. This helps teams stay on track with goals and objectives, and eliminates the need for manual data entry and manual report creation.
5. Generative Reporting

Generative AI tools generate code, text and images in response to a variety of prompts. They can write speeches in a specific tone, summarize research or assess legal documents quickly and efficiently. They can also create artworks, such as realistic video game screenshots or musical compositions, and aid complex design processes, such as generating programming codes or designing molecules for new drugs.

The rapid rise of generative AI tools has unleashed a torrent of experimentation, creativity and potential productivity gains for knowledge workers. But as vendors develop more sophisticated models and make them easier to use, organizations need to carefully consider how they might implement generative AI in their workflows. Gartner recommends connecting use cases to KPIs to ensure that any projects that incorporate generative AI improve operational efficiency or deliver net new revenue or better experiences for customers.

It’s also important to be realistic about how generative AI will impact knowledge work. Some tasks, such as postsecondary English language and literature teachers’ detailed work activities preparing tests and evaluating student papers, might be best served by generative AI, allowing them to spend more time on teaching. Others, like a property manager’s monthly or quarterly reporting on occupancy rates and rental prices, may be more easily automated by generative AI.

Finally, organizations should be sure to provide employees with training on how to use generative AI tools. They should explain that the results of generative AI can be flawed and may have factual errors. And they should emphasize that it’s essential to double-check AI-generated text, images and content before presenting it directly to customers. Companies should also consider setting up low code automation workflows that can include modules from GitHub Copilot, ChatGPT and other generative AI APIs so employees can use them without needing to know the specific code or algorithm used.

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Pub: 16 May 2026 07:01 UTC

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