While generative AI is the hottest trend in technology today, it's not a flash in the pan or an overnight craze. Rather, these algorithms have existed for decades. Researchers have been developing some form of chatbot since the 1960s, and the development of large language models has occurred throughout the intervening years.

If you've ever interacted with a customer service bot, you've seen evidence of generative AI's commercial potential. Using natural language processing, machine learning or both, these bots generate relevant responses to users' queries. Now, this concept is entering new realms.

Generative AI is going through a period of both growth and increasing recognition. Tools such as ChatGPT have given the technology its greatest pop cultural breakthrough to date, and developers have poured increasing amounts of resources into creating more powerful and useful algorithms.

Now, as a product designer and developer, you may be intently interested in what these generative AI offerings can do for your workflow. There are actually two distinct branches of evolution you can latch onto:

• Designers and developers can integrate generative AI tools into their day-to-day processes, building efficiency through automation.
• Generative AI can be integrated into digital products to improve productivity.

Whether your company can incorporate one of these approaches or both, today is an exciting time to be involved in product design. By keeping an eye on generative AI models and their evolution, you can stay at the cutting edge of your field.

Generative AI in product design: as a productivity tool

What would the product design process look like with AI as a key collaborator? In the years ahead, ambitious teams will find out as they uncover new ways to integrate algorithms into their workflows.

The short answer is that AI-aided product design will likely not change drastically overnight. Asking ChatGPT to come up with a foolproof new product idea from whole cloth is not a formula for success because algorithms rely on humans for input and direction. It's still the people on a product design team who will determine the relative success of the effort.

With that said, what generative AI can do is introduce new efficiencies into everyday processes. Automating steps that are slow, time-consuming and labor-intensive has long been a good way to tune up a workflow, and generative AI may take this concept to new levels.

Algorithms may find their greatest productivity-tool use case as powerful digital assistants. When a designer encounters an issue that would have previously slowed them down, they can run the problem by the generative AI system.

Equipped with these tools, product designers can:

• Generate more new product ideas in less time and iterate quickly, using generative AI as an aid with brainstorming.
• Improve the user testing process, generating scenarios and helping teams be more comprehensive.
• Streamline workflows, automating any step when they would be forced to slow down and manually code standard or predictable features.

The assistance of generative AI may allow designers to smooth out their workflows, limiting the potential for human error while also saving them time. With busywork and repetitive tasks turned over to bots, employees will gain back more hours in their days to be more creative and idea-driven while also creating consistent, high-quality designs.

This AI-aided development process isn't as simple as "telling the algorithm to design an app," but organizations shouldn't ignore the potential for what generative AI really offers. Over the next few years, outcomes such as faster time to market, better collaboration and more sustainable development may empower design teams that make intelligent use of new technology.

Generative AI in product design: powering app features

Upon hearing that product designers are looking at generative AI algorithms, it's natural to think about how the technology could serve as a productivity tool. However, there is a second use for generative AI in the product sphere, which may prove even more empowering.

This is the use of generative AI to power software features, allowing app users to directly benefit from the power of these algorithms. Whether a company is developing an internal or consumer-facing product, it's worth considering whether there is a use case for generative AI.

Generative AI is so game-changing because it can work with many kinds of user input. When asked a question in conversational language, a text program like ChatGPT can synthesize a response, speaking to the user in an easy-to-comprehend form. The ability to work such simultaneously powerful and easy-to-use functionality into apps is worth investigating.

There is a hype encouraging businesses to integrate generative AI products. Companies may be tempted to "force it" and use the technology just so they can say they're using it. It's much better, however, to consider real situations users may face.

Would users of an app benefit from a bot being able to answer their questions or prompt them with the right way to phrase something? Could a generative algorithm become a compelling part of a help system, assisting users as they navigate the app?

Nearly every industry has at least some generative AI use cases because the general technology can be adapted to multiple kinds of input. Designers who find a valid role for these algorithms in their apps may thrive in the years ahead.

Generative AI limitations and misconceptions

Perhaps the most important part of navigating the generative AI landscape today is avoiding false assumptions about what the technology can and can't do. Designers and developers who become too enamored by hype or unrealistic promises may miss out on the genuine potential generative AI has for their workflows.

First, it's worth addressing the current limitations, challenges and issues associated with generative AI and large language models:

• The technology is only as good as its data set. Algorithms aren't truly "thinking" or coming up with novel ideas, but rather compositing responses out of the information they have access to. This means if the data is biased, the results will come out flawed.
• These systems have an unclear relationship with intellectual property rules. Can a business claim ownership of intellectual property designed with generative AI input? What happens if people oppose the collection and use of their data in large language models? Questions such as these have caused some companies to temporarily ban the use of generative AI.
• Perhaps the most prominent misalignment between expectations and reality comes from generative AI's need for human intervention. When users hear the term "AI," they may envision a bot that works independently. There is still a significant need for human input and oversight, however. Developers' success with generative AI hinges on their own skill with it.

Generative AI's power comes from its ability to analyze sets of data and come to conclusions. It can comb through large amounts of input faster than a person could, freeing up human employees to take the lead on creative ideation.

Leaving the actual process of creation to people rather than software is a necessary consideration because generative AI, for all its sophistication, is not creative. In the years ahead, employees can sharpen their ability to interact with the algorithms, giving them context and direction and harnessing their power to take some of the strain out of repetitive work.

Dipping into generative AI use with expert consultants

Companies that successfully integrate generative AI will be able to deliver high-quality software on a streamlined schedule. Having algorithms in place to automate the most programmatic and repetitive parts of the workflow is a great way to provide consistency with less strain. Everything from collaboration to turnaround time stands to benefit.

With that said, organizations shouldn't rush to infuse their whole process with new technology. In addition to the notable drawbacks and challenges associated with generative AI use today, there is simply no benefit in using algorithms just for the sake of doing it. Updates to workflows should be made with intention, helping companies target specific goals.

An expert consultation or design partnership can be a great opportunity to work with exciting new technologies, including generative AI. The experts who participate in these projects can show off how to integrate new best practices, but with a thoughtful and gradual approach. For example, if there is a compelling legal or functional reason to keep new algorithms out of the development process, these consultants will avoid it.

Transcenda's teams provide this level of intelligent, hands-on support, providing the exact type of collaboration needed to achieve an organization's aims, whether that means a limited or long-term engagement. New technology tools, including generative AI, can become a natural part of the product or workflow, applied because they make sense, not because they're trendy.