Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models

Question 1

What is “Bouschery, Sebastian G., Vera Blazevic, and Frank T. Piller (2023) “Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models” about?

Answer 1

This article addresses possible uses for these tools, concerns for job replacement, research opportunities, limitations, and managerial implications from combining AI & humans. AI abilities require us to reconsider many established NPD practices, including how good ideas are created, like using creativity techniques or other divergent thinking tools

Question 2

What are Transformer-based language models?

Answer 2

A type of AI designed to process and generate (natural) language in the form of text and can be used for various tasks such as machine translation and text summarization, insight extraction, or generating creative output.

Question 3

What does NLP stand for?

Answer 3

Natural language processing

Question 4

What are some uses for transformer-based language models?

Answer 4

Text summarization, sentiment analysis, and idea generation

Question 5

What is the conclusion from “Bouschery, Sebastian G., Vera Blazevic, and Frank T. Piller (2023) “Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models”?

Answer 5

TLMs hold transformative potential in NPD by enhancing problem and solution spaces through efficient data processing and insight generation. The authors recommend viewing TLMs as integral team members in hybrid intelligence frameworks, combining human creativity with AI’s processing power. This AI-augmented model calls for rethinking traditional innovation roles and processes, urging managers to leverage TLM capabilities thoughtfully and responsively.

Question 6

What is the “double diamond framework” in “Bouschery, Sebastian G., Vera Blazevic, and Frank T. Piller (2023) “Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models”?

Answer 6

The Double Diamond model traditionally organizes innovation into divergent (problem exploration) and convergent (solution finding) processes. The authors adapt this model to an AI-Augmented Double Diamond, where TLMs help expand the range of ideas and solutions teams can explore by enabling access to larger knowledge pools.

Question 7

What are the limitations and challenges of TLMs, in “Bouschery, Sebastian G., Vera Blazevic, and Frank T. Piller (2023) “Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models”?

Answer 7

1) Reliability issues (may generate inaccurate responses)
2) Bias in Training Data (since TLMs are trained on internet-sourced data they may inherit biases leading to oversight)
3) Data cutoff in training (TLMs rely on existing data, so knowledge may not include developments unless regularly retrained)

Question 8

What are the managerial implications of “Bouschery, Sebastian G., Vera Blazevic, and Frank T. Piller (2023) “Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models”?

Answer 8

1) AI-Augmentation Problem & Solution Spaces: managers can leverage TLMs to support large-scale processing in NPD, esp when it comes to UD, in order to expand problem and solution spaces, leading to superior innovation
2) Resource Reallocation - by automating time-intensive tasks, using TLMs allows teams to focus on high-value strategic tasks (vs grueling busy work)
3) Redefining Roles - some team members may shift to “prompt-engineering” roles, to optimize AI output for innovation tasks; which may streamline AI-human collaboration
4) Supporting Open Innovation - the potential to gather insights from outside the company can help facilitate broader knowledge sharing & collaboration

Question 9

What are the theoretical contributions of “Bouschery, Sebastian G., Vera Blazevic, and Frank T. Piller (2023) “Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models”?

Answer 9

1) Expansion of hybrid intelligence in innovation
2) revised double diamond framework
3) prompt engineering as a research domain

Question 10

What are the limitations and future research for “Bouschery, Sebastian G., Vera Blazevic, and Frank T. Piller (2023) “Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models”?

Answer 10

1) field-specific testing (test across more industries)
2) algorithm & human interaction dynamics (research how human inputs shape AI responses in real-time)
3) understanding bias & error mitigation (explore methods to screen AI-generated content for bias & misinformation)
4) regulatory & ethical considerations (ie. re: reliability, data privacy, & IP best practices need to be established)