November 2023
Innovation in the Age of AI: Exploring the Potentials and Perils
<a href="" target="_self">Lauren Burger</a>
Lauren Burger

Senior Design Strategist

Innovation in the Age of AI: Exploring the Potentials and Perils

In the ever-evolving landscape of design and innovation, there’s a transformative force that has come on the scene. It stands ready to infuse creativity and inspiration into innovation teams, and at the same time has a mind that can understand and manipulate even the most complex datasets. It can take a seemingly disparate set of insights and extract meaningful patterns; it can help articulate a vision and bring it to life through imagery; and it can take near-flawless meeting notes, summarize them, and translate then them into dozens of different languages.

If this sounds like an addition you’d like to have on your team, you’re in luck – it’s accessible to any company, as long as you’re willing to dedicate the time to mastering the art of collaborating with it. We’re talking about none other than Artificial Intelligence (AI). AI has woven itself into the fabric of our industries, promising to revolutionize the way we approach creativity and problem-solving. From streamlining repetitive processes and analyzing trends, to generating novel ideas and optimizing designs, the potential of AI in innovation is boundless.

However, as we dive into this AI-driven era of innovation, we must also navigate the ethical and strategic complexities it presents. In this article, we’ll explore the intricate role of AI in the innovation process, revealing both beneficial applications, and critical considerations that can serve as a guide away from potential pitfalls. Join us on a journey that explores how AI can be a catalyst for innovation while remaining a responsible and strategic partner in the creative process.

Harmonizing Human and AI Expertise

In any innovation process, it’s essential to maintain a guiding principle when it comes to AI: it’s a powerful tool, but its true potential shines when thoughtfully integrated alongside human expertise. In this dynamic partnership, AI excels in certain areas – such as predictive analytics, automation of repetitive tasks, and generating many ideas in response to a user query – creating unprecedented efficiency and accuracy. However, it’s vital to acknowledge that while AI is valuable in many areas, there are tasks where it may produce answers that are not only incorrect but potentially misleading.

When embarking on an innovation journey, it’s our responsibility to lead a co-creative relationship, where the machine enhances human potential and vice versa. This positions AI as an enabler, not a replacement, ensuring we’re unlocking both a powerful and responsible collaborator in innovation. At Sundberg-Ferar, we’re already thoughtfully integrating AI tools into our innovation processes, using them to harness data-driven insights for inspiration, streamline research, enhance creative collaboration, and refine concepts.

AI in Research: From Inspiration to Assessment

In the innovation journey, research plays a pivotal role, influencing decisions from the earliest sparks of creativity to the final selection of solutions. Although AI can’t create insights out of thin air, it can offer indispensable support throughout the research process, enriching our understanding and enhancing decision-making. Here’s how AI can contribute to research at different stages of innovation:

  1. Efficient Online Data Collection: AI can automate data collection from diverse sources, like social media and product reviews. It then sifts through vast amounts of information, identifying trends, customer preferences, and emerging market needs.
  2. Transcribing Research Sessions: Machine learning algorithms, powered by natural language processing and speech recognition, can quickly transcribe audio recordings into written text from sessions like interviews, focus groups, or even just internal meetings. This can take place either in real-time or during post-session transcription.
  3. Text and Data Analysis: AI tools can distill the essence of data or textual content, such as the notes from research sessions mentioned above. These tools uncover themes, categorize sentiments (positive, neutral, or negative), and start identifying needs and challenges.

    This is an area where a human lens remains vital to elevate the value of the themes AI can identify. While these tools can offer a good starting place, outputs often require further refinement – such as identifying connections and interdependencies between themes or extracting further nuance – to provide truly insightful conclusions.
  4. Synthetic User Insights: Emerging AI tools are starting to be able to simulate user responses in online research. Researchers can input basic information about their target users to these tools, like age, profession, hobbies, relationship status, and even pets. AI-based “users” then assess various concepts, helping prioritize ideas and providing feedback for improvement.

    Results from these tools are promising but underscore how critical real humans are in providing a projectable assessment of concepts. Although able to replicate some classic human biases, these tools are still not able to integrate others. There’s also the big hurdle of predicting new insights, as opposed to just reproducing existing findings the tool was trained on.i
  5. Secondary Research Support: AI can help identify analogous industries and expand on knowledge gaps to help point researchers in new directions to investigate, ensuring they’re looking at a problem from a range of perspectives.

Sparking Creativity

One of the most exciting new applications of AI in innovation lies in its capacity to fuel creative idea generation and brainstorming. Today, AI is a driving force behind a wave of innovation tools, both in standalone generative platforms and as integrated assistants within familiar software we use daily. This technology is helping inspire a diverse range of solutions across various domains, from product design to programming code and beyond.

These AI-driven tools take various forms:

  1. Text-Based Creativity: Users can provide prompts or initial ideas, and AI responds with a diverse array of creative content and support. It can expand on existing concept descriptions, generate totally new ideas, or offer inspiration in the form of thematic topics or questions to fuel the imagination of human team members.
  2. Visual-Based Inspiration: AI can go beyond text and create a variety of visual outputs. This can include generating original images based on parameters the user inputs, exploring multiple styles based on a desired visual theme, visualizing concepts based on a written description, and even suggesting color palettes and combinations for a specific project.
  3. Collaboration Enhancement: AI’s role in the creative process isn’t limited to generating text and visuals. It can also support the effectiveness of collaborative sessions themselves. This includes real-time translation services to facilitate global teamwork, virtual meeting assistants that transcribe and summarize discussions, and project management tools that identify and allocate tasks.

At SF, we’ve found AI can be a unique creativity spark in our Ignition CoLABs – which is the method we use to bring people together for a collaborative workshop, fueled by inspirational insights, to ignite ideas and forge impactful solutions. In the leadup to a CoLAB session, AI can skillfully identify trends and explore emerging innovation domains. SF innovators ground those learnings with context relevant to our client’s goals and use this as part of the workshop fuel to generate creative energy. Then during the workshop itself, AI can contribute alongside human collaborators, creating hundreds of additional novel ideas in a matter of minutes, that in turn push divergent and innovative thinking on the human side.

Our team recently harnessed a variety of AI tools throughout the phases of a collaborative workshop. As is common during in-person workshops, participants generated numerous ideas on sticky notes.

Innovation in the Age of AI: Exploring the Potentials and Perils

Following the workshop, our team photographed the day’s work, and leveraged the Post-it® App to seamlessly transform these analog ideas into digital notes. Handwriting recognition technology further expedited this process by converting written concepts into typed text – what a time-saver! These digital notes were then seamlessly imported into our online collaborative workspace within Miro.

Innovation in the Age of AI: Exploring the Potentials and Perils

Once in Miro, we leveraged integrated generative AI tools to continue exploring divergent ideas, ensuring that the topic was looked at from a multitude of perspectives. We also used text analysis tools to help identify thematic keywords across the notes, which we ultimately used when identifying potential opportunity areas to pursue. This process not only made our team’s time more efficiently spent, but helped ensure we were exploring multiple angles of our goal with diverse perspectives, to take the team’s creativity to new levels.

Innovation in the Age of AI: Exploring the Potentials and Perils

Concept Maturation

Once the team has a set of promising ideas, AI can help develop and guide these concepts from their initial rough sketches or written descriptions, into well-defined concepts that each clearly explain an idea and its value. This can play a crucial role in assessing which direction to prioritize for development and where additional refinement is needed.

  1. Concept Statement Enhancement: AI can offer a helping hand in crafting and enhancing concept statements. It can generate initial statements or improve those already in development. This enhancement might involve refining language, enriching details, or crafting scenarios to make the statement more robust. It can also provide translation services to adapt statements for global markets, ensuring a broader reach for concepts.
  2. Concept Visualization: Building on the visual creativity capabilities explored earlier, AI can also assist in visualizing concepts. It can provide minor refinements to the design team’s imagery, such as optimizing layouts, color schemes, and visual elements. And in some cases, AI can even take on the entire visualization task, provided the user can articulate their vision effectively through prompts. This versatility ensures that your concepts are not only well-described but also visually compelling.

Managing the Risks of AI

The dynamic landscape of AI tools is transforming the way we learn, ideate, collaborate, and innovate. These tools are poised to drive new levels of efficiency, inspire fresh thinking, and propel creativity to new levels. But in the age of technology, the scale tips both ways. The flipside of all this potential is that there are several critical areas innovators must navigate to avoid the pitfalls still emerging on the AI horizon.

  1. Biases in Output: Human involvement in the data selection that trains AI can introduce biases that are subsequently automated and perpetuated. If the teams entering data lack diversity, this can lead to inherent biases in AI systems.

    A well-reported example of this is in facial recognition technology, which has been shown to be a relatively inaccurate biometric marker, especially among females, individuals of color, and those aged 18-30. Issues arise from the predominantly white and male photos used in standard training databases and the insight that most default camera settings aren’t optimized to capture darker skin tones.
  2. Privacy Concerns: AI systems often rely on vast datasets to train their algorithms and improve performance. This data can include personal and sensitive information such as names, addresses, financial data, and other personally identifiable information. When it comes to the world of new product and service innovation, there’s also the unique risk of accidentally sharing confidential trade secrets or business information by including this data when trying to get the most out of generative AI tools.

    The collection and processing of this sensitive information can raise concerns about how it is being used and who has access to it when fed into an AI platform – it may result in unintended exposure or misuse of data that should otherwise be private. Some best practices to mitigate these concerns include anonymizing data before using it in AI systems, limiting the data that is used to only that is absolutely necessary, and always using secure platforms and tools for collaboration.
  3. Protection of Creative Ownership: At the heart of generative AI is the collection and processing of existing text and images to understand patterns and relationships, which then allows these tools to intelligently create content informed by what has been learned. This act of referencing already-existing content raises questions and concerns about whether AI is already infringing on intellectual property in its training processes.

    On the flip side, this also raises questions about the true ownership of the content AI produces, and attributions that are owed. Legal issues surrounding the ability to copyright or patent AI-generated content are in the process of being defined now through ongoing court cases.
  4. Avoiding Overreliance on AI: It’s essential for users to discern AI’s competence and limitations. While AI excels in creative ideation, it may not be adept at complex problem solving. The boundaries of AI proficiency must be understood to prevent overreliance on AI-generated content and ideas.

    AI lacks the ability to fully understand the broader strategic goals and nuances of innovation. Human expertise is essential for setting the vision, defining objectives, and making high-level strategic decisions.” – ChatGPT-3.5

    At the same time, AI limitations may be difficult to spot. AI tools tend to confidently deliver answers, even when they might be inaccurate – known in the industry as “hallucinations” – which can mislead users if taken at face value.

As we continue to harness the power of AI in innovation, understanding these hazards and proactively addressing them will be pivotal in unlocking the benefits while minimizing risks. AI’s journey into innovation is still unfolding, and as stewards of this transformative technology, SF can help you tread thoughtfully to ensure it aligns with your company’s values and objectives.

To facilitate this, SF engages in client-by-client conversations, understanding unique perspectives, preferences, or even aversions regarding the use of AI tools in our collaborative efforts. This approach ensures that our AI-supported innovations align with your specific needs and interests.

But we’re not just here to provide insights; we’re here to empower you to take the next step. If you’re ready to embrace the transformative potential of AI in your innovation journey, reach out to us today. Our team believes in the power of AI to augment human creativity, streamline operations, and drive insights that help lead to breakthrough innovations. Our mission is to offer our clients a competitive edge by leveraging the synergy between human expertise and AI capabilities. The result? Faster, more efficient innovation, sharper market insights, and creative solutions that stand out in a dynamic landscape. With AI as our creative partner, we’re positioned to provide our clients with cutting-edge solutions and thoughtfully crafted innovations that set them apart in today’s dynamic markets. Contact us and let’s embark on this exciting journey together.


Innovation in the Age of AI: Exploring the Potentials and Perils


Lauren Burger

Senior Design Strategist

With a background emphasizing the importance of combining both human desirability with engineering feasibility in design, Lauren has a deep passion for understanding the needs of people and how they translate to a business growth strategy.

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