Generative AI in Corporate Innovation: The Pros, Cons, and Untapped Potential

Discover how generative AI impacts the corporate venture process from idea to concept, its trustworthiness and limitations, illustrated in a 4-day innovation sprint case study.

AI has made big waves in the last few months, with tools like ChatGPT, Copilot, Stable Diffusion, Midjourney, and more helping us boost productivity, speed and creativity in new and exciting ways. And there’s no sign of a slowdown. In a recent poll by Gartner:

  • 70% of organisations reported they were exploring new ways to leverage AI
  • 19% said they were already in pilot or production mode
  • By 2025, 35% of CROs aim to create a generative AI team in their GTM organisation

AI-powered tools can generate code, write engaging copy and turn your text prompts into dazzling illustrations in seconds - but can they be successfully leveraged for corporate venturing activities? To answer this question, we decided to test the assumption in our recent 4-day innovation sprint. Spoiler alert: the short answer is yes but with a few limitations.

Leveraging AI, we were able to generate over 200 ideas, visualise 75 of them and produce more than 4 new brand directions with unprecedented agility. To give you a better idea of how this works in practice, we’ve outlined our 4-day innovation sprint below, highlighting how the use of AI: 

  • Impacted our innovation funnel
  • Sped up the ideation process
  • Provided a helpful outside perspective

But before we get into that, let’s kick things off with some context.

The essential elements of idea development

Going from idea to viable concept is no easy task, often requiring various rounds of rigorous testing and pivots to get to that final successful iteration that customers love. While there is no one formula to get you there every single time, there are three essential elements of idea development you can’t succeed without: time, resources, and an outside perspective. Let’s take a closer look at each:


In the realm of idea generation, time is an integral component. The early stages of creating an idea may be relatively swift, fueled by bursts of creativity and brainstorming. However, as the idea starts navigating down the innovation funnel, the process increasingly becomes more rigorous and time-intensive, calling for meticulous research, repeated refinements, and a constant cycle of testing and iteration.


Resources are crucial in this process as well. At the heart of successful idea generation and validation lies a mix of essentials: access to wide-ranging data, cutting-edge technologies, robust internal support, and a pool of industry-specific expertise. These elements serve as the bedrock upon which ideas are not just formed but also validated and refined to their fullest potential.

An outside perspective

When you're deeply invested in a concept, it's easy to develop cognitive blind spots or unintentional biases. This is where an external view becomes invaluable, ensuring that ideas are not clouded by insularity. An outside perspective brings a much-needed fresh eye, offering invaluable insights that can streamline the validation process and aid in the refinement of the idea.

The role and trustworthiness of generative AI at each stage of the innovation funnel

The role of generative AI varies from stage to stage, with different degrees of trustworthiness throughout the innovation journey. To test exactly how trustworthy it is at each stage, we compared human results with AI results during a project. Here’s what we found out:

Idea generation and persona creation 

During idea generation and persona creation, AI rapidly produces comprehensive lists of ideas and personas. However, humans are still needed to select the most pertinent ideas and decipher persona motivations.

Pains and gains mapping 

AI can efficiently enumerate possible gains and pains, but human verification is needed to verify the accuracy and relevance.

Concept visualisation 

AI truly excels in this domain, conjuring up captivating visuals and thus earning complete trust. Nonetheless, even in this scenario, the final approval still requires a human touch. Here are a few examples of visuals created in MidJourney:

Business model proposition 

AI is skilled at suggesting viable business models but falls short in discerning key stakeholders and discarding impractical ones, a job best suited for humans.

Assumption mapping 

At this stage, trust dips further. AI can pinpoint the riskiest assumptions but requires human intervention to further define them.

Validation roadmap creation 

Here, AI demonstrates a reasonable level of trust, capable of drafting a plan based on risky assumptions. However, it has to be based on very well-thought-out assumption mapping (which is something AI can't do). In addition, the responsibility of finalising and executing the plan still rests with humans.

Concept selection 

AI's job in choosing concepts is limited. For example, it can be used to test concepts for a specific persona, simulating a reaction to the concept. While it can provide some interesting perspectives, when it comes to the final choice (which involves a broader set of factors), humans do it best.

Our journey to revitalise a beverage brand using AI tools

In a market brimming with competition, a leading client in the beverage industry wanted to foster a stronger connection with its target audience by revitalising its brand. The project involved identifying and activating passion points that align with the brand’s vision and purpose. We kickstarted the track with a 4-day innovation sprint aimed at:

  • Generating a broad range of ideas that cater to specific personas.
  • Visualising the ideas through branded and unbranded content
  • Validating the ideas through various tools and persona testing
  • Ending up with actionable recommendations and next steps

Here’s a breakdown of the process:

Day 1: Ideation

The team used various AI tools to generate new ideas, designing prompts, to help generate a plethora of innovative ideas that catered to specific personas. By the end of day 1, we had produced an impressive list of over 100 ideas.

Day 2: Visualisation

On day two, we used advanced AI-powered graphic tools to visualise the ideas. This involved the creation of both branded and unbranded visuals, using mid-journey. The visualisation process was crucial because it enabled us to better conceptualise how each idea could be brought to life.

Day 3: Evaluation and validation

With the visualisations in place, we shifted our focus to evaluating and validating the ideas. A variety of survey tools were used to gather immediate feedback. In addition, the ideas were tested against specific personas using AI. This helped us understand how different consumer segments might react to each idea and provided a basis for refinements. 

Day 4: Final thoughts and recommendations

On the last day, we synthesised all the data and insights gathered during the sprint. The best ideas were identified, and the team provided a clear set of recommendations for implementation. 

The pros and cons of AI in innovation

AI is a valuable asset throughout the innovation process, delivering much-needed content at each stage. When used correctly, it has the potential to double productivity. However, the accuracy of AI-generated content may fluctuate from stage to stage and human intervention remains indispensable for effective validation and the execution of the plan. 

What is it missing? In a nutshell, deep understanding. While AI is capable of generating contextually suitable responses based on patterns in its training data, it lacks a profound understanding of the content. This shortcoming can be particularly challenging when generating ideas related to non-Western markets, as the accuracy may be compromised.

Moreover, AI usage also raises ethical concerns. The AI can unknowingly perpetuate biases present in its training data, leading to outputs that might not be universally applicable or fair. These lessons highlight the need for balance. Recognising the boundaries of AI is key to getting the most from it during the innovation process.

Embracing AI for Enhancing Innovation

Generative AI’s potential for catalysing and speeding up new ideas in businesses is truly exciting. But, the technology is still in its early stages, and companies should be aware of the potential risks and disadvantages. 

If you're interested in exploring new ways AI can further your innovation strategy, we'd love to partner with you. 


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