The Impact of AI on Innovation Teams

Adapt or Fall Behind

Incorporating AI’s power into innovation practices is not just a competitive advantage but a necessity.

Vincent says, “The question isn’t whether AI will replace innovation teams; it’s how those teams will use AI to amplify their impact.”

Businesses that fail to integrate AI into their workflows risk falling behind as their competitors automate, optimize, and innovate at an unprecedented pace. “We’re in that spectrum of disruption. At the bottom end are people who still don’t know what ChatGPT is. At the top end are companies where AI is completely changing business models,” Vincent notes.

Jan and his team transformed their consultancy into an AI-powered venture-building platform in just over a year. He bluntly describes the shift from the consultant perspective: “We proved the hypothesis last September. Now, instead of us doing the work, AI does it, and it does it better. The traditional consultant model is becoming obsolete,” he adds. We designed our AI agents to be highly adaptable, allowing us to quickly add new use cases without rebuilding from scratch. This flexibility is what makes AI a game-changer. It can continuously evolve to meet new challenges.”

However, many corporations remain hesitant, either in denial about the urgency of AI adoption or lost on where to start. Concerns about job security, governance, and potential risks lead to hesitancy. “A lot of companies are still in a wait-and-see mode, but they don’t realize they’re already falling behind,” says Vincent. “Those who hesitate will find themselves playing catch-up in a world where AI-driven innovation is the norm.” He describes this early skepticism as a common reaction that is rapidly fading. “There was a shell shock at first, but we’ve seen a fundamental shift now. There’s no escaping AI, and companies know they need a plan for integrating it.”

Jan points out that readiness is about mindset. “We talk to executives who know AI is important, but they still treat it as an experiment rather than a transformation. That hesitation is costing them.”

Natasha sees AI as a strategic partner. “The more we learn to work with it, the more powerful our innovation efforts will become.” She encourages innovators to embrace AI as a transformational tool.

“Instead of traditional validation processes, we now have AI-powered simulations that allow us to predict and test concepts in ways that weren’t feasible before. That’s what makes AI transformational. It’s an entirely new way of thinking about innovation.”

The Changing Functions of Innovation Teams

One of the biggest concerns for corporate innovators is how AI is changing the structure and function of their jobs. Will it simply redefine their roles or make them redundant? Natasha highlights two key impacts.

“Companies want to do things cheaper, better, faster, so they focus on efficiency gains. But then there’s the deeper shift, which is about doing things that were previously impossible. It’s not just about automating what we already do but about completely rethinking the innovation process itself.”

She stresses that businesses that use AI merely for optimization are missing its broader potential. “The real value is in how AI allows teams to rethink the innovation process itself, moving away from traditional validation to AI-driven simulations that accelerate decision-making.”

According to Vincent, the transformation is already in full swing. “Companies that embrace AI aren’t reducing their innovation teams; they’re evolving them. The focus is shifting from manual execution to high-level orchestration of AI-driven processes.” Instead of spending weeks on research and validation, innovators now use AI to accelerate those processes and free up time for strategic thinking. “Businesses have moved from skepticism to acceptance, and now they’re asking the real questions like where AI fits into their long-term strategy and how to make it a competitive advantage.”

Jan offers concrete examples: “We automated the early-stage venture-building process, reducing months of work to hours. But that doesn’t mean people are out of a job. Instead, they now focus on shaping AI strategies, interpreting results, and making informed decisions.” He gives an example of AI-driven landing pages, which are automatically created, optimized, and tested in real-time. “We have over 70% success rate on these AI-generated ideas in terms of how they perform against what previously was human-powered or consultant-powered,” he shares.

Instead of relying on time-consuming manual efforts to build and refine landing pages, AI instantly generates variations, analyzes engagement, and identifies the most effective design. “AI tests them, optimizes them, and refines them based on real user behavior,” Jan outlines.

This example demonstrates that AI is accelerating existing workflows and fundamentally reshaping how businesses validate new concepts. The ability to generate real-time market feedback without extensive human intervention reduces risk, increases speed, and significantly lowers costs, making AI an incredibly valuable tool for innovation teams.

“The real challenge isn’t just building these AI tools,” says Natasha. “It’s about orchestrating them across the entire organization to drive real business value.”

Jan, Natasha, and Vincent agree that innovation teams must position themselves as strategic architects, ensuring that AI-driven innovation efforts align with business goals and market realities. Natasha adds, “The innovators who thrive in an AI-driven world will be those who learn how to integrate AI effectively rather than resisting it. It’s about partnering with AI, not competing against it.”

Balancing Automation and Human Oversight

While automation is increasing, AI still needs human oversight. Vincent discusses the spectrum of AI’s pervasiveness. “Right now, we’re somewhere in the middle of that spectrum. Some teams are still figuring out how to use AI to streamline tasks, while others are pushing toward full automation,” he says. “Human decisions are still crucial, but there will be a point where AI can autonomously handle most innovation workflows.”

Jan emphasizes that some aspects of traditional innovation processes, like personas, are more for human reassurance than actual necessity. “People get hung up on personas, but AI doesn’t need them like humans do,” he says. “They’re useful for traceability and communication, but AI can generate and test ideas without them.”

The key is to design AI systems that can adapt and improve without constant manual input and to know when to step in to guide them toward the right strategic direction. “At the end of the day, you still need people to shape the strategy,” says Vincent. “AI can execute, but humans need to decide where to aim it.”

Overcoming Resistance

As AI assumes more responsibility, organizations must overcome significant cultural and operational resistance to realize its full potential. Despite the clear benefits, many companies remain resistant to change. Whether due to fear of job displacement, lack of technical expertise, or internal bureaucracy, hesitation is a significant barrier to AI adoption in the corporate world.

Vincent acknowledges this blocker: “Many teams see AI as a threat rather than an opportunity. In reality, those who use AI strategically will have a huge advantage over those who don’t.”

Jan suggests a practical approach for companies struggling with AI adoption.

“Start with small, low-risk AI experiments. Prove value quickly, and then scale up. AI must become an integrated component of strategic innovation, not just a tool for incremental efficiency.”

Natasha emphasizes the role of leadership in driving AI adoption: “Executives need to set the tone. If leadership embraces AI and provides clear direction, the rest of the organization will follow.” Vincent expands on this, noting the weight of leadership decisions: “CEOs have their reputations on the line with how they treat their companies and their employees and how capitalistic they want to be with these efficiency gains. Where they fall on that spectrum will significantly impact their brand image.”

Rethinking Data Management

One of the significant concerns corporations have around AI adoption is data security and compliance. Companies hesitate to integrate AI due to fears of mishandling proprietary information, but as Vincent points out, these concerns are not new. “Think about Google,” he says. “Companies have been trusting Google’s search algorithms and data processing for years, often feeding sensitive business information into Google’s ecosystem without a second thought. Yet, there’s a sudden hesitation when it comes to AI and large language models.”

Vincent believes this won’t be an issue for long and emphasizes that AI security and compliance hurdles are being solved rapidly. “Legal teams are catching up. The major AI providers are implementing robust security measures to ensure enterprise compliance. The hesitation we see now is temporary. Soon, AI will be as seamlessly integrated as cloud computing and SaaS platforms.”

Jan describes how fear of data sharing initially slows adoption, but once executives see tangible results, companies prioritize AI integration and push past compliance barriers. “The first reaction from companies is always, ‘Can we use our data with AI?’ The answer is yes, but they respond, ‘We can’t give you our data because of our policy.’” To this, Jan typically responds, “Okay, fine. Let me show you on public data that this works.”

Jan recalls a case with a Fortune 500 company where they presented the public data results directly to the CEO. “The CEO saw this and said, ‘Obviously, everything has to run through this. Why would we do it any other way?’ Suddenly, security and compliance teams were told to figure out whatever was necessary to make it work. The hesitation was gone overnight.”

For organizations unsure of where to start, Natasha advises a pragmatic approach. “The key is not to wait until every question is answered. Begin with controlled implementations using internal, non-sensitive data. Once teams see the value AI delivers, the concerns start to fade, and broader adoption becomes inevitable.”

Preparing for an AI-Powered Future

We shouldn’t make the mistake of thinking AI is a passing trend. It’s a fundamental shift in how innovation happens. So, how can corporate innovation professionals prepare for what’s next?

Jan, Vincent, and Natasha believe that while AI is transforming innovation jobs, it is not making humans obsolete but redefining their roles. AI automates repetitive tasks, accelerates processes, and even outperforms human-led research in some areas. But at its core, it is a tool for augmentation, not replacement.

Jan suggests focusing on AI fluency. “You don’t need to be a data scientist, but you do need to understand how AI works and how to apply it strategically.”

Vincent advises companies to create a roadmap for integration. “Think beyond efficiency. AI can help you do things that were previously impossible. Define where AI fits into your broader innovation strategy and build from there.”

Natasha encourages innovators to remain adaptable to the inevitable increase of AI automation.

“The best innovators will be those who remain open to change and continuously refine their approach. We need to think about the future that we want to create. Where are human beings most important? There will be a role for people but we need to be intentional about where that role is.”

The future of AI-powered innovation and innovation teams is already here. Will you adapt or be left behind?