In the ongoing debate surrounding artificial intelligence and Innovation Management, the question of whether it is inherently good or bad has become less relevant. The genuine concern lies in how we, as humans, choose to utilize this remarkable technology. Unfortunately, many entities pursuing profit make bold claims about AI without the necessary knowledge. Having had the privilege of working with Artificial Intelligence and Innovation Management since the early 90s, I can’t help but feel a sense of dread as I read countless misguided articles.

Artificial Intelligence and Innovation Management

To begin, it is essential to acknowledge the value of generative models and Large Language Models. However, their potential can only be fully realized in the hands of capable individuals with sound judgment and creativity driving innovation. Additionally, it is crucial to recognize that anyone, including those with misguided intentions, can react to signs and information, including AI. The history of wars instigated by signs or prophecies is a stark reminder of the dangers of misinterpretation. So the potential danger lies in people, not in technology or innovation.

Given these insights, offering valuable advice on what to do and what to avoid regarding AI is imperative.

Let’s start with the do’s working with Artificial Intelligence and Innovation Management

• Firstly, utilize generative models combined with data for social studies. These models are based on people’s perceptions and arguments, much like the public discourse of the ancient Greek Akademia or the vibrant exchanges on the internet. By leveraging this approach, we can gain deeper insights into societal dynamics and foster meaningful discussions.
• Secondly, Harness the power of generative models and large language models to rewrite and improve texts. This iterative process of generating and refining alternative versions can significantly enhance the speed and quality of writing across different languages. This, in turn, promotes integration and understanding among diverse cultures and linguistic communities.
• Thirdly, embrace the potential for diversity. AI and Large Language Models can drive inclusivity and diversity by providing individuals with dyslexia, dyscalculia, ADHD, and related diagnoses powerful tools to express their creativity without fear of judgment. This inclusivity has the potential to unlock the next wave of innovation needed to address urgent global challenges, such as climate change.

Now, let’s turn our attention to the don’ts

• Do not rely solely on Generative AI for creativity. While these models can offer assistance, they lack inherent creativity and should not replace genuine human imagination and innovation.
• Do not depend on AI to predict future trends and key drivers. Human biases inevitably influence AI, just as they have throughout history. Therefore, exercising caution and considering a broader range of perspectives is crucial when analyzing trends.
• Do not assume that AI will always provide accurate facts. Input from individuals can be flawed, systems can be biased or manipulated, and facts can be intentionally distorted or omitted. Human judgment and critical thinking are essential for fact-checking and verification.
• Do not serve your customers directly without a human filter. Customers are the lifeblood of any business, and it is vital to treat them with respect. Avoid underestimating their intelligence and the value of human interaction, as personalized experiences and genuine connections can significantly impact them.

By adhering to these guidelines, we can navigate the realm of AI responsibly. We can harness its potential for social studies, enhance the quality of writing, and foster inclusivity. However, it is crucial to remember that AI is not a substitute for human creativity, accurate trend analysis, or infallible fact-checking. Always prioritize the human touch and the needs of your customers.

In conclusion, let us embrace the power of AI while remaining vigilant. With the right approach, AI can propel us toward a future where we effectively address global challenges and create a more inclusive and prosperous society.

Also, read our article about AI strategy and the seven steps:

1. Define the rationale behind AI implementation.
2. Identify external drivers influencing your organization.
3. Analyze your organization’s needs based on these drivers.
4. Assess your readiness for radical innovation.
5. Link needs and readiness, defining clear KPIs for monitoring progress.
6. Set additional KPIs for managing your innovation portfolio across horizons.
7. Align AI strategy with IT, corporate, HR, and marketing strategies.

If you are curious to learn more about tech scouting, a great foundation to understand more, have a look at our courses here.