Many leaders, from founders and CEOs to product heads and board members, often face a shared frustration: they see clear, tangible benefits in their AI innovations, yet the market may not reciprocate their enthusiasm or may be hesitant to adopt them. Common barriers such as cost, resistance to change, and perceived risks frequently obstruct the path.
However, a critical issue that often goes unnoticed, particularly in the realms of AI and generative AI, is the gap between the perceived value of an innovation within a specific context and its actual applicability or impact in the real world. An innovation might promise significant enhancements in quality, cost, or efficiency, but its real-world effect in the intended setting could be minimal or even nonexistent.
🆕Creating a new solution DOES NOT GUARANTEE that your customer will automatically realize business value.
🚀Making your solution faster than its predecessor DOES NOT GUARANTEE that your customer’s process will become faster.
⚙ Improving efficiency with your solution DOES NOT GUARANTEE that the customer will save costs.
In these scenarios, while you might be doing something right, it may not be sufficient for your customers to realize value. This challenge arises when other factors in the surrounding context prevent the realization of the benefit. It’s a subtle but critical misalignment that can render even the most promising technological advancements ineffective.
Consider an AI system that reduces the risk analysis time in commercial lending from three days to one. It’s an impressive achievement, but if another part of the process, such as document validation, takes seven days and remains unchanged, the overall impact is nullified if time for approval is the key metric. Or consider using generative AI to enhance emails to B2B customers. If those emails reach someone without decision-making authority, the improvement may be mostly in vain.
This issue of focusing too narrowly on a specific context is understandable and often valuable. Specialization, breaking problems into parts, and honing in on specific challenges drive innovation and foster deep expertise. However, this approach can also mislead us into evaluating our creations solely within a limited scope. This pitfall is not confined to small startups or specific sectors; it’s a phenomenon I’ve observed across both large and small companies, and with various technical and business-specific solutions. Recognizing and addressing this challenge is essential for translating technological promise into tangible, real-world value.
Translating Innovation into Value: The Imperative of Zooming Out
These scenarios underscore a critical need: the imperative to “zoom out” and understand how innovation fits into the larger system. Creating something new and exciting isn’t enough; it must function effectively in the real world, where complexities and interconnections often dictate success or failure. This perspective is not just a theoretical exercise; it’s a practical necessity for anyone seeking to translate technological promise into tangible, real-world value.
In the context of AI innovation, zooming out means adopting a higher-level perspective to fully grasp where and how a solution or innovation is used. It’s about transcending the narrow focus on a specific task or feature and understanding the broader ecosystem. This includes recognizing the various actors involved, the systems they interact with, the underlying economics, and other contextual factors that influence how the innovation functions.
We must find a balance, going high enough to cover all the pertinent aspects without making the view so broad that it becomes overly complex and unmanageable. By zooming out, innovators can understand what else is needed for their creation to be effectively utilized, ensuring that the innovation translates into real-world value.
Some readers might draw parallels between what I’m sharing and frameworks like PESTEL analysis, systems thinking, or contextual analysis. While there are similarities, the nuance here is crucial — I’m asking you to zoom out from a given context of use, focusing on the factors that directly impact the translation of technological benefits into real-world value, rather than adopting a very broad macro perspective that might not provide practical solutions.”
Applying the Zoom-Out Perspective
Let us consider the 2 examples we spoke about earlier.
Mortgage Risk Analysis — Addressing the Real Bottleneck: Zooming out to understand the entire mortgage approval process uncovers the real bottleneck: document validation. This broader view leads to targeted solutions like automating this area or partnering with specialists. The focus shifts from merely speeding up one part to addressing the main obstacle, aligning the innovation with actual customer needs.
Generative AI for Email Enhancement — Reaching the Decision-Makers: A zoomed-out perspective on B2B communication shows that success depends on reaching the right audience. By focusing on market segments where emails reach decision-makers or finding different channels for content delivery, the innovation becomes more than a clever feature; it helps open up a new market sub-segment.
The zoom-out technique is not confined to understanding processes or identifying key players. It can uncover essential insights into complementary technologies or systems needed, the economics of the system, or other critical factors. More importantly, it’s not just about taking in the broader picture but identifying all the hurdles that come in the way of realizing the value of the innovation. By addressing these barriers, the zoom-out perspective transforms technological promise into tangible, real-world value.
Practical Tips for Implementing the Zoom-Out Perspective
To effectively apply the zoom-out perspective in your innovation journey, consider the following tips:
1. 🌟Find the Goldilocks Zone
Zoom out far enough to touch upon most issues that impact your innovation, but not so far that you’re overwhelmed with too many variables or impractical concerns. For an AI application in mortgage processing, understanding upcoming mortgage regulations may be relevant, while macroeconomic factors affecting AI globally might be too distant to impact your specific solution.
2. 🛠️Look for Various Types of Issues
Explore business, technology, people, culture, economics, and more. For instance, AI systems might need training suitable for workers grappling with various technologies. By considering a wide array of factors, you can uncover hidden challenges and opportunities.
3. 📡Search for Unknown Unknowns
Engage with others involved in the ecosystem and ask open-ended questions. This approach can reveal unexpected insights and help you navigate the complexities of the broader context.
4. 🔍“Zoom In” Again
While zooming out helps identify various hurdles, solving them requires zooming back into specific areas. Understanding the big picture is vital, but implementing effective solutions requires detailed focus.
5. ✒️Look for Non-Technical Solutions
Often, the hurdles are non-technical. For example, an AI-driven customer service tool might require organizational changes to integrate effectively with existing workflows. Recognizing and addressing these human factors can be crucial.
6. 🧬Find Complementary Solutions
Identify what complementary solutions might be needed for your innovation to work. If your solution generates email text, there might be a need for complementary tools to ensure the generated content complies with policies.
7. 🌈Be Open for Change
Be aware that you might find other issues insurmountable or that you may be better off working on another area. Being open to change can lead to more effective solutions.
8. 💪Do This Exercise Early and Often
Implement the zoom-out perspective early in your product life cycle and revisit it regularly. This proactive approach ensures alignment with real-world needs.
Conclusion
The ‘zoom out’ perspective is a powerful tool for bridging the gap between technological promise and real-world value. It’s a journey of discovery that can lead to unexpected insights and opportunities. I invite you to apply this perspective in your innovation journey and share your experiences. Your feedback can help refine this approach for future innovators. Together, let’s turn technological promise into tangible, real-world value.
Image by ThuyHaBich from Pixabay
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