What Is the Proof-of-Concept Trap – and How to Avoid It

The spark of an idea and inspiration is an exciting time during the innovation process. And to fan the flames, businesses need to create and validate a working model, better known as a prototype.

But there’s a catch: prototyping is more than just checking whether the innovation is feasible, scalable, desirable, and viable. The real work is knowing how to deliver long-term, meaningful outcomes to the organization and understanding how the innovation changes human behaviour, process, technology, and regulatory compliance.

The proof-of-concept trap refers to that moment when a company develops beautiful ideas, builds shiny prototypes to evaluate them, and fires up the company on their value – only for the sledgehammer of reality to pummel progress. The company can’t deploy the innovation because it does not have the right technology, business process, or culture in place to support it.

Unfortunately, this is what happens when a business does not look at the full picture when considering how innovation and intelligent technologies can make a difference to the company. After spending countless hours, significant capital, and tremendous brainpower on the innovation, the business finds out that it needs to implement a technology stack or its components to build a supporting foundation. And worse, it’s possible that the required technology is more mature than the existing IT architecture.

Although this is a frustrating event for everyone involved, it’s also one of those crossroads where providers, such as SAP, shine. From the beginning, businesses need to scale their view through a more pragmatic lens. Businesses should discuss with their partners what’s possible for their innovation and help ensure that the effort delivers what matters most – real, meaningful outcomes over time.

Scaling innovation from the very start helps flush out enablers as well as blockers of the process and the final deliverable. If a business creates a prototype and wants to roll it out across 7, 15, or 30 sites, deployment can become quite complicated if those obstacles are in the way.

It is also critical to comprehend how the new innovation capability changes how business value is conceived and measured. Imagine if a business is looking at using machine learning and artificial intelligence to run digital marketing programs. The meaning behind key performance indicators, such as customer lifetime value, can shift significantly because customer relationships will change and new opportunities for monetization will emerge.

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