When Alice asks the Cheshire Cat: "Would you tell me, please, which way I ought to go from here?" the Cat replies, "That depends a good deal on where you want to get to."
Sadly, or fortunately for lovers of Lewis Caroll’s Alice in Wonderland, without a clear destination, Alice's journey becomes a random exploration.
That same experience can happen to innovators when they peer through the looking glass of AI tools. There are lots of options that might be helpful, but how should you use them, and which ones will actually help you meet your objectives?
The random exploration that Alice undertakes certainly exposed her to some novel experiences. However, the best approach is to have a plan and a goal, allowing you to dive in and experiment with confidence.
AI helps innovators unlock human potential
AI tools offer a world of opportunities, especially where they’ve been trained on real, highly accurate consumer quantitative data. Because this early stage of the innovation process doesn’t require you to go out to consumers, you can get early feedback on your innovations in hours.
This will help you assess whether product X will succeed with your target group, or if you can grow portfolio share in market Y. It can help identify differentiation opportunities quicker while also ensuring sensitive ideas can be assessed with maximum privacy. These AI predictions and actionable insights can then be applied to the next stage of the development process. This is where the human insight truly comes into play.
The power of the human in the process is to understand people and identify innovations that not only meet people’s functional needs but also ensure your brand stands out in its category, ultimately fostering long-term success.
Expanding your innovation testing regime
Used well, AI tools make you more efficient, giving your brands an edge, for example by providing early feedback and AI-powered insights across multiple concepts, many more than it’s been possible to test in the past.
This enables you to think more broadly while also ensuring you only take the very best concepts forward to more traditional in-depth testing. The ability to assess multiple concepts for early-stage feedback means you can quickly and easily assess ideas that might previously have slipped through the net. This kind of data-driven approach for product development also builds confidence in a world where the focus is on increasing pace while still making the right investment decisions. The expectation is that AI will rapidly become part of your daily toolkit, another method that all insights and analytics experts will use to identify the most effective innovations.
Three ways to innovate more successfully with AI tools
Despite the potential, AI tools can also face a credibility test. Not only are many insights managers wary of AI-generated data, but many other stakeholders can also lack trust in the results.
You can build a sense of belief in three ways that will also help you use AI tools to help build an agile innovation culture. Each of these steps will increase stakeholder confidence and help prove the technology’s value as part of the innovation process.
- Ensure each tool is trained on accurate data: You need to reassure yourself and your wider stakeholders that any AI tool is based on quality, robust data. Kantar uses high quality panel data to train machine learning and AI models on the power of meaningful difference and generates norms that can be used to test outcomes. Critically, the whole process is also overseen and updated by expert data scientists to ensure accurate predictions.
- Track your AI-powered performance: As with every insight tool, you need to assess whether AI is helping you innovate better. The key tests will be if tools are delivering advantage via wins such as unearthing new insights, making you more creative by helping you explore ideas you might previously have abandoned and allowing you to learn-test-learn. If you are ticking these boxes, confidence and momentum will follow.
- Start small to build confidence: Most companies are in the early stages of AI and testing could involve smaller projects initially. This might range from road-testing global innovations in local markets to testing more extreme iterations of a concept.
The win for innovators is people plus AI
AI delivers many advantages, especially where it’s been trained on real consumer quantitative data and that data is highly accurate. Because this early stage of the innovation process doesn’t require you to go out to consumers, you can get early feedback in hours.
At the heart of this challenge is the need for you to understand people, their emotions and needs. And while AI has progressed, your role has become even more critical. You need to be the human in the room, guiding your brand towards successful, Meaningfully Different innovations.
After all, would Alice have had a better time in Wonderland, if she’d had clearer directions?
To discover how you can use AI-powered insights for innovation with ConceptEvaluate AI, register for the upcoming webinar on 11 July.
