Data and AI are changing the future of work. They are critical inputs to decision-making, and in some cases are replacing human labour entirely. That said, research shows that, using today’s AI technology, fewer than 5% of occupations can be fully automated. The question is how AI will influence innovation and design services in the near future?

AI-enabled products are already pervasive in the finance, logistics, HR, digital content and life science industries, being used for everything from  fraud detection, strategy and risk advisory to HR, early cancer screening and using genomic data to predict protein structures. All of these applications play into the specific strengths of today’s AI: they accelerate repetitive analytical processes by finding correlations in vast datasets, detecting patterns and flagging potential (predefined) anomalies, be it cancer genes, unusual transaction or a hazard that prompts a self-driving car to hit the brakes.

This kind of ‘skill assistance’ has already proven useful to innovation, design and engineering teams, who use AI-enabled data applications to automate, accelerate and enhance consumer research, design processes and marketing execution. AI is useful in streamlining workflows as well. But where do the bigger opportunities for data and AI in the wider end-to-end innovation and design process lie?

Creativity thrives in  making unexpected connections and disruptive statements, often based on little more than a gut feeling. How could AI-enabled data possibly replace such an innate, invisible process? The question, then, is not about replacement, but enhancement. How can AI-enabled data bring an innovation and design professional the same value as it does a medicine developer, fraud specialist or self-driving car user?

The answer can be found in the following six principles: Monitor, Automate, Customize, Synthesize, Reproduce and Create. Using these six principles, AI-enabled data can indeed deliver added value to the creative process and enhance the user experiences of existing products.

Principle 1 – Monitor

AI can detect specific combinations of sensor states in big datasets. This principle is widely used in self-driving cars, but also in more complex design thinking processes to exclude biased designers or recognize unique consumer behaviour in a specific context.

Principle 2 – Automate

Automating tasks on the basis of sensor data input and pre-defined actions can already be done by today’s AI-enabled assistants, but in a creative process there are many ways to automate and enhance existing design process steps and tools.

Principle 3 – Customize

Customize a design output to match its audience. This is an essential principle for brands in today’s crowded media landscape. AI applications can, for example, help brands customize advertising to their audiences.

Principle 4 – Synthesize

Develop scenarios from multiple unrelated data sources and synthesize several new data types to create detailed insight on consumer behavior. Understanding behavior and context is a critical part of creating relevant future brand and product propositions. The Synthesize principle can be applied in the innovation process but also in the improvement of already existing products and services, such as the way Netflix uses AI-enabled data.

Principle 5 – Reproduce

Train an AI application to understand an existing creative expression (art, music, literature) and allow it to re-create something in the same creative domain. IBM supercomputer Watson created a new Gaudi painting. The Next Rembrandt was created using multiple data sources. Music producers are already taking advantage of AI-created melodies. Already, AI-enabled data can reproduce a creative skill or craft to a certain extent.

Principle 6 – Create

Using AI-enabled data to develop completely new products. NotCo is creating new dairy products using AI-enabled applications. The sheer scale of available data will play an essential role in filling innovation funnels of global FMCG companies.

With access to data sets and AI applications going mainstream, it is now feasible to incorporate data science at more stages of innovation and design processes in order to deliver more relevant products and services. Data and AI are changing the mix of qualitative consumer research, quantitative product research and prototyping required for successful innovation.

Designers play an essential role in directing these powerful AI-enabled data tools to develop high-quality, ethical solutions. Dasha Simons, AI business transformation consultant at IBM, stated this in our interview with her: “Design needs data as a powerful resource, but the same is true the other way around with tech savvy companies needing design to implement qualitative aspects.” Qindle will keep on researching opportunities and the possibilities of AI in creativity and design.