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 will AI 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 and strategy and risk advisory to 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 transactions, or hazards that prompt 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 on 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 based on sensor data input and predefined actions can already be done by today’s AI-enabled assistants. In a creative process, there are many ways to automate and enhance existing design process steps and tools.

Principle 3 – Customize

Customizing a design output to match its audience is essential 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 into consumer behaviour. Understanding behaviour and context is a critical part of creating relevant future brand and product propositions. The Synthesize principle can be applied in the innovation process and 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 recreate 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. AI-enabled data can already reproduce a creative skill or craft to a certain extent.

Principle 6 – Create

Use 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 datasets and AI applications going mainstream, it is now feasible to incorporate data science at more stages of innovation and design processes 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, an AI business transformation consultant at IBM, stated in our interview: “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 continue researching opportunities and possibilities of AI in creativity and design.