Much is currently being discussed on how AI could replace researchers in the industry. I have been reflecting on some positive developments that AI technology can offer us to drive excellence in our roles.

AI can actually put the ‘human’ into the heart of the research workflow process. AI offers us the opportunity to truly place human critical thinking at the very heart of insight.

In this article I reflect on the innovation pipeline testing workflow, and what leveraging AI could look like in terms of real commercial benefits. AI is allowing for: greater speed of turnaround; efficient leveraging of past historic data; cost efficiencies and more critical thinking; input and collaboration with our stakeholders and research partners at key pivotal stages.

AI also allows for stronger partnerships across stakeholder, insight and researcher teams, as all can collaborate and pivot at pace to reiterate and recycle insights and concept ideas in real time. This is exciting and offers much more opportunity for strategic teamwork.

AI also allows for qualitative experts to bring their wealth of expertise and experience to co-creative and sequential recycling phases in the workflow, opening up exciting ways for these experts to strategically work with both clients and customers and deliver impactful insights that influence commercial decision-making. In the innovation pipeline, investment risks can often be very high. Every step of the workflow has significant impact and commercial repercussions.

Let’s look at how AI allows us to put the human into each part of the five-step innovation workflow process.

1. Leverage existing data & primary sources

AI tools allow for fast harnessing and leveraging of data across the internet, across big data sets and knowledge repositories, sifting and gathering all insights across primary and past research at lightning speed. This means humans can focus on the prompts, data accuracy, strategic thinking and stakeholder partnership, rather than on the manual collation and synthesis of data.

More time can be spent on the ‘insights,’ ensuring they are powerful, customer-centric and resonant as well as on sharpening the strategic brief. We focus on the big picture thinking and not manual tasks.

2. Insight generation

Phase two is all about critical thinking and human work: insight generation and optimisation done with clients, customers, and qualitative experts, sequentially recycling, and optimising the insights. This is work that can be done on the hoof and is not for the faint-hearted.

It’s a real opportunity for qualitative experts to bring their A-game, partnering with clients strategically, while championing the
customer and delivering optimised insights – the essential foundation of the entire innovation pipeline.

Insights leaders can focus on partnering with their stakeholders in this iterative phase and challenging where necessary – leading to stronger, more differentiated and commercially impactful insights.

3. Quantitative insight validation

Insights are quantitatively validated with humans in this third phase, and significance testing ensures the most robust insights move forward into the concept development phase. Human rigour is validated and sig tested.

4. Concept co-creation & recycling

Now the concept is built and evaluated. Qualitative experts co-create concepts with customers and clients and AI technology is used in facilitating reiterations and stimulus developments in real time.

The AI facilitates the speed and recording of reiterations but it does not replace human customer input nor human qualitative, marketing or insight expertise. People create and build the ideas and AI tech facilitates an efficient and expedient process.

5. Quantitative concept testing

Quantitative testing with humans and benchmarking against KPIs and action standards leads to final idea refinement.

In the above workflow, AI allows for a very thorough and robust start, efficiently revisiting all historic data, allowing for greater human critical thinking and reiterative work in the subsequent phases. This allows insight teams, marketers and customers to bring more critical thinking and strategic collaboration into the workflow phases.

Qualitative experts will be elevated from researchers into true strategic partners, influencing the process in real time by integrating and recycling live customer feedback. Tools such as DALL-E, Midjourney and others can be used for stimulus creation and synthesis tools can allow research transcripts to be synthesised quickly, meaning researchers can discuss insights with clients at pace.

AI offers tools to help us be better, faster, more efficient, customer-centric and focused. It also allows us to be more strategic and to level up into strategic influencers, impacting the commercial agenda.

Febronia Ruocco is a global strategic insights & analytics director and executive leadership coach