Key takeaways:
1. If everyone can get an answer, what is the role of research?
“Executives might think they can just ask AI a question and get the answer. They’re ‘AI pilled.’” — Michelle Grant, SAP
Data is being democratized and moving into UX, CX, innovation, and other parts of the organization. Executives and stakeholders can increasingly ask AI a consumer-related question and get a useful answer on their own.
Giving your internal stakeholders access to a target customer persona chatbot makes insights accessible, but creates a fundamental question for the research function: If research is no longer the gatekeeper to consumer understanding, what is its role?
VF’S TAKE:
AI capabilities are increasingly distributed throughout the organization. If we don’t give access, stakeholders will just find another way to get insights. It’s no longer about producing better research, but staying relevant, earning attention, influencing stakeholders, and translating insight into action.
That means understanding the business and its priorities, knowing how to influence stakeholders, telling a story people will remember, collaborating across functions, using AI effectively, and applying the methodological judgment to know when an answer holds up and when it needs to be challenged.
2. How do we use AI to put research into the flow of the business?
Stakeholders increasingly expect answers as quickly as they can get them from tools like Copilot, putting pressure on researchers to deliver useful information that fits into the workflow. As AI capabilities create new research opportunities, they also raise questions about quality, validation, expertise, and what constitutes defensible research. Synthetic data is a good example.
“Synthetic is a land mine, and we need to be careful about undermining our own craft.” — Thomas Walker, eBay
The strongest critiques centered on synthetic quantitative data, particularly synthetic sample boosting. Melanie Courtright at Sego offered an alternative: after evaluating the options, she saw more promise in digital twins.
VF’S TAKE:
The reality is, high-quality research has friction. It takes time and money to do amazing work.
In response to this, we see strong potential in digital twins. A well-built digital twin can put research intelligence in the hands of stakeholders when they need it, allowing them to explore questions, test ideas, and get directional feedback between traditional research projects. Book a meeting to learn more about our qualitative digital twin platform.
We saw this with MetLife, where digital twins of HR decision-makers are being used throughout the year to develop questionnaire inputs and explore themes between waves of its Employee Benefits Trend Study. It puts research in-house and on demand, with the speed and accessibility of Copilot.
3. There continues to be an attention deficit.
“Good work is being done by all of us, but how do we get this out to people better in our organization and get them to act on it?” — Jill Donahoe, Nestlé Purina
Some things don’t change. Just because we can get data faster and there are more ways to generate insights, getting people to actually pay attention and act is still difficult.
Storytelling, influence, and business fluency came up repeatedly at CRC as skills that will become increasingly important.
VF’S TAKE:
Amid the scramble to figure out what AI can help us do and where it will take us, it’s important to keep our eye on the ball: delivering impact for our clients.
Jill’s observation that “storytelling differentiates high performers from low performers in terms of getting attention” really hit home for the team at VF. Storytelling has been a core part of how we turn the data and insights we uncover into a storyline that people can understand, remember, and act on.
See what it looks like in practice in our award-winning work with Dexcom: Bringing a Segmentation to Life with AI.
