Mariana Peneva of IRG interviews Jiunn Shih for the Humanized Growth in the era of AI study. The opening question is broad: how has marketing been using AI to drive growth, what was tried, and what worked.
His answer starts with education rather than deployment. The first step for the marketing organization was to understand the different AI tools, test them, and map them against specific marketing needs.
Key findings
- He groups the potential uses into four: lowering the cost of activities such as generating content and assets, providing better insights, supporting better decisions, and personalizing the customer journey.
- Personalization is the one he has not attempted. He describes the company as still navigating it, while the live use cases sit in insights.
- The main completed project is a revision of the company’s demand spaces, last done in 2017, prompted by shifts in how people choose fruit and healthy foods since COVID.
- Rather than questionnaires and consumer interviews, the new version mapped what people actually search for and look for across demand spaces for healthy foods.
- His summary of what AI is for is compact: an amplifier of what a business already does well, or a way to make it more efficient.
What was actually built
The demand spaces work was done with an AI agency after comparing it to traditional market research. It came back faster and at slightly lower cost, and he says it was more thorough and opened up a better perspective on the spaces themselves.
A second benefit is repeatability. Refreshing the work in a couple of years, or testing directionally whether a space is growing, is now much faster.
The second project is a proof of concept on generated assets: producing images of the brand’s mascot characters in different poses, faster and cheaper. He reports the images look great and arrive at a much faster pace.
The limitation he names is control. They cannot run their own prompts and still have to go through the agency, though the ambition is that social media images could eventually be generated this way.
Who marketing has to work with
The obvious partner is the digital, technology, or information officer, and the reason is data: getting different sources ingested into one organizational store.
The second reason is protection. He describes continuous investment in cybersecurity and an obligation to be transparent with the digital team about what marketing is doing, so they can set the right level of defense.
The partnership he says needs strengthening is with HR. Because AI is a new tool, it requires building different capabilities in the team, so identifying those capabilities and the mindset belongs on the chief people officer’s agenda.
That work has not started structurally. The conversation has begun, but he is explicit that a more structural program is needed because AI affects every part of the business, not only marketing.
What the CEO has to supply
He splits the CEO’s role in two. The first is resource allocation, since no chief executive will say no to AI, but the level of money and people committed to testing and proving the case is what matters.
The second is visible engagement. He wants a chief executive who says let us do it, let us learn it, let us prioritize, and who personally engages with the initiatives to support a culture of testing and learning.
He is candid that the picture may not hold. The technology is evolving so rapidly that in six months to a year the view could be much clearer or simply different.
Company-wide, two things have happened: an earlier initiative to ingest data into a database and set up internal pipelines for analytics, and now a rollout of an AI assistant with tracking of how much it helps and what people use it for.
Keeping the human in it
His view of marketing’s contribution to an AI transformation is balance: holding the technology against empathy, creativity, and an understanding of culture and the business model.
Marketing’s role, he says, does not change. It remains the external window to the world, bringing what consumers and customers need and how the category and culture are shifting, and it is the function that brings creativity to the business.
Asked how the human side survived the demand spaces project, he answers that behind the data were people: humans looking at photographs, humans searching for what to eat.
He says he never treated data points as data, only as indications of the activities, behaviors, and actions of human beings, and that the technique lets you look at actual behavior more thoroughly than a questionnaire of one or two thousand people could.
Where the bigger opportunities are
Across the organization he expects AI to make individuals more effective and to reduce repetitive work. One live test is expense approval, where the aim is to eliminate or substantially reduce the process.
He also leads innovation and research, where the ambition is to accelerate the fruit breeding program now that the genome has been mapped.
Breeding decisions run on three clusters of traits: consumer traits such as flavor, size, color, and health content, supply chain traits such as storage and travel, and orchard traits such as yield per hectare, pest resistance, and performance in different climates.
That process is how the company developed its red kiwifruit, launched about three years earlier and not yet in Europe at the time of the interview.
Back in marketing, the next horizon he names is personalization in consumer engagement, campaign optimization, and more automated content generation for different journeys.
The closing point
He returns to the fundamentals. He does not see AI changing what marketers do: the power to shape how people think, feel, and believe, and to drive different choices and behaviors, which AI can amplify or make more efficient.
His warning is about attachment to tools. Marketers get so attached to the technology that they forget the job: understanding consumers and developing profitable solutions, products, and brands that solve people’s needs.
Asked what he would want from the study, he names one question: how AI helps companies be more humanized rather than dehumanizing growth, and which organizations are already doing that.
Scope and limitations
This is a single interview transcript, machine generated, and several company and agency names are rendered imperfectly in it. The projects described are early: one completed insights study, one proof of concept, and an assistant rollout still being assessed.
Source
This page summarises Transcript: Humanized Growth in the Era of AI – Zespri (Jiunn Shih), by Mariana Peneva, Jiunn Shih, 2024-12-05.