Everyone has access to AI. Yet 75% of innovations still fail. The difference between AI success and expensive failure isn't the technology: it's the data that powers it. With 40+ years of consumer intelligence and 170 million panellists worldwide, Kantar shows how quality data transforms AI from promise to profit.
The Data Difference in AI Performance
- The Reality: 9 out of 10 marketers see GenAI's future impact, yet organisational readiness scores just 4.9 out of 10.2 This gap between aspiration and capability determines market winners.
- The Foundation: AI without quality data is just expensive computing. Kantar's 40+ years of proprietary data, encompassing 250,000 ad tests and 35+ million human responses, as well as 39,000 product concepts with over 6 million consumer evaluations, create AI capabilities that competitors can't replicate, achieving 89% accuracy in predictions.
- The Proof: Iceland Foods launched AI-created ready meals. Google tested 11,000 ads in under a month. Genomma Lab scaled creative across Latin America. 96 of the world's 100 biggest advertisers rely on Kantar's AI solutions and our comprehensive suite of products, powered by over 40 years of consumer intelligence.
- The Path: Success requires three elements: comprehensive data, domain expertise, and human oversight. Kantar's AI solutions combine all three, delivering significant campaign performance improvements through LINK AI and 20%+ ROI increase through LIFT ROI.
96 of the world's 100 biggest advertisers rely on Kantar's AI solutions and comprehensive suite of products, powered by over 40 years of consumer intelligence.
Why Most AI Initiatives Fail to Deliver
If AI is everywhere, why isn't every brand winning with it? Kantar's recent Global Trends Report provides clear evidence: while 57% of marketing professionals consider AI-driven insights essential, organisational readiness averages just 4.9 out of 10. This gap between aspiration and preparation explains why so many AI initiatives fail to deliver.
Patricia van der Mooren, Partner in Kantar's Consulting division identifies five critical barriers: limited understanding of AI's capabilities, ethics and compliance concerns, data management challenges, lack of AI-specific skills, and organisational readiness issues. "We see quite a gap between the expected impact of Gen AI and the internal readiness today," she notes.
The Hidden Cost of Generic Data
Jane Ostler cuts to the heart of the problem: "AI is only as good as the data it's trained on." When every brand uses similar datasets and generic models, they risk creating AI-generated content and insights that lack differentiation.
Generic language models can't distinguish between the complex relationships linking brand equity metrics and financial performance. They miss the category-specific purchase cycles that determine when marketing investment translates to sales. Those familiar with brand and sales data understand these crucial lag times. While standard AI solutions miss these connections, properly trained models consider the nuances that make the difference between accurate predictions and expensive mistakes.
When Purpose Meets Processing Power
Leading brands are closing this readiness gap through purposeful implementation. At Heineken, Global Consumer and Market Insights Director Tony Costella describes their approach: "We talk about envision, empower, and enable. In these early stages, it's a lot about envisioning and showing what's possible to get people excited about what the future can look like.
Microsoft Advertising's Paul Longo, General Manager in AI Ads, sees AI bringing brand and performance marketing closer together than ever before: ""We've seen customer journeys when they have a Copilot chat included shrink by up to 30%. And when there's a purchase involved within that consumer journey path, we've seen a 53% increase in purchases within 30 minutes of that chat interaction.
These results only materialise when AI is powered by quality data and clear purpose. Without this foundation, the 62% of marketers increasing their AI investment risk pouring resources into sophisticated technology that delivers generic results.
62 % of marketers increasing AI investment, but without data strategy, it's wasted spend.
How 40 Years of Data Creates Unbeatable AI
Kantar's AI journey didn't begin with ChatGPT. It started in 1983 with multivariate statistics, the precursor to modern machine learning. This 40-year progression from algorithmic forecasting through deep learning to today's generative AI has created an irreplaceable foundation: comprehensive, validated, proprietary data that transforms AI from impressive technology into business advantage.
What Makes Data "Quality"?
Quality isn't just about volume, it's about validity. True quality data combines behavioural patterns capturing real-world customer interactions with attitudinal insights from surveys and feedback. It requires historical depth spanning decades, not months, to identify genuine trends versus temporary fluctuations. It demands diversity: Kantar's millions of global panellists provide representation across demographics, markets, and behaviours.
Critically, quality data must be verified. Kantar's Qubed anti-fraud technology and Triple Lock Quality Loop, our proprietary system that verifies data quality at collection, processing, and output stages, ensure data integrity at every stage. As Ostler emphasises: "Even synthetic data, the artificially generated information that extends real insights, requires a solid foundation of high-quality, relevant and recent human data to be truly useful."
Heritage Data in Action
The scale of Kantar's proprietary data creates capabilities that generic AI simply cannot match:
- LINK AI processes 37 million human responses from 250,000 ad tests accumulated over three decades
- ConceptEvaluate AI analyses 39,000 concepts tested globally with nearly 6 million consumer evaluations
- TrendAI examines 1.3 billion data points annually, combining decades of brand measurement expertise
Generic models trained on internet content can generate plausible-sounding marketing copy, but they can't predict which concepts will succeed in the market because they've never seen real consumer responses to actual products.
A brand needing insights from Gen Z luxury car buyers in Germany might traditionally yield just 20 responses, insufficient for statistical validity. Kantar's approach uses high-quality panel data to train synthetic models, transforming those 20 responses into statistically valid insights. But this only works because the foundation data is comprehensive, verified, and representative.
Forbes predicts synthetic data will become a $2.34 billion industry by 2030. The brands that win will be those with access to quality data that makes synthetic insights reliable.
Synthetic Data: Extending Human Intelligence
The emergence of synthetic data, AI-generated information that extends real insights, proves why data quality matters. Synthetic Sample Boosting allows brands to understand underrepresented segments without expensive recruitment. But as Jane Ostler Kantar's Thought Leader warns: "Synthetic data is an extension of human data, not a replacement."
Kantar processes 1.3 billion data points annually across 170 million panellists.
Dive Deeper: The Human Element: How Expertise Combines with Data to Produce AI-Powered Insights
How Domain Expertise Amplifies AI Performance
Data alone isn't enough: expertise transforms it into competitive advantage. Kantar's three-layer integration model shows how proprietary knowledge multiplies AI's impact.
The foundation layer enhances core AI capabilities through domain-specific training. Here, decades of consumer research and industry taxonomies teach models to understand the nuanced relationships between creative elements and consumer response across markets.
The analytics layer integrates proprietary algorithms and frameworks. LINK AI exemplifies this, combining video and audio analysis with natural language processing and custom neural networks. Our AI solutions achieve up to 89% accuracy when predicting outcomes versus traditional surveys.
The reasoning layer transforms complex insights into practical intelligence. KAiA, Kantar's AI assistant, enables users to explore brand performance through natural language queries while accessing comprehensive knowledge bases. A leading cereal manufacturer discovered this power when they implemented KAiA across 100+ users analysing data for 30 brands across 40 markets.
Natural Language plus Data Science Collaboration
"Data is often only accessible by trained analysts," a cereal manufacturer's Global Head of Analytics explained. KAiA uses natural language as a way of getting answers on the fly without having to be a skilled analyst.
This collaboration extends beyond accessibility. While AI processes vast datasets rapidly, human expertise provides strategic context that shapes actionable recommendations. Brand experts guide parameter selection for AI training and identify which metrics carry the most weight. They validate AI pattern detection to distinguish short-term blips from lasting trends and apply market context that AI alone will miss.
IP as Competitive Moat
This combination of data, expertise, and technology creates insights competitors cannot produce. NeedScope AI, for example, brings precision to emotional clarity analysis across brand touchpoints. Our research confirms that brands with strong emotive clarity achieve significantly greater differentiation and are 1.5 times more likely to command premium pricing.
These capabilities can't be replicated by simply using generic AI tools. They require decades of validated frameworks, a deep understanding of what drives brand growth, and the expertise to ask the right questions.
"Instead of manual tasks, teams can focus their time and energies on the things that really matter: the 'so what for the business?' questions." - Senior Insights Manager, Global Brewer
Kantar’s AI Lab is pushing the boundaries of what’s possible, experimenting with clients and strategic partners such as Microsoft. We have 500+ AI data scientists and technologists, including 100+ top engineers from Microsoft, plus privileged access to their latest AI models and platforms. Our AI Lab, led by Chief AI Scientist, Dr. Ashok Kalidas, is recognised as one of AdWeek's 2025 Top 100 AI Trailblazers and managing 50+ live R&D workstreams.
Brands with strong emotive clarity achieve significantly greater differentiation and are 1.5 times more likely to command premium pricing.
Real Brands, Real Results, Real ROI
Theory meets reality when brands achieve breakthrough results. The following cases show how quality data and proprietary expertise translate into competitive advantage.
Speed Revolution without Sacrifice
When Iceland Foods aimed to pioneer AI-created ready meals, they needed swift, reliable concept testing for their new wellness range. Using ConceptEvaluate AI, they quickly identified the most promising products and energy-focused benefits. The AI-powered evaluation helped Iceland pinpoint which concepts would resonate strongest with consumers while maintaining brand differentiation. The insights guided Iceland's product development strategy and accelerated product innovation while reducing market risk, leading to the launch of their first AI-created ready meals in early 2025.
Google pushed speed even further, testing 11,000 ads in less than a month using LINK AI, a scale and velocity that traditional methods simply cannot achieve. This isn't just faster testing; it's a fundamentally different approach to creative optimisation.
"The time from idea to launch in the FMCG space is anywhere between 18 to 24 months. If we can lower that time, it can fundamentally mean the difference between success and failure." Unilever's Global Lead for Innovation, Suprio Banerjee
Test Everything: The Scale Transformation
Genomma Lab Internacional faced a different challenge: evaluating vast quantities of advertising content across multiple Latin American markets while maintaining brand consistency and commercial effectiveness. LINK AI provided a centralised testing solution whose predictions aligned with real-world sales improvements and market share gains.
"This allowed us to evaluate more creative assets at lower costs while uncovering valuable patterns for developing targeted communications across different brands and audiences," reported their insights team. The shift from testing selected concepts to testing everything fundamentally changes how brands approach creative development.
A global brewer with over 500 brands experienced a similar transformation through KAiA, automating 17 report types and handling more than 14,000 questions from 120 users. The Senior Insights Manager noted: "Instead of manual tasks, teams can focus their time and energies on the things that really matter."
The question isn't whether to use AI: it's how to make it deliver genuine business value. Success requires more than technology; it demands the right foundation, expertise, and approach.
From Generic AI to Competitive Advantage: Three Steps
- Audit Your Data: Quality beats quantity every time. What unique data do you possess? Is it verified, representative, and comprehensive? Generic data produces generic insights.
- Proceed With Purpose: Define business objectives before choosing technology. As Andrew Stephen from Oxford's Saïd Business School observes about successful implementations: "They work when you've got a clearly defined purpose to these projects."
- Create Safe Spaces: Follow Heineken's model of controlled experimentation: spaces where teams can explore AI capabilities without concerns about data privacy or quality.
How Kantar Accelerates Your AI Journey
With Kantar, you don't start from zero. You gain immediate access to 40+ years of validated consumer intelligence, pre-trained models ready for your specific challenges, and a unique partnership with Microsoft that ensures cutting-edge capabilities.
Looking ahead, we're building the future of AI-powered marketing: synthetic data boosting at scale for Brand Guidance programmes, API connectors for seamless integration, end-to-end agentic Creative AI, and synthetically created digital twins that predict consumer behaviour. By 2026, these capabilities will be standard, but brands partnering with Kantar can access them today.
The competitive advantage isn't in having AI; your competitors already do. It's in powering AI with quality data, proven expertise, and validated frameworks that transform technology into growth.
Dive Deeper: From data sorting to competitive edge: How large-scale AI models are powering next-gen insights
Five Principles to Harness AI’s Secret Sauce and Build Your AI Native Future
- Pick the Right Problem: Define clear business objectives before choosing technology. AI is the means, growth is the goal.
- Bring the Right Data: Quality beats quantity every time. 40 years of validated insights trump terabytes of internet text.
- Use the Right Model: Match AI capabilities to your specific challenge. One size doesn't fit all; leverage proven models built for your industry.
- Start Small and Now: Begin with focused pilots that deliver quick wins. Scale what works, learn from what doesn't.
- Manage Change, Not Just Technology: AI success requires more than tools; it needs people ready to use them. Create safe spaces for experimentation while building organisational capability."
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