Observed Signal · May 20, 2026 · Opinion · Source: AdExchanger · Impact: 2/5 · Sentiment: Positive
Brands Can Use AI Without Perfect Data
An opinion piece by Matt Emans (Co‑Founder, Newton Research) published on May 20, 2026 on AdExchanger argues that brands should deploy agentic AI on existing, imperfect data rather than waiting for perfect datasets. Emans explains that AI agents build semantic understanding across disparate, messy data sources and can compensate for gaps by adjusting confidence, leveraging priors, and flagging uncertainty. He contends that frequent AI-driven measurement (e.g., automated MMM, incrementality testing, attribution) reduces risk compared with infrequent manual studies, and that early adoption of agentic AI yields faster, practical measurement and optimization gains for audience targeting, media buying, and closed-loop measurement.
Sector commentary encouraging practical adoption of agentic AI for measurement and optimization; useful perspective for advertisers and measurement teams but not a platform product launch or regulatory change.
Track Newton Research Signals & Market Shifts in Real-Time
Polaris7 autonomous intelligence agents track regulatory filings, primary sources, executive changes, and deal flow 24/7. Create your free Explorer workspace to monitor these entities.
Key Takeaways & Evidence Grounding
- Opinion article by Matt Emans, Co‑Founder of Newton Research, published on AdExchanger on 2026-05-20.
- The piece argues agentic AI can interpret and reason over spotty or inconsistent datasets without extensive upfront schema harmonization.
- Emans claims agentic AI enables more frequent measurement (e.g., MMM, incrementality testing, attribution) and that frequent reads reduce overall measurement risk versus rare manual studies.
- The article positions agentic AI as a practical route for brands to derive value from imperfect data across targeting, media buying, and measurement.
Connected Companies & Entities
1 Entity mappedOntology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
AI Empowers Marketers: Redefining Success in AdTech
AI is not a substitute for marketers but a set of tools built on quality data and guided by human expertise that can empower planning, activation, optimization and measurement. It can automate insights, detect patterns and generate reports quickly, but people must define the right questions, interpret results and ensure automation aligns with brand and business goals. Looking ahead to 2026, the article outlines AI's impact in three areas: audience targeting and segmentation, campaign optimization, and the broader ad tech stack. It highlights advanced audience modeling that combines deterministic and probabilistic data to identify high-fidelity lookalikes, and predictive behavioral models that forecast engagement and conversion. It also discusses privacy-preserving techniques like federated learning, dynamic creative optimization, and rapid programmatic bidding with transparency challenges. Beyond this, AI powers data ingestion, identity resolution, and measurement advances such as multitouch attribution and AI-powered incrementality testing, underscoring that real data and clear strategy are essential to realizing AI’s potential.
Performance Marketing Needs Clean Data Before AI
The article argues that performance marketing teams must fix data quality, tracking and attribution gaps before relying on AI for campaign optimisation, reporting and budget decisions. It cites multiple 2026 industry studies showing widespread AI adoption but persistent measurement and data-readiness problems: Salesforce finds high AI adoption yet many generic campaigns and poor customer context; Gartner reports rising AI spend but limited readiness to scale; Adobe and McKinsey flag limited CDP coverage and trust risks from inaccurate data. Common issues include missing UTM parameters, inconsistent partner IDs, lost click IDs, late or misattributed post-install events, and fragmented partner payout records. The piece recommends shared partner rules (stable IDs, taxonomy, conversion definitions), clear event definitions, and end-to-end data discipline so AI outputs (channel rankings, budget recommendations, partner payouts) are trustworthy.
Data Quality: The Key to AI's Marketing Future
AI systems are only as good as their data. The article argues that the next standard for AI in marketing is data quality defined by accuracy, freshness, consent, and interoperability. Accuracy means signals anchored to real human identity; freshness means ongoing updates to reflect current consumer behavior; consent involves transparent governance; interoperability enables cross-platform integration via a secure identity spine. As marketing shifts toward agentic advertising, flawed data accelerates bad decisions. The piece emphasizes continuous data validation, deduplication, and context to keep models reliable, and notes that deterministic signals require ongoing verification. It also asserts governance should be embedded in data platforms to meet privacy laws, and that human oversight remains essential in turning automated insights into actionable strategies. It concludes by praising Experian as a source of accurate, privacy-first data and urges building data principles around transparency and trust.
Track Real-Time Market Signals & Shifts
Set up custom watchlists to receive automated, evidence-grounded executive digests whenever material signals or shifts occur across your tracked landscape.