Observed Signal · Jan 2, 2026 · Opinion / Commentary · Source: ExchangeWire · Impact: 2/5 · Sentiment: Neutral
Three New Year’s Resolutions for Ad Tech 2026
Shirley Marschall, in an ExchangeWire column dated January 2, 2026, proposes three modest New Year’s resolutions for the ad tech industry: (1) stop reflexively declaring legacy channels or tools 'dead' and base such claims on credible behavioural evidence; (2) remove the 'AI' smoke screen by being honest about what AI features actually deliver rather than using AI as marketing gloss; and (3) actively prune and sunset outdated measurement, targeting and format layers so the ad tech stack is less cluttered and more maintainable. The piece cites industry voices (Mark Ritson, Justin Scarborough, José Miguel Sokoloff) and frames the recommendations as practical cultural and operational changes rather than radical technical shifts.
Industry-relevant opinion piece that highlights practical operational and cultural issues (AI honesty, measurement clutter, hype) affecting ad tech practitioners, but it does not announce technical releases, policy changes, or major market moves.
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Key Takeaways & Evidence Grounding
- Article published on ExchangeWire on January 2, 2026, authored by Shirley Marschall (ExchangeWire weekly columnist).
- Proposes three resolutions for ad tech in 2026: reduce 'death of' declarations; be honest about AI claims; and actively retire/streamline legacy ad tech stack components.
- Calls out industry behaviours around marketing hype, ambiguous 'AI-powered' claims, and accumulation of measurement and targeting layers (cookies, IDs, CDPs, DMPs, multiple KPIs).
- Quotes or references industry commentators Mark Ritson, Justin Scarborough and José Miguel Sokoloff to illustrate points about rhetoric, AI honesty and human roles.
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AI in Ad Tech: A Call for Calm in 2026
Shirley Marschall’s ExchangeWire year-end column assesses AI and ad tech in 2025 while looking ahead to 2026. The piece argues that the AI conversation has grown fatigued after a year marked by hype, fear-mongering, and a flood of GenAI tools, with users often acting as unpaid testers. It sketches an industry divided between rapid change and adoption challenges, referencing quotes about AI narratives and the “Ever-Waser” mindset in advertising. Marschall envisions a calmer, more vanilla 2026, even as she notes potential developments like AI agents, quantum computing, or the first data center in space. The column closes with holiday wishes and an invitation to share ideas for 2026 on LinkedIn, while pointing to headlines about AI integrations and evolving industry chatter.
AdTech 2026: Transparency and Innovation Take Center Stage
AdExchanger opens 2026 with a reflective comic noting that 15 years have passed since the January 2011 strip. It frames ongoing tensions between middlemen and buyers as unresolved but highlights a hopeful trend toward greater transparency across the ad supply chain. Beyond the comic, the page features a Must Read roundup: iSpot has launched SAGE, an agentic AI platform with a ChatGPT-like interface to generate campaign ideas; the OAAA has introduced a new content taxonomy to classify inventory in OpenRTB bid requests; and the IAB has published an AI regulations framework to standardize when AI in ads should be disclosed. The piece also references LiveRamp’s Q4 earnings and other programmatic/CTV topics, including Evertune’s feature for targeting on sites cited by AI chatbots and guidance to reduce ad fraud in CTV supply.
Ad Tech’s 'Fairy Dusting' of Innovation
This ExchangeWire column argues that ad tech vendors frequently overstate the impact of small or superficial features — a practice the author likens to 'fairy dusting' in cosmetics. The piece says trends and labels (e.g., 'retail media', 'agentic AI') are rapidly adopted across vendors and retailers to drive narratives and justify budgets, even when actual implementation or concentration of capability varies widely. Examples include the broad rebranding of many retailers as retail media networks despite differing data quality and measurement, and the relabelling of existing automation as 'agentic' AI. The author warns this dynamic makes differentiation collapse into language, shifting buyer scrutiny from measurable outcomes to marketable claims.
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