My Take (focused on Marketing) – 2
Five More Predictions for Marketing in 2026
Beyond Alpha, Artificial People, and NeoMails, here are five observable predictions that tie together the NeoMarketing thesis:
- Marketing Ops Becomes Goal-Based, Not Journey-Based
Marketers will increasingly specify constraints + goals (“reactivate dormant Q4 customers, $10K budget, 15% target, max 20% discount”), and agents will build and continuously adjust the plan. “Journeys” become an audit trail, not a design artifact. The marketer’s job shifts from building flows to setting objectives and guardrails.
- The Primary KPI War Shifts from CTR/ROAS to Retention Economics
Expect a visible shift to metrics like repeat rate, churn prevention, margin contribution, and engagement persistence. AI makes top-of-funnel output cheap, but profitable retention remains hard. The CMOs who win budget battles will be those who can speak the language of lifetime value, not campaign performance.
- Personalisation Splits into Two Tiers: “Cheap Content” vs. “Earned Relevance”
Most brands will flood customers with AI-generated variants—more emails, more ads, more noise. The winners will be those who combine modelling + timing + restraint to feel human and respectful. Relevance becomes a premium. The “Not for Me” problem doesn’t get solved by more content; it gets solved by better understanding.
- “Answerability” Becomes a Core Marketing Function
As discovery fragments across ChatGPT, Perplexity, TikTok, and AI assistants, brands will optimise for being cited and selected by AI interfaces. This means structured data, strong first-party content, clear entity truth, and machine-readable product information. A new discipline emerges—call it GEO (Generative Engine Optimisation) or Agent Optimisation—that sits alongside (and eventually supersedes) traditional SEO.
- The Customer Model Layer Becomes the New Moat
Brands will compete not on how many tools they have, but on the quality of their customer representation and learning loop. The strategic asset isn’t the CDP, the ESP, or the analytics platform—it’s the world model that understands each customer and improves with every interaction. This is what Artificial People enables, and it’s what separates brands that truly know their customers from those that merely have their data.
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The Great Rebalancing
Together, these shifts—Alpha pricing, consumer world models, inbox-as-attention—mark the beginning of a fundamental rebalancing: from acquisition-led growth to retention-led profitability.
For two decades, digital marketing has been an acquisition arms race. Brands pour money into Google and Meta to capture new customers, then struggle to keep them. CAC rises relentlessly; LTV stagnates. The math only works with venture subsidies or market dominance.
Agentic commerce breaks this model. When AI agents mediate discovery, the lowest-friction, highest-trust option wins—not the one with the biggest ad budget. Retention becomes the moat. Owned channels become profit centres. The brands that keep customers (and their agents) coming back don’t need to acquire as aggressively.
And central to it all is the insight that will define the next era: In an age where AI makes production limitless, the constraint is no longer content or computation. It is human Attention.
The marketers who win will not be those who send more. They will be those who earn more—more attention, more intention, more repeat behaviour, more trust. That’s the heart of NeoMarketing. And 2026 is the year it stops being a vision and starts being a necessity.
Summary: The NeoMarketing Predictions
| # | Prediction | Core Shift |
| 1 | Alpha Pricing | From messages licensed to outcomes delivered |
| 2 | Artificial People | From segments to world models |
| 3 | NeoMails | From cost centre to attention surface |
| 4 | Goal-Based Ops | From journey design to objective setting |
| 5 | Retention KPIs | From ROAS to lifetime value |
| 6 | Earned Relevance | From content volume to timing + restraint |
| 7 | Answerability | From SEO to agent optimisation |
| 8 | Customer Model Moat | From tool stack to understanding layer |
