SODP Dispatch - 08 January 2026

18 Best Ad Networks for Publishers in 2026

By Vahe Arabian & Samuel Fatola

Ad revenue represents one of the three monetization pillars publishers have access to—with the others being subscriptions and affiliate marketing. As such, those publishers that have prioritized ad revenue need to ensure they pick the right ad network.

The global digital advertising market is projected to hit around $1.3 trillion by 2027, driven by factors such as the growing adoption of smartphones and the ongoing rollout of the Internet of Things (IoT).

The role of digital advertising remains pivotal to brand strategies, with research showing that around 50% of online users search for a product video before making a purchase.

The growth of digital advertising and how mobile ad networks work offers immense monetization opportunities for publishers that are in a position to capitalize. A key element of that positioning is the ad networks they choose.

When choosing an ad network, it's important to consider the ad formats available, the targeting options, the optimization tools and the revenue share.

📊 2026 News SEO Trends: 20 Global Experts Predict the Year Ahead | NewzDash

Last year we worried about AI. This year we’re living it: fewer clicks, more zero-click experiences, and visibility now happens across multiple surfaces, not just rankings. 2025 felt like the panic year. In 2026, the real work starts: adapting workflows, redefining what “success” looks like, and building a visibility strategy that holds up even when the SERP becomes an answer engine. This is no longer a single-channel game. “Search, Discover, video, and AI answers are not separate channels anymore, they’re different doors into the same building. If your trust signals crack in one, you feel it everywhere.” — John Shehata

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By Vahe Arabian

🤖 Tips on how to leverage Agentic AI for 2026

Right now, we're witnessing a pivotal moment in enterprise AI adoption. Agentic AI systems that can act autonomously to achieve goals is transitioning from experimental pilots to production-scale deployments. This shift represents more than just technological maturation; it's a fundamental change in how organisations operate.

Here's what leaders need to know about this transformation and how to prepare for scaled agentic AI deployment:

1) Infrastructure Readiness: The Foundation for Scale

Moving agentic AI from pilot to production requires robust infrastructure that most organizations don't yet have. Unlike traditional AI models that simply predict or classify, agentic systems need to interact with multiple tools, databases, and APIs while maintaining security and compliance.

👉 Actionable Tip: Audit your current infrastructure for API connectivity, authentication systems, and observability tools. Invest in orchestration platforms that can manage multi-step agent workflows and provide proper logging for compliance and debugging.

2) Trust and Governance Frameworks Become Critical

In pilot phases, companies can tolerate occasional errors. At scale, agentic AI systems making autonomous decisions require comprehensive governance frameworks. Organizations must define clear boundaries for agent autonomy, establish approval thresholds, and create rollback mechanisms.

👉 Actionable Tip: Develop a tiered autonomy model where agents have different permission levels based on risk. Implement human-in-the-loop checkpoints for high-stakes decisions and create transparent audit trails for all agent actions.

3) From Single-Purpose to Multi-Agent Orchestration

Early pilots typically focus on single-use cases—a customer service agent or a data analysis assistant. Production-scale deployment means coordinating multiple specialized agents that work together, each handling distinct tasks while sharing context and goals.

 👉 Actionable Tip: Design your agentic systems with modularity in mind. Create specialized agents for distinct functions (research, analysis, execution) that can collaborate through standardized protocols. This approach enables easier scaling and troubleshooting.

4) ROI Measurement Shifts from Proof-of-Concept to Business Impact

Pilot success is often measured by technical feasibility—"Does it work?" Production requires demonstrable business value: cost savings, revenue generation, or productivity gains that justify the investment.

👉 Actionable Tip: Establish clear KPIs before scaling. Track not just agent performance metrics (accuracy, response time) but business outcomes (customer satisfaction scores, operational cost reduction, time-to-resolution). Build attribution models that connect agent actions to business results.

5) Data Quality and Access Become the Bottleneck

Agentic AI systems are only as effective as the data they can access. At scale, data fragmentation, inconsistent formats, and access restrictions become major obstacles. Organizations discover that their biggest challenge isn't the AI, it's the data infrastructure.

👉 Actionable Tip: Prioritize data integration and standardization efforts. Implement semantic layers that give agents consistent access to enterprise data regardless of source. Consider investing in knowledge graph technologies that help agents understand relationships between data points.

6) Change Management: Preparing Teams for AI Collaboration

The shift to production-scale agentic AI fundamentally changes how teams work. Employees need to learn when to delegate to agents, how to verify agent outputs, and how to collaborate effectively with autonomous systems.

👉  Actionable Tip: Develop comprehensive training programs that go beyond tool usage to teach AI collaboration skills. Create clear escalation paths when agents encounter problems, and establish feedback loops where human users can improve agent performance over time.

7) Cost Management and Resource Optimization

Pilots run on limited budgets with predictable costs. Production-scale agentic AI can consume significant computational resources, especially when agents make multiple API calls or process large datasets autonomously. Runaway costs become a real risk.

👉  Actionable Tip: Implement cost guardrails and resource quotas for agent operations. Use caching strategies to reduce redundant API calls, and establish monitoring systems that alert teams to unusual resource consumption patterns before they impact budgets.

Organizations that succeed in scaling agentic AI will be those who:

• Build a robust infrastructure with proper security and observability

• Establish clear governance frameworks with appropriate autonomy boundaries

• Design for multi-agent collaboration rather than siloed solutions

• Focus on measurable business outcomes, not just technical capabilities

• Prioritize data quality and access as foundational requirements

• Invest in change management and team readiness

• Implement proactive cost management and resource optimization

The transition from pilot to production isn't just about technical scaling; it's about organizational readiness. Companies that approach this shift strategically, with attention to infrastructure, governance, and human factors, will unlock the transformative potential of agentic AI.

The question isn't whether agentic AI will move to production at scale; it's whether your organization will be ready when it does.

Journalism Leaders Predict 210 Major Trends for 2026 | NiemanLab

For 16 years now, we’ve asked smart people what they think will happen in the coming year in the world of journalism and digital media. And this year’s collection of predictions, published last month, is the biggest — and, I think, the best — yet. (I may be uniquely qualified to make that judgment, as I am certainly the only human to have read all 1,881 predictions we’ve ever published.) But I’m certainly aware that sorting through all those predictions — 210 this year — can be a lot. Every year, there are nuggets of gold that don’t get the attention I think they deserve, and it can be hard to determine a prediction’s topic from a headline alone.

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British News Publishers Face Brutal Rankings Collapse in December Google Update | PPC Land

The latest Google core algorithm update delivered harsh consequences for British news publishers, with more than two-thirds of major UK news websites losing search visibility between December 11 and December 29, 2025. Analysis from SEO analytics firm Sistrix revealed The Guardian experienced the steepest decline, dropping 30 points in its visibility score to 228.9, while The Telegraph fell 19 points to 43.9 and The New York Times decreased 12 points to 53.3. The December 2025 core update began rolling out at 9:25 AM Pacific Time on December 11, according to the Google Search Status Dashboard.

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CES 2026: Ad Tech Industry Enters Year of Consolidation with AI Focus | Digiday

Historically, the Consumer Electronics Show was the January kick-off event where media buyers could eye the hardware onto which they could festoon their clients’ wares. Manufacturers of household goods took center stage. However, now the lines of the traditional business calendar have blurred, making it difficult to delineate between industry verticals. Sources characterized CES, which officially kicks off today (Jan. 6), as the precursor to the mid-year upfront discussions, to AdExchanger.

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Being Right Isn’t Enough for AI Visibility Today | Duane Forrester Decodes

When most people hear the phrase “AI bias,” their mind jumps to ethics, politics, or fairness. They think about whether systems lean left or right, whether certain groups are represented properly, or whether models reflect human prejudice. That conversation matters. But it is not the conversation reshaping search, visibility, and digital work right now. The bias that is quietly changing outcomes is not ideological. It is structural, and operational. It emerges from how AI systems are built, trained, how they retrieve and weight information, and how they are rewarded. It exists even when everyone involved is acting in good faith.

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Web rot rising | Axios

Traffic to top websites has fallen by more than 11% in the past five years, according to data from Similarweb — a clear sign of the challenges traditional publishers face in the AI era. Why it matters: Internet usage and adoption continue to grow, but older websites are struggling to keep up as newer AI-driven experiences start to dominate user attention. Data shows that those older sites don't just magically disappear. They continue to rot on the open web for years, clouding search results and leaving behind trails of broken or outdated links. By the numbers: Taking a look at aggregate web traffic globally to the top 1,000 websites — including newer ones — internet traffic has held steady over the past five years at around 300 billion average monthly web visits, per Similarweb.

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