I am convinced that agentic orchestration (the kind that actually helps individuals and teams be more productive) will happen at scale on the desktop and not in the cloud (look no further than StationOne as a reference example). This prediction is based on a few key observations:
Every major brand is signing enterprise deals with one or more of the major LLM Foundation Model Providers. This means that AI tooling that can leverage the master agreement model API's, as opposed to using the vendors own model instances, will be preferred or even mandated by the organization.
Every major brand has governance requirements on how data can be used in AI applications. The implication of this is both a rise in Shadow AI projects that are going to happen on localized environments (on machines) and a rise in AI solutions that have a de minimis amount of compliance hoops to get through for approval (using API keys already approved by brands and running on machines of employees and not via a third-party SaaS tool).
People, those that are at the heart of existing workflows, will be the first to be empowered to extend their productivity by authoring agentic tools to make their lives better. They will integrate the tools they use with the models they're allowed to use, to produce better and faster results.
We believe that the above describes what we call 'Integrative AI' as it brings together the resources that people currently use, with models that they're allowed to use, to produce better productivity results through consistent team-based prompting templates. StationOne is a perfect example of this and our top prediction is that this category (Integrative AI) will see major growth and will be the primary productivity use-case of AI in the enterprise.
As an extension of the above, with at-scale access to Integrative AI, individuals are going to be producing far more work-product than ever before. The result is that human participation in the creative process will be repositioned as a primary area of time spent vs execution of time-consuming repetitive tasks. Instead of looking for new ways to implement AI, 2026 will see humans collaborating with agentic tools (individually and as teams) to improve efficiency and complete tasks faster and with consistent excellence. This will be manifested in experiences that combine teams of humans and agents, working together, to deliver results.
The whole industry will be busy re-creating the tools that we have relied on from the past, using next-generation agentic tooling of today. Whether it's ad buying, creative optimization, audience targeting, or measurement, the whole industry will re-invent, re-architect and re-deploy systems using agentic technology approaches to transform the business of digital advertising with the new agentic approaches that are available today. AdCP, Agentic RTB Framework and other related initiatives are good examples of these new approaches. Hundreds of companies will spend 2026 re-conforming their products and services around these and other technology approaches while also trying to show their own relevance and value through their ability to interoperate.
Ads are soon to enter AI chat with OpenAI already announcing their plans earlier in 2025. But these will look different to traditional search ads. OpenAI will retain control over performance, measurement and monetization. Other platforms are sure to follow suit.
Whether these initiatives are called 'Ads', 'Offers', or 'Integrated Commerce' is yet to be seen, but the ad monetized approach of LLM driven top-of-funnel discovery to bottom-of-funnel outcomes will become solidified in 2026.
Data privacy regulation has been a major focus over the last 5 years globally. Despite this, the US federal government has yet to establish a legislative mandate around privacy and end-user consent related to advertising and personalization. This has led to an enforcement strategy that is subjective at best, leveraging a patchwork of state legislation and litigation (or threat of litigation) by State AG's and the FTC using unprecedented subjective harm claims to Section 5 violations.
Not much better outside of the US, major voices in the privacy field have concluded that GDPR is a tax to, and not a protection of, European citizens and wielding the related regulations as more a means to profit from and block advancements of US based companies rather than truly protecting end-user privacy in the region.
In short, after all of the discussion of the importance of Privacy and the reference to privacy as a political flogging tool, no federal law has been created and no executive order established by the executive branch.
For the first time ever, the preponderance of data that will be used and applied, will be from large language models (LLM's). These models are trained on data where the source data isn't easily traceable and the notion of 'opt-out' for end-user consent isn't really a viable concept. In contrast, if a user opts-out of marketing tracking on a website, a database record can be updated by the publisher of that website. If a user wants to be 'forgotten about' in an LLM, a model must be entirely re-built. Operationally this isn't viable.
Add to this the reality that despite no federal framework having ever been created for privacy protection (despite all of the fanfare by politicians and pundits alike), a federal framework has been created to protect AI companies through a recent executive order which limits states' ability to restrict AI companies and policies outside of stated federal objectives.
It is harder than ever to regulate technology solutions for transparency, privacy and consent in a world where LLM's are the source of data, and where huge volumes of data are combined together to produce vector graphs (the framework upon which LLM's are formed). It's even harder if no federal policy on privacy has been created, but where a federal AI mandate has been put in place via executive order.
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