The news: Epic rolled out new genAI tools for clinicians, including an AI scribe solution that transcribes doctors’ notes during patient visits. Epic will incorporate ambient technology from Microsoft to power its medical documentation technology. Our take: Epic’s AI scribe solution with Microsoft/Nuance as its development partner delivers a major blow to startups like Abridge and Ambience. These two companies are part of a booming ambient AI scribe space that has totaled nearly $1 billion in investment funding so far this year, per a July analysis from STAT. But Epic’s presence will make it much tougher for smaller players to stand out in the category, since doctors will be drawn in by the efficiency of using scribe tools from their EHR system.

The news: Publishers are tackling AI scraping with a new strategy—pay per crawl. Rather than one-time licensing deals, usage-based compensation models would have AI companies pay publishers and content providers based on how often their work is used in AI-generated responses. Our take: These usage-based models could be a more equitable deal for publishers whose content powers AI engines that are earning tens or hundreds of millions of dollars per year. To avoid getting locked out of monetization, brands should act now to review existing content agreements, explore licensing opportunities, and push for fairer models that recognize the value of original content.

The news: Many marketers and salespeople doubt AI’s ability to boost company revenues or customer satisfaction. Some even believe it adds to their workload, signaling a disconnect between AI adoption and employee confidence. Only 39% of marketers and sales professionals in the US and UK are confident that their departments’ use of AI drives revenues, per General Assembly’s AI in Marketing & Sales report. Nearly half (46%) believe AI only somewhat improves the customer experience or doesn’t at all. Our take: Organizations that prioritize tailored training and tie outcomes to KPIs like team efficiency and customer satisfaction could help employees feel empowered and translate AI investments into measurable impact.

The news: CEO Mark Zuckerberg has reorganized Meta Superintelligence Labs (MSL) into four units focused on research, superintelligence, products, and infrastructure, per The New York Times. Meta further splitting its AI division, which it spun off in June, underscores both ambition and internal turmoil as it races rivals like OpenAI and Google. Our take: Meta’s public growing pains show it won’t sit out the AI race, even if upheaval is the cost. Its future direction will have wider implications—if Meta leans into closed AI models, the shift could reshape how outside developers and partners interact with its platforms. For advertisers, the signal is clear: Expect fresh AI features in Meta’s ad products, but brace for volatility as Meta struggles to align its people, platforms, and technology.

The news: Meta is moving forward with its ad automation ambitions by introducing new options to consolidate ad targeting, per a company announcement. Meta’s Ads Manager page noted that “some detailed targeting options have been combined,” and that ads using now-unavailable options no longer deliver starting in January. Our take: Automated AI campaigns are the path forward as long as giants like Meta continue pushing for automation and away from manual—necessitating advertisers take key steps to adapt. Campaign goals must be reframed for an AI-first environment.

Generative AI is rapidly moving from novelty to necessity in advertising, collapsing production costs and timelines while expanding creative possibilities. National TV ads that once required six figures and weeks of work can now be made in days for a fraction of the budget, opening broadcast-quality campaigns to smaller advertisers. With nearly 90% of large video advertisers already adopting AI, use cases like personalization, ideation, and versioning are proliferating. Yet consumer skepticism remains strong—especially among older audiences—underscoring that human craft and cultural nuance still matter. The challenge ahead: merging automation’s efficiency with trust and authentic creativity at scale.

The news: Google Ads is ending manual language targeting, taking over a significant element of campaign management. In lieu of manual targeting, Google’s AI will detect user language automatically using signals such as language settings and historic search activity. Our take: Brands should consider auditing current campaigns to identify where automated language detection might create gaps and establish safeguards, such as breaking out campaigns by region or market and including clear, native-language text in headlines and descriptions to signal intended language to both users and Google’s systems.