Business Technology Roundtable

A strategic business case and global market assessment The enterprise artificial intelligence landscape is undergoing a profound structural transition, particularly with the Professional Services related industries. As AI foundation models commoditize basic text generation and public datasets reach cognitive density limits, forward-thinking organizations are realizing that generic intelligence offers no sustainable competitive advantage. In response, enterprise architects and strategic advisors are shifting focus toward architectures that capture, structure, and monetize internal institutional knowledge. Central to this structural pivot is the concept of the Proprietary Intelligence Foundry. The foundry represents a strategic pipeline framework designed to transform an enterprise's undocumented human judgment into a permanent, defensible, and sovereign digital asset. Conceptual Foundations The Proprietary Intelligence Foundry is defined as a strategic pipeline architecture that cap...

In June, I wrote about why AI training had moved inside the enterprise : the open web has run dry of the cognitive density frontier models now need, and the next phase of the Applied-AI race would be won on access to human tacit knowledge, not public data. I said the leading tech vendors had already restructured to capture it. What I didn't have yet was a single, undeniable proof point of a professional services firm making the same bet with its own money. Now I do. Kirkland & Ellis, the world's highest-grossing law firm, is spending roughly $500 million of its own revenue over the next three to four years building proprietary AI systems it will own outright. A meaningful piece of that is a co-built platform with Palantir called the Fund Formation Engine, designed to run private-equity fund documentation, side-letter drafting, and obligation tracking across Kirkland's fund-formation practice. Kirkland didn't pick Palantir for its model. It picked Palantir for its on...

Google just published the most detailed empirical picture yet of how Generative AI is actually being used across the global economy, and it should reset how executive teams talk about AI transformation. The AI & Economy ATLAS study, built on 15 million de-identified interactions across the Gemini App, Google AI Mode, and the Gemini API, maps usage against more than 800 occupations, 4,000 work tasks, and 150 countries. The headline finding is not that Applied-AI is transforming work. It is that AI has diffused everywhere while penetrating almost nowhere near as deeply as the enterprise boardroom narrative suggests. The AI Coverage Illusion Gemini usage now touches 68 percent of detailed occupations, covering occupations that represent 88.4 percent of total U.S. employment. That is the statistic executives will quote. It is also the one most likely to mislead them. Among occupations with any measurable AI usage, the median worker is applying AI to just 21 percent of their constituen...

The Applied-AI Requirement, Seen from My Own Desk A few months ago, I began evaluating a paid license from some of the leading AI providers on the assumption that my advisory work would eventually outgrow the free AI app tiers. It has not happened yet. Each time I approached the point of subscribing, a new free or entry-level release arrived that adequately covered what I actually needed: drafting, research synthesis, and editorial refinement.  My requirements were never exotic. They were representative of a large share of knowledge work, which is precisely the point. This is not a story about frugality. It is a story about an AI capability moving target. The capability that once justified a premium license a year ago is now embedded in the free tier of the same provider, or matched by a competitor's low-cost model. Independent benchmark trackers have shown the performance gap between open and proprietary models narrowing from double digits to less than one percentage point within ...

The public internet, long treated as an inexhaustible resource for training large language models, has run dry. Not in terms of raw volume, but in terms of the cognitive density that frontier AI now requires. Research I published through GeoActive Group's Applied-AI Initiative confirms what a growing number of senior researchers have quietly acknowledged: the next phase of the AI race is being won or lost on access to human tacit knowledge, and the leading tech vendors have already restructured their organizations to capture it. This is not an incremental refinement to existing AI training methodology. It is a wholesale reorientation of how the most resource-intensive companies in the world are deploying their most valuable internal asset: the unwritten reasoning of their best people. The Structural Bottleneck Driving This Shift Three converging constraints have forced this strategic pivot. First, models trained on generic web content have hit a reasoning ceiling. They perform ade...

Economic growth is fueled by strategic investment. Venture capital (VC) has quietly become one of the most consequential forces in the global networked economy, yet most C-suite leaders engage with it only at the margins. That needs to change. A landmark new report from the World Economic Forum and Stanford Graduate School of Business, released this month, offers a comprehensive and at times sobering assessment of where the VC industry stands and where it is headed. For senior executives navigating technology strategy, capital allocation, and competitive positioning, the findings carry direct implications. The Scale of the Opportunity and the Strain Beneath It VC assets under management have grown more than sixfold since 2008, reaching $3.4 trillion globally in 2025. Seven of the ten largest companies in the world by market capitalization, including Apple, NVIDIA, and Amazon, received venture backing in their early stages. Among U.S. public companies founded in the past 50 years, VC-ba...

To date, the dominant narrative around artificial intelligence (AI) in business was one of cautious optimism shadowed by disappointment. Organizations launched pilots, generated buzz, and then quietly shelved initiatives that failed to scale. That narrative is changing. The World Economic Forum (WEF) inaugural MINDS report, produced in collaboration with Accenture, offers one of the most comprehensive snapshots yet of what successful, real-world Applied-AI adoption actually looks like. The findings are instructive, occasionally surprising, and carry clear strategic lessons for any organization still searching for the bridge between experimentation and ROI impact. The Scale of What is Happening The MINDS program drew applications from over 30 countries spanning every major region, with participation cutting across industries from energy and healthcare to financial services and advanced manufacturing. Information technology (IT) accounted for nearly one-third of all submissions, but what...

The generative AI (GenAI) wave that began with ChatGPT's arrival in late 2022 has already started to feel like yesterday's story. A recent TBR research report on the Applied-AI and GenAI market landscape makes one thing clear: the industry is pivoting fast, and the companies that fail to adapt to agentic AI will find themselves playing catch-up in a market that rewards those who move decisively. For the uninitiated, agentic AI refers to systems that don't just respond to prompts but actively plan, execute, and iterate across complex multi-step workflows with minimal human intervention. This is no longer a futurist talking point. It is reshaping how enterprises think about automation, how IT service firms price their work, and how hyperscalers compete for the next trillion dollars in technology spending. A Market Growing at Breakneck Speed The numbers alone make a compelling case for attention. TBR estimates that combined AI and GenAI revenue across major hyperscalers, inclu...