Topic
Enterprise context
Connectors, plugins, skills, and the decision model behind them.
The Long Lake playbook
A three-year-old AI holding company just closed a 6.3 billion dollar take-private of Amex GBT. How the model works, what it claims, what is still unproven, and what private equity should take from it.
The credit manager's AI playbook
Where AI earns its place across a credit manager, from origination and monitoring to CLO operations and the wealth channel, and what has to be true first.
Beyond cost: AI as a revenue engine
Most portfolio AI plans are cost plans. The larger and more durable opportunity is revenue: capacity that turns into growth, new AI products and services, and new ways to price.
Scaling AI across a portfolio
Most portfolio AI programs can make one company work. Few make the tenth company cheaper than the first. What to share across a portfolio, what each company must keep, and who runs it after the builders leave.
The deskless majority
Most of the workforce has never had a keyboard, a login, or an hour to spare for training. The interface problem that kept them off enterprise software is the one this generation of AI removes.
Where AI helps across a university
A guide to the work AI genuinely helps with today, area by area, from admissions and advising through research administration and accreditation, and what has to be true for it to help.
No two institutions start in the same place
Higher education is covered as one market with one story. Five kinds of institution, what leadership in each is actually working on, and where AI tends to help first.
Your advantage is already on campus
Access to capable AI is now ordinary, which moves the question from what an institution can buy to what it already has. Four things a university holds that nobody can copy, and how each becomes a system.
What forward deployed means
A job title that was one company's internal jargon three years ago is now the most contested role in AI. What it actually is, why it came back, and what it should leave behind.
What to build, and what to buy
Enterprises are buying most of their AI and worrying about what they bought. The build-or-buy line does not run between layers of the stack. It runs through every one of them.
What an ontology is for
The word has escaped into every enterprise AI pitch. Here is what it actually means, why agents made it urgent, and how it becomes the foundation of an AI transformation.
Agents need your decision model, not just your data
Retrieval gives an agent what the company knows. A decision model gives it how the company decides, and that is what lets it act.
Meet the work where it happens
Fig is not religious about form factor. Slack or Teams, the mobile app or WhatsApp, desktop or web: the AI fits how your organization operates.
Fig Connectors: Your AI with Context
Connectors give Fig the context of your actual work: the files, tools, and systems your team already uses, with permissions intact.
Fig Expert Packages and Skills
Expert packages and skills give Fig a head start on your work: packaged expertise, reusable methods, and a library that grows with your team.