How Scale AI is Pioneering the Future of Work

In the current AI platform shift, enterprises require deep customization that off-the-shelf software often lacks. Unlike mature SaaS platforms where functionality is a matter of configuration, today’s AI solutions must be built to augment or automate human work—processes that have never been codified in software before. This creates a gap that the “forward-deployed” model is uniquely positioned to fill, enabling companies to build bespoke solutions directly for enterprise needs.

The Lag in Enterprise AI Adoption

While consumer-facing AI like ChatGPT generated immense excitement, enterprise adoption has lagged by as much as 18 months. This delay isn’t due to a lack of model capabilities, but rather the practical hurdles of implementation:

• Change Management: Integrating AI into established workflows requires significant organizational shifts.
• Integrations: Connecting AI models to legacy systems and proprietary data sources is a major technical challenge.
• UX and Security: Enterprises need to figure out the right user experience paradigms, security protocols, and permissions for AI-powered tools.

Initially, enterprises were in a pilot and proof-of-concept phase. Now, they are moving to production and seeking to dramatically expand AI’s footprint across their businesses, tackling larger and more complex problems.

Vertical AI vs. The Custom Forward-Deployed Model

There are two primary approaches to bringing AI to the enterprise:

1. Vertical AI Products: These companies (e.g., Harvey, Sierra) offer productized solutions for a specific industry. The benefit is faster deployment and a standardized feature set.
2. Custom, Services-Led Solutions: This model, employed by Scale AI, focuses on building bespoke agents and applications tailored to a specific company’s unique needs.

For the largest Fortune 500 companies, the custom approach is often preferred. These organizations don’t want an “average” solution that their competitors also use. Their competitive advantage—their “secret sauce”—lies in unique data, distribution, and internal processes. The forward-deployed model allows them to embed this unique advantage directly into the AI systems they build, creating a durable moat.

The forward-deployed motion isn’t just about services; it’s a wedge to install durable software. The process involves solving the customer’s immediate problem with a significant custom component, and over time, productizing the repeatable elements back into a core platform.

“The key is that it should be the customization and a wedge into installing your software that is durable over time… The real value is, hey, you know, only 20% of the repeatable stuff exists today, but we’re going to go solve the problem of a Fortune 10 company. We’re going to do a ton of it custom.”

Building the Forward-Deployed Team

The forward-deployed model is more than just consultants who can code. The team’s mandate is to solve the customer’s problem end-to-end while identifying repeatable patterns to feed back into the core platform. At Scale, this team is composed of three distinct roles:

• Forward-Deployed Engineer: Handles full-stack development, data integrations, and infrastructure setup.
• Forward-Deployed Machine Learning Engineer: Focuses on building and evaluating agents, diving deep into data, and training models.
• Forward-Deployed Product Manager: Acts as the voice of the customer, defines requirements, and, most importantly, sets an ambitious, AI-native vision. They often function as the “chief product officer for AI” at the client company, pushing them to rethink processes from the ground up.

The ideal candidate for these roles is a “unicorn”—part PM, part founder, with deep technical and data expertise, executive presence, and a willingness to do the unglamorous work (the “schlep”) required to get a solution across the finish line.

Trading Margin for Moat: A Go-to-Market Strategy

The core thesis is that in this platform shift, companies should be willing to sacrifice short-term gross margins to build a long-term, defensible business.

• The Moat: The moat in AI is not the software itself, but is built on switching costs and data network effects. By embedding deeply into a customer’s critical workflows, a company becomes a system of record and a system of work. This generates new, proprietary data (e.g., standard operating procedures, evaluation data for agents) that becomes a valuable asset, making the solution increasingly difficult to replace.
• The Margin Trade-off: This deep, custom work is expensive and can lower traditional software margins. However, this investment is crucial for owning the most valuable layer of the new AI stack. Companies that over-optimize for high margins today risk being too superficial to build a lasting competitive advantage. They might fail to solve the customer’s entire problem, which includes messy integrations and legacy systems.

“Your job is not to build a product. Your job is to solve a problem. And that is what enterprises are going to expect. That is what’s going to unlock 10x bigger contracts.”

The Importance of Saying “No”

A critical, if counterintuitive, piece of advice for this era is that revenue teams should be saying “no” more often. With high demand for all things AI, it’s easy to get pulled into projects that don’t align with the core vision. The most dangerous mistake is to chase revenue that leads to a fragmented product and a weak strategic position. The best companies are disciplined, focusing only on customer engagements that compound their advantage and lead them toward building a durable, defensible platform.

• Tags:
• Go-to-Market
• Feature
• Enterprise AI
• AI Strategy
• Forward Deployed Engineering
• Services-Led Growth
• Scale AI