However, instead of law firms sending junior associates over to their banking and institutional clients for six months, the artificial intelligence era is putting a new spin on how and why firms are embedding their own employees client-side.

This time, the professionals arriving on-site are not lawyers or management consultants. They are forward deployed engineers, or FDEs, technical specialists employed by AI vendors who embed directly within customer organizations to customize models, integrate enterprise systems, troubleshoot adoption barriers, and accelerate operational deployment.

As generative AI shifts from experimentation toward enterprise-scale implementation, the rise of FDEs reflects a growing recognition that buying AI software is the easy part. Making it work reliably inside complex organizations is something else entirely.

For chief financial officers, that can mean AI strategy is evolving from a procurement issue into an operational capability question.

See also: Tech Giants Just Made Every Business Their Business

The Human Layer Behind Enterprise AI

The rise of FDEs underscores a paradox emerging across the enterprise AI market. Despite the automation rhetoric surrounding AI, successful deployment remains human-intensive.

OpenAI announced Monday (May 11) that it plans to acquire applied AI consulting and engineering firm Tomoro, which will bring about 150 experienced FDEs and deployment specialists to OpenAI Deployment Company, a $4 billion partnership between OpenAI and 19 global investment firms, consultancies and system integrators. The company is designed to help the AI provider gain greater share in the winner-take-most enterprise AI market.

OpenAI rival Anthropic also announced last week the launch of its own new venture focused on selling AI tools to enterprise companies, in partnership with Goldman Sachs, investment firm Blackstone and private equity group Hellman & Friedman. The Anthropic initiative will help companies embed Anthropic’s Claude AI model into their businesses.

As FIS Head of Product Management, Payment Networks Mladen Vladic wrote in a new PYMNTS eBook, “AI Runs Payments. Governance Decides What Happens Next,” integration is key to ensuring effective AI governance.

Organizational readiness is the most cited barrier to AI adoption at large companies. More than 71% of executives at companies with at least $1 billion in yearly revenue named it as the chief limit on AI performance, according to research by PYMNTS Intelligence. Just 11% said the technology itself is the main obstacle.

After all, the underlying models are rapidly commoditizing. Multiple providers now offer highly capable generative AI systems. What increasingly separates winners from laggards is the ability to integrate those tools responsibly into repeatable workflows that employees actually use.

Read also: CFOs Turn to AI Harnesses as Agentic Capabilities Scale

Why Finance Teams Are Becoming AI’s First Enterprise Battleground

Historically, enterprise software transformations often began in marketing, customer service or IT. But AI’s early enterprise value proposition is landing squarely inside finance operations.

Accounts payable automation, procurement workflows, forecasting, treasury management, audit preparation, compliance reporting and FP&A analysis are all becoming prime candidates for AI augmentation. Finance organizations are rich in structured data, governed by repeatable processes, and under constant pressure to improve efficiency.

The PYMNTS Intelligence report “Smart Spending: How AI Is Transforming Financial Decision Making” found that more than 8 in 10 CFOs at large companies are either already using AI or considering adopting it.

Still, as AI pushes further into the back office, many CFOs are discovering that enterprise AI deployment is less analogous to installing SaaS software and more akin to undertaking a systems integration project layered atop organizational transformation. Technology vendors increasingly recognize this reality, which helps explain why some AI firms are staffing aggressively around deployment and customer engineering roles rather than relying exclusively on product-led adoption models.

The emergence of FDEs also reflects a broader shift in enterprise buying behavior. Companies no longer want merely a software license; they want operational outcomes. Vendors are being asked to share accountability for adoption, integration success and measurable ROI.

First, embedded engineers can shorten deployment timelines by addressing integration problems in real time. Second, they can improve employee adoption by tailoring AI systems to actual workflow needs rather than idealized product assumptions. Third, they may reduce governance risk by helping organizations establish controls around outputs, permissions and auditability from the outset.

Ultimately, FDEs may represent something larger than a staffing trend. They are evidence that enterprise AI still requires substantial institutional translation between software capability and operational execution.

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