What if the biggest AI transformation isn’t about making humans better at their jobs, but completely reimagining what jobs should look like?

SaaStr’s Founder, Jason Lemkin, highlights insights from David Boskovic, CEO of Flatfile, a leader in deploying AI agents for Fortune 500 companies in highly sensitive data environments. Boskovic’s work with mission-critical enterprise data gives him a rare, unfiltered view of AI’s real-world performance not just in theory, but in high-stakes situations where mistakes are unacceptable.

In his perspective, AI isn’t simply a tool to make workflows faster. It’s a force reshaping roles, organizational design, and the very definition of “work.” Many traditional functions are proving to be a better fit for AI than humans, challenging enterprises to rethink team structures from the ground up.

The Three Paths to AI Integration and Why Two Fall Short

Boskovic observes that most enterprise AI adoption strategies fall into three categories:

1. Tool-Centric Implementation Adding AI features to existing workflows without rethinking processes.
2. Process-Centric Implementation Attempting to map AI capabilities onto current human-driven systems.
3. Role-Centric Implementation Designing entirely new workflows and roles that fully leverage AI strengths.

The first two approaches often underdeliver because they treat AI as a bolt-on solution, not a structural shift. Role-centric integration, though harder to execute, delivers the most transformative results.

Lessons from the Space Industry: New Markets Through Cost Reduction

Boskovic draws an economic parallel to the space industry, where drastic cost reductions—driven by technology didn’t just make existing missions cheaper; they created entirely new industries. Similarly, AI-driven cost and time efficiencies open the door for enterprises to pursue opportunities that were once unimaginable.

A Content Team Case Study: Humans as Directors, AI as Executors

In one Flatfile-led AI integration, a content team restructured its workflow so AI agents handled execution-heavy tasks like drafting, formatting, and distribution. Human team members shifted focus to direction, creativity, and quality assurance.

The result:

• Faster production cycles
• Greater emphasis on creative vision
• Reduced burnout from repetitive work

New Organizational Roles Emerging with AI

Boskovic predicts that enterprises will see new job categories emerge as AI becomes deeply embedded. These include:

• AI Operations Specialists: Bridge AI capabilities with business objectives.
• Prompt and Process Designers: Craft instructions and workflows that maximize AI output quality.
• Ethics and Risk Managers: Focus on responsible AI use, compliance, and governance.

Deployment Realities: Timing and Technical Challenges

Boskovic cautions that AI rollouts are far from plug-and-play. Even with cutting-edge models, challenges persist:

• Model Inconsistencies: Output quality can vary, creating trust gaps.
• Timing Gaps: Organizational readiness often lags behind technical capabilities.
• Cultural Shifts: Teams must adapt to AI-first workflows, which requires significant change management.

The New Value of Skills in an AI-First World

As AI takes over repetitive work, the market value of human skills is shifting:

• Routine Work: Decreasing in value as automation handles execution.
• Judgment-Based Work: Increasingly premium, as human intuition, strategy, and creativity remain irreplaceable.

Observations from Fortune 500 Rollouts: Who Thrives and Who Struggles

Boskovic’s deployments reveal clear patterns in employee adaptation:

• Thrivers: Curious, adaptable employees who see AI as a collaborator.
• Strugglers: Those who rely heavily on routine work or resist changing workflows.

Enterprises that invest in training and change management help more employees transition successfully, but leadership must recognize that resistance is natural and plan for it.

Why Boskovic’s Perspective Stands Out

Unlike futurists offering broad predictions, Boskovic speaks from experience in active enterprise environments. His insights are grounded in:

• Direct Experience: Hundreds of AI deployments with real stakes.
• Dual Perspective: Implementing AI for clients while scaling Flatfile internally.
• Practical Lessons: Sharing what doesn’t work as openly as what does.

The conversation around AI in the workplace is often theoretical. Boskovic’s approach offers a rare window into the experimental, messy, but deeply transformative reality of AI adoption at scale.

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