By Emmanuel Benhamou (BGV) & Sam Levan (MadKudu)
Introduction
AI is no longer just a tool to improve productivity – it’s becoming a core component of the modern workforce. At BGV, we’ve seen firsthand how AI-native startups are restructuring operations, not by replacing human workers, but by expanding workforce efficiency through automation-first architectures and agentic workflows.
At MadKudu, this transformation is not theoretical – it’s happening in real time. Every team member, regardless of technical background, is responsible for recruiting, training, and managing their own AI agents. This marks a fundamental shift in how businesses think about human productivity:
- AI doesn’t just assist work – it executes work autonomously.
- Every employee, even in non-technical roles, actively manages AI.
- The workforce is transitioning from execution to orchestration.
This blog explores how AI-native companies like MadKudu are pioneering this transformation, what it means for businesses, and how companies can adopt similar principles to stay ahead in an AI-driven world.
The Shift from SaaS to Agentic AI: A New Playbook for Work
For decades, SaaS platforms helped businesses optimize tasks, but they still required human execution. AI-native companies, on the other hand, are built differently. Agentic AI doesn’t just enhance workflows – it takes over execution loops entirely.
At MadKudu, this shift is foundational to both internal operations and customer-facing solutions. As Sam Levan, CEO of MadKudu, explains, the company doesn’t just leverage AI as a tool – it runs on AI at every level:
“Every function, from customer success to marketing to sales ops, is powered by AI agents trained to execute work independently. Employees act as managers of automation, fine-tuning AI to improve efficiency over time.”
This evolution marks a clear distinction:
- SaaS enables employees to use software for productivity.
- Agentic AI redefines the role of the workforce, requiring employees to manage AI systems that autonomously execute tasks.
Rather than relying on traditional headcount expansion, AI-native companies structure workforces where execution capacity increases as AI systems improve, rather than as employee numbers grow.
AI as a Team Member: How MadKudu Structures Its AI Workforce
At MadKudu, every employee is responsible for managing AI agents, a departure from conventional organizational structures where automation is siloed in technical teams. This model changes the way work is distributed and executed. Employees are no longer simply using AI-powered tools; they are directly responsible for overseeing AI agents that perform specialized tasks.
By embedding AI deeply into operations, MadKudu ensures that automation enhances decision-making rather than replacing human insight. AI handles:
- Data processing to extract and analyze key insights.
- Workflow automation to eliminate repetitive tasks.
- Predictive analytics to anticipate market trends and customer behaviors.
The result is a seamless human-AI collaboration, where AI continuously improves execution while employees refine and guide its decision-making processes.
Aligning AI with Core Business Needs: The Revenue Impact
For companies integrating AI at the workforce level, the real question isn’t just about efficiency – it’s about business impact. AI must contribute to growth, not just reduce costs.
MadKudu’s customers, primarily CROs and sales leaders, focus on two primary metrics: whether they will hit their revenue targets and how strong their pipeline is. “Our customers care about efficiency, but what matters most is business outcomes,” Levan explains. AI must drive tangible financial results, not just streamline processes. While automation reduces operational overhead, its real value lies in enabling employees to work on higher-value initiatives, making organizations more competitive.
This is why AI integration isn’t just about automation – it’s about ensuring that automation directly supports core business objectives. AI-native companies understand that success isn’t measured by how many tasks can be automated, but by how AI helps organizations generate more revenue, improve service quality, and outcompete their peers.
AI Integration: Embedding Intelligence into Business Workflows
A major reason AI adoption stalls in enterprises is friction. AI tools often operate outside core workflows, requiring users to switch contexts or adopt new systems.
MadKudu solves this problem by embedding AI-driven insights directly into existing platforms like Salesforce and Gong. This ensures that AI seamlessly integrates into the daily workflows of employees, rather than requiring them to adapt to new systems. “Our agents don’t live in a separate app,” Levan notes. “They are embedded into Salesforce, Gong, Outreach – so they feel like natural extensions of the tools customers already use.”
By eliminating friction in AI adoption, this approach accelerates business impact. Users engage with AI passively, leveraging insights and automation without needing extensive training or behavioral shifts. The best AI solutions don’t demand attention – they deliver results without disrupting workflows.
Tackling AI Infrastructure Costs: The Economics of Scale
One of the biggest challenges AI-native companies face is managing compute costs as they scale. Unlike SaaS, where software costs are relatively predictable, AI infrastructure costs can fluctuate based on model complexity and execution volume.
To address this, MadKudu continuously optimizes its infrastructure by:
- Selecting cost-efficient AI models that balance performance and expenses.
- Routing tasks to the most appropriate AI systems to maximize efficiency.
- Diversifying compute providers beyond AWS, including platforms like Groq.
Levan emphasizes that this shift has changed how software businesses think about costs:
“For the first time in years, we’re talking about cost of goods sold (COGS) in software again. AI is expensive, and companies must be strategic in managing compute infrastructure.”
These optimizations are essential for AI-native startups to maintain competitive unit economics. As AI technology evolves, companies that actively manage compute efficiency will maintain an advantage over competitors who fail to account for these costs.
The Future of AI-Driven Work: Challenges & Opportunities
While AI-native companies present massive opportunities, they also introduce fundamental challenges that businesses must navigate:
- Trust & Transparency: Employees and customers need clear insights into how AI reaches decisions.
- Pricing Model Shifts: AI-driven solutions are moving toward usage-based pricing, requiring companies to balance predictability and scalability.
- Human-AI Role Evolution: Employees must transition from executing tasks to managing AI, requiring companies to invest in training and upskilling programs.
Levan emphasizes that AI does not simply eliminate work – it shifts human focus to higher-value activities. “Markets are competitive. If your sellers become much better with AI, it’s not that they have more free time – it’s that they provide more value and service.” Businesses that recognize this shift and act accordingly will be well-positioned to lead in the AI-driven economy.
Conclusion: AI as a Force Multiplier for the Workforce
The rise of Agentic AI is not about replacing people – it’s about scaling execution and unlocking human potential. Companies like MadKudu are proving that AI can serve as an extension of the workforce, amplifying productivity while keeping people at the center of decision-making.
Businesses that successfully integrate AI into their workforce will outpace competitors, drive more value for customers, and fundamentally reshape what workplace efficiency looks like. The opportunity isn’t just to automate tasks – it’s to elevate work itself, enabling employees to focus on strategy, creativity, and high-impact initiatives.
For leaders, the challenge is clear: embrace this shift, redefine roles, and build a workforce where AI isn’t just a tool, but a fundamental driver of business success. Those who act now will define the future of AI-driven execution, leading the market rather than adapting to it.
