AI Management Emerges as Next Frontier as Bika.ai Seeks to Redefine "Digital...

As artificial intelligence continues to reshape industries, OpenAI’s framework for artificial general intelligence (AGI) development has provided a roadmap for progress—spanning five model levels: Chatbots, Reasoners, Agents, Innovators, and Organizers.

Today’s most advanced AI Agents can already decompose large goals into sub-tasks, acquire knowledge autonomously, coordinate resources, and execute tasks independently. These systems are evolving beyond static tools to become what experts increasingly describe as “digital labor”—entities capable of replacing humans in defined professional functions.

By OpenAI’s scale, most AI Agents now sit between the second and third stages of development. As they move from collaborative assistants to independent workers, a new challenge emerges: how to manage a growing workforce of intelligent Agents, and how to foster collaboration between humans and AI systems—an issue that may define the next phase of the AI economy.

“The AI industry is quietly shifting from a competition in productivity to a restructuring of production relations,” said Chen Peilin, founder and CEO of Bika.ai.

Falling computing costs and the rapid rise in reasoning capability of large models have fundamentally altered the logic of AI application. With the rise of AI Agents, individuals can now deploy computing power as “digital labor” with far fewer barriers to entry.

Yet most AI Agents still operate in silos, competing rather than collaborating. This fragmentation leads to inefficiencies such as duplicated effort, unquantified labor value, and operational distortions caused by model bias. The central question, Chen said, is no longer how powerful AI can become, but how intelligently AI systems can work together.

“How should thousands of intelligent Agents collaborate? How can we measure their output? And how will humans fit into this new labor ecosystem?” Chen asked. “We are now entering an age where managing AI becomes as important as building it.”

The Rise of the “Intelligent Manager”

Chen compares today’s stage of AI development to the early industrial revolution, when factories first introduced machines and assembly-line workers but lacked foremen to coordinate production.

“Everyone focused on making the machines faster and workers more skilled—but without a foreman, productivity couldn’t scale,” Chen said. “Today’s AI industry faces the same problem. As Agents become the mainstream workforce, the ability to manage them effectively will determine who wins.”

Bika.ai aims to fill that role. The company’s flagship platform functions as an AI management tool designed to organize, coordinate, and measure the work of AI Agents—essentially serving as a digital “manager” that oversees virtual employees.

Bika.ai’s approach redefines the relationship between humans, AI, and the intelligent systems they supervise. The company has already raised tens of millions of U.S. dollars from leading venture capital investors, underscoring growing investor confidence in what Chen calls the field of “AI management science.”

When AI ceases to be merely a tool and becomes a workforce, it requires a new management framework. Bika.ai seeks to provide that foundation.

In this model, AI Agents are treated as “team members” capable of handling specific roles—market research, data analysis, design, or customer outreach—within a standardized workflow. Users can set high-level goals, while Bika.ai breaks them down, delegates sub-tasks to appropriate Agents, and tracks progress to completion.

“An individual using Bika.ai can command the productivity of a small company,” Chen said. For instance, a designer can direct a group of AI Agents to conduct market research, draft copy, and create visuals—reducing their own work to final refinements.

The company has integrated over 5,000 Master Content Providers (MCPs) through its ToolSDK.ai ecosystem, spanning tools from email and payments to CRM and cloud applications. More than 100 industry-specific templates—from marketing and sales to content operations—serve as ready-made playbooks for AI teams.

“These templates replicate the value-creation logic of real companies,” Chen said. “They allow users to orchestrate entire business processes, not just isolated tasks.”

Quantifying AI Labor

As AI adoption accelerates, enterprises face a new challenge: how to quantify and compensate the output of non-human workers.

According to McKinsey & Co., between 20% and 40% of global jobs—representing a payroll market worth over $60 trillion—could eventually be replaced or supplemented by AI Agents.

Current models often charge per task (for example, a small fee for generating copy). But Chen argues this approach ignores the complexity and quality variation of AI labor. “There’s no fair way to compare an AI that completes a detailed analysis in 30 minutes with one that produces a vague summary in two hours,” he said.

Bika.ai instead adopts a subscription-based pricing model, billing according to the number of “human seats” and usage levels. This makes performance more measurable and transparent, enabling fairer allocation of wages and encouraging developers to improve efficiency.

Chen predicts that a standardized pricing mechanism—similar to a “human position salary system”—will gradually emerge, forming the foundation for a trillion-dollar AI payroll market. In this future, the competitive advantage will shift from execution to AI management capability.

Managing AI at scale also raises concerns about privacy and data security, especially when multiple Agents collaborate across sensitive information domains.

To address these issues, Bika.ai employs sandbox isolation, ensuring each Agent operates within its own protected environment—akin to a virtual machine—preventing cross-data leaks. Users can also customize models before task assignment, tailoring access permissions to their specific needs.

“The integrity of AI collaboration depends on technical safeguards,” Chen said. “We’re building the infrastructure to make sure Agents can work together without compromising data boundaries.”

Bika.ai’s architecture runs on Amazon Web Services (AWS), which provides the global computing backbone for its multi-agent system. The company leverages AWS’s serverless architecture (Lambda) to dynamically execute and orchestrate workflows without pre-provisioned servers, optimizing both scalability and cost.

By integrating services such as Amazon RDS, DynamoDB, and S3, Bika.ai supports CRM and ERP applications handling millions of records. The firm’s AgentCore Browser Tool further enables large-scale, low-latency web interactions in a fully managed environment.

“AWS brings both technical depth and compliance maturity,” Chen said. “Its global infrastructure and regulatory certifications—from GDPR in Europe to CCPA in the U.S.—help us expand securely across markets.”

Toward the Age of AI Management

As AI workforce systems mature, the paradigm is shifting. AI is no longer a competitor to humans, but a collaborator that amplifies human potential.

The “intelligent manager” operates quietly in the background, distributing tasks, allocating resources, and measuring performance—while users focus solely on defining their goals.

“The next trillion-dollar opportunity will not come from building smarter models, but from managing them smarter,” Chen said.

Bika.ai plans to continue expanding its Agent Store, where users can deploy specialized AI workers for industry-specific needs. The company’s ultimate ambition, Chen added, is to make AI management infrastructure as ubiquitous as enterprise software—quietly coordinating a global digital workforce.

“In the future,” he said, “AI management won’t just reshape enterprises. It will redefine how value is created and distributed across society.”