Adopting AI in the Enterprise Is Like Hiring an Employee

Adopting AI in the enterprise is like hiring an employee

Artificial intelligence has quickly penetrated the market and is finding its way into everyday enterprise operations. Many organizations suffering from FOMO (fear of missing out), and afraid they will lose market share from competition that has figured out a better way to use the technology, are rushing to implement the technology themselves. Because of this, AI is now reshaping industries, functions and even the structure of organizations themselves. Yet despite its pervasiveness, many business leaders struggle to frame AI adoption in terms that resonate with familiar strategic concerns like hiring, performance, culture and management.

One of the most effective metaphors is to think of AI not merely as a tool but as a worker. Like people, AI systems must be recruited, evaluated, managed and integrated into a workforce and an organization's workflow. They consume resources, deliver outputs and carry risks. Some AIs will be analogous to temporary contributors, others behave more like contractors with specialized skills, and some are long-term employees who hold institutional knowledge. In time, some AIs will even act as managers, orchestrating the work of other AIs and possibly even their human coworkers.

This workforce metaphor clarifies decisions around investment, governance and strategic alignment. It forces enterprises to ask familiar questions: Is this worker worth the cost? Should I trust them with sensitive information? Do I want to rely on them long term? By reframing AI adoption as a workforce planning exercise, organizations can apply proven management principles to an emerging technological reality.

The TLDR version: In this article, we explore six analogies:

1. AI must earn its wage.
2. Off-the-shelf AI resembles temporary workers. 
3. SaaS AI functions like contractors. 
4. Sovereign AI is equivalent to full-time employees.
5. Enterprises will inevitably employ multiple AIs, as they do with people.
6. Some AIs will act as managers, orchestrating the work of others.

AI must earn its wage

No enterprise hires employees without expecting value in return. Salaries, benefits and training are costs that must be justified by performance. AI should be treated no differently.

The cost of deploying AI is not simply licensing fees. It includes infrastructure (servers, storage, compute cycles), integration (APIs, workflow adjustments), and human training (so employees know how to use it effectively). Just as payroll is one of the largest expenses in any company, AI represents a new line item of recurring cost. Enterprises must therefore treat AI adoption as an investment that must generate measurable ROI.

Consider how employees are evaluated. New hires often go through a probationary period, during which their performance is carefully monitored. They may be assigned limited responsibilities while managers determine whether they are capable and trustworthy. Enterprises do the same with AI through pilot programs. A generative AI tool might be tested with marketing copy, or a predictive model might be trialed on a limited dataset. Only after proving effectiveness does the AI get "promoted" into wider use.

If AI adoption is framed this way, leaders will avoid the temptation to chase hype or adopt systems simply because competitors are doing so. Instead, they can apply a familiar discipline: What value does this worker bring? How do we measure their contribution? And what happens if they don't deliver? In other words, AI must earn its wage just like everyone else.

Off-the-shelf or public AI: The temp worker

Public AI models—such as ChatGPT, Gemini, or Claude—are analogous to temporary staff. They are easy to hire, flexible and inexpensive compared to long-term alternatives. A company that needs help generating ideas, summarizing documents or creating simple prototypes can "hire" a public AI in minutes. No contracts, no infrastructure, no deep onboarding.

But as every workforce leader knows, temp workers have limitations. They don't understand the company's culture or systems. They probably should not be trusted with confidential files. Their relationship to the organization is shallow: they show up, they perform a task and they leave. Public AI behaves the same way. It produces outputs but has no organizational loyalty, no memory of your company's history, no alignment with your policies and no guarantee of data security.

The risks are real. Sharing sensitive information with a public AI can be like asking a temp worker to handle payroll data or strategic plans. There is no assurance that information won't be leaked, reused or exposed. And since public AIs are available to everyone—including competitors—there is no exclusivity. You are hiring from the same pool as the rest of the world.

Still, temp workers serve a purpose. They can fill gaps quickly, provide surge capacity and deliver value at a fraction of the cost of a permanent hire. Likewise, public AIs are excellent for brainstorming, experimenting or rapidly testing ideas. Enterprises just need to treat them as temps: valuable, but not to be trusted with the keys to the kingdom.

SaaS AI: The contractor

If public AI is a temp, then SaaS AI is a contractor. Contractors bring specialized skills and integrate more deeply with company operations, but they remain outsiders governed by contracts.

Think of Microsoft Copilot or Salesforce Einstein. These AIs connect directly to enterprise data: emails, calendars, customer records, sales pipelines. They are domain experts, like consultants hired to bring niche expertise. The value is immediate: Copilot can summarize meetings, draft reports and help with productivity tasks; Einstein can analyze customer data and suggest actions for sales teams.

Yet, just as contractors never fully belong to the company, SaaS AIs remain under the vendor's control. The enterprise rents their skills but does not own the intellectual property. The contractor may leave, raise rates or change terms of service at any time. Companies that depend too heavily on them risk disruption if the vendor withdraws service or alters licensing.

This arrangement is not inherently bad. Contractors are often essential for complex projects that require expertise unavailable in-house. SaaS AIs deliver enormous value precisely because they come pre-trained, pre-integrated and ready to work. The key is balance: use contractors strategically, but don't rely on them for your most sensitive or mission-critical roles.

Sovereign or purpose-built AI: The full-time employee

At the highest level of commitment, enterprises can invest in sovereign AI—custom-built, in-house or purpose-designed systems. These are equivalent to full-time employees: costly to recruit and train, but deeply aligned, loyal and long-lasting.

Sovereign AI reflects the organization's data, policies and culture. It can be tuned for compliance requirements, built around proprietary workflows and trained exclusively on company datasets. This makes it trustworthy for sensitive industries like healthcare, finance and government, where data integrity and security are paramount.

Like employees, sovereign AIs require continuous development. Training data must be refreshed, models must be updated, and systems must be monitored for drift or bias. They need "professional development" in the form of updates and retraining. But the payoff is enormous: a sovereign AI becomes part of the institutional memory, a resource that grows more valuable with time.

Enterprises that invest in sovereign AI are making a statement of commitment, much as they do when hiring a full-time workforce rather than relying solely on temps or contractors. It signals an intention to build enduring capability rather than renting talent on demand.

Multiple employees = multiple AIs

No enterprise hires one employee to do everything. The workforce is built on specialization: marketing professionals, financial analysts, IT staff, customer service representatives. The same principle will apply to AI adoption.

Enterprises will not deploy a single all-purpose AI but a portfolio of specialized AIs. A marketing AI may generate campaign copy, while a supply chain AI predicts logistics bottlenecks. A customer service AI may handle chat support, while a cybersecurity AI monitors networks for threats. Each AI is a specialist contributing to the overall success of the organization.

This division of labor offers both efficiency and resilience. It prevents over-reliance on one system and allows organizations to match the right AI to the right task. It also encourages experimentation: different teams can test different AIs without jeopardizing the entire organization.

Over time, enterprises will come to see their AI workforce as a team, diverse in skills and responsibilities, much like their human workforce.

AI managers: Orchestrators of other AIs

As the AI workforce expands, enterprises will face a new challenge: coordination. Just as human employees need managers to align their efforts, enterprises will require AI managers—systems designed to orchestrate the work of other AIs.

These orchestration AIs will assign tasks, integrate outputs and ensure consistency across systems. Imagine a manager AI that receives a business question from leadership. It assigns part of the task to a financial forecasting AI, another to a supply chain AI and another to a customer sentiment AI. It then synthesizes the results into a single, coherent report.

This managerial layer mirrors human organizational hierarchies. Some AIs will be individual contributors; others will act as team leads or supervisors. Just as human managers require oversight, orchestration AIs will need rules and governance to ensure accountability. Enterprises will face new questions: Who manages the managers? How do we prevent bias or misalignment at the orchestration level?

The metaphor comes full circle here. Enterprises will not just employ AI—they will build entire organizational structures of AI, with workers, specialists and managers all contributing under human leadership.

Conclusion

Framing AI adoption as workforce planning brings clarity to a complex transformation. AI must earn its wage, proving ROI just like any employee. Public AI tools are like temps: fast, flexible, but limited in trust and integration. SaaS platforms are contractors: skilled, integrated, but governed by external agreements. Sovereign AI systems are full-time employees: costly but loyal, aligned, and long-term. Enterprises will employ multiple AIs, mirroring the division of labor among human staff. And as adoption scales, orchestration AIs will emerge as managers, coordinating and supervising other systems.

The workforce metaphor is more than clever imagery. It is a practical framework for leaders facing decisions about cost, risk, trust and strategy. The future of work will be hybrid—not only human and machine, but also layered, with AIs filling every role from temp to manager. Enterprises that thrive will not merely "use" AI; they will "employ" AI: recruiting, training, evaluating and governing their digital workforce with the same discipline they apply to their human one