In the evolving world of AI monetization and how best to use AI in practical ways at your company, a fascinating trend is emerging: services businesses or AI software companies are charging per AI Full-Time Equivalent (FTE) hired. This model borrows heavily from traditional hiring frameworks by mapping the output of human employees and translating it into an AI offering, packaged and priced as a “virtual employee” or AI Agent.
Since we have vast experience at Spiral Scout offering FTE hires to companies building software that need outsourced or IT out staffing services, as well as our work on Wippy.ai, we are uniquely positioned to explain the trend we are seeing. The approach not only aligns with hiring budgets but also provides a clear, predictable value proposition for organizations.
This post will dive deep into the mechanics, benefits, challenges, and future of the AI FTE pricing model, bolstered by real-world insights and examples that we have learned from Spiral Scout and Wippy.ai which is an agentic framework we built for creating AI Agents.
What is the AI FTE Model?
The AI FTE model is a pricing strategy where AI output is benchmarked against the productivity of human employees in specific roles. Instead of pricing AI based on abstract metrics like API calls or processing units, this model offers customers a tangible, relatable unit of comparison: the cost and output of a human worker.
How it works and what we are seeing:
- Define Output Benchmarks: Identify typical outputs for a given role. For instance, a sales development representative (SDR) might research 100 accounts, send 50 emails, and make 30 calls daily.
- Package this offering as a SKU: Bundle these outputs as a SKU for customers to purchase.
- Price Competitively: Offer the AI FTE at approximately one-third the cost of a human employee, factoring in overheads like benefits, taxes, and software licenses.
This method reframes AI not just as a technology purchase but as a workforce strategy. Instead of offering human resources, we can now offer AI Agents to help the human workforce get more out of a smaller team.
Why Companies Are Adopting It
We now see a strong alignment with hiring budgets. Most organizations allocate more money and time to hiring than to tech. The AI FTE model seamlessly taps into these budgets by framing AI as a “hire” rather than a tool. In this example, a hiring manager with an open SDR role could allocate the same budget to an AI SDR, reducing time-to-productivity and minimizing ramp-up costs and the con that an employee may turn over or leave. There is also a predictable value proposition where AI FTEs promise to be comparable or have better output at a fraction of the cost of a human hire. They offer:
- No vacation or sick days
- Instant scalability
- Zero ramp-up time
- No complaining of menial tasks
- We have been building Proof of Concept (PoC) for businesses, where the businesses can “spin up” an AI FTE for trial periods, reducing perceived risk compared to hiring a human employee.
This also creates a more simplified prospecting process. Job postings become intent signals. For instance, if a company has a job posting for 30+ days, it’s an indicator of unmet needs, making it a prime candidate for an AI FTE pitch. AI-driven SDRs can generate automated outreach to these prospects, and make sure there is alignment with their pain points.
And lastly, there is accessible and transparent pricing. Traditional AI pricing models often rely on complex metrics like API usage or credits, creating friction. The AI FTE model simplifies this, presenting outputs directly comparable to human equivalents.
Real-World Applications of the AI FTE Model
- Sales Development
- AI-powered SDRs can handle outbound prospecting at scale. One AI SDR could:
- Process 6x the volume of accounts as a human SDR.
- Operate at 1/3rd the cost.
- Example: An AI SDR priced at $750/month manages 2,000 customer interactions, with additional usage billed incrementally. This scalability appeals to both startups and enterprises.
- AI-powered SDRs can handle outbound prospecting at scale. One AI SDR could:
- Customer Support
- AI FTEs manage repetitive queries, freeing human agents to focus on higher-value tasks. Flexible pricing models based on conversations or tickets handled ensure alignment with business needs.
- Recruitment and Seasonal Roles
- AI recruiters can ramp up during peak hiring seasons, working alongside human recruiters to double output. This mirrors traditional consulting models but with faster scalability and reduced costs.
Challenges and Criticisms
This doesn’t mean there isn’t criticism of this model and what the future can hold. A few objections we hear center around:
- Pricing Sustainability
- Critics argue that this model risks a “race to the bottom” as competition increases. AI FTEs could become commoditized, driving prices down to the cost of computing.
- We see that there can be a “race to the top” where AI FTEs continually improve, offering higher outputs and justifying premium pricing.
- Complexity in Defining Outputs
- Determining fair benchmarks for productivity can vary across industries and roles. For example, it is very common that the scope of an SDR’s role differs by company size and maturity.
- A common solution for this is that companies must collaborate with domain experts to establish transparent and defensible metrics.
- Loss of Intangibles
- Human employees bring intrinsic value, such as cultural fit and potential for leadership roles. AI FTEs lack these qualities, making them less suitable for organizations emphasizing long-term talent development. This is why we always suggest pairing humans with AI so its AI-led and human-assisted.
- Adoption Barriers
- Many hiring managers remain accustomed to human hires. Overcoming resistance requires robust demos, clear ROI demonstrations, clear onboarding processes, and strong business context integration.
Future Outlook
The AI FTE pricing model may initially seem niche but has the potential to reshape AI monetization and the use of AI agents in our business world. As AI capabilities evolve, these “virtual employees” could:
- The transition from entry-level outputs to more advanced roles increases their perceived value as they are onboarded and trained more.
- This will grow the hybrid human-AI roles, where human employees oversee and optimize AI FTE performance. There is no doubt in our mind that those humans who are great at delegation will be the best at harnessing AI Agents.
- Unlock opportunities in underserved markets, particularly SMBs looking to scale efficiently and compete with fewer people.
- Drive innovation in workforce planning, pushing organizations to rethink hiring strategies.
Long-Term Vision: The “FTE” language does help simplify adoption for HR, sales, technical and finance teams. As AI integrates further into our business operations, this model might evolve into collaborative systems where AI enhances human productivity rather than replacing it entirely. At one point in the future, we will have AI-led and human-assisted work as the norm.
Conclusion
The AI FTE model is more than a pricing strategy—it’s a paradigm shift in how businesses approach technology adoption and think about the type of workforce that they need in the future. By aligning with hiring budgets, simplifying pricing structures into what is commonly known now, and emphasizing real, tangible outputs, it bridges the gap between human and machine workflows. While challenges and uncertainties remain (or even as LLMs catch up to the tech that is being built), early adopters have an opportunity to shape the future of AI monetization and outcompete their larger, more established rivals.
For organizations exploring this model, the key lies in experimentation, transparency, and delivering undeniable ROI. The journey of AI FTEs is just beginning, and its impact will be felt across industries. If you have questions about how AI FTE can impact or benefit your company or how other companies are using AI, please reach out for a free demo and consultation.
How do you see AI FTE impacting your business?
