You’re an ambitious Talent Acquisition leader, well aware of the ever-changing tides within the HR landscape.

You know that Artificial Intelligence (AI) and Machine Learning (ML) have the potential to change not just the recruitment space, but also potentially your own role. These innovations hold the promise of automating significant tasks, thereby freeing up recruiters to focus on deep, strategic work.

And now the CEO has emphasized the urgency to accelerate this project, which adds time pressure to the situation.

But, there is an elephant(or a tortoise) in the room

Can your existing Enterprise Software as a Service (SaaS) vendors genuinely lead the charge in AI-driven advancements?

I’m not so sure they are going to get the CEO off your back quickly enough.

Their legacy business models create an innovator’s dilemma that will likely slow adoption of transformative AI technology.

The Present Landscape

AI has the potential to automate many responsibilities currently performed by people. Take for example the routine and often time-intensive task of screening resumes.

Advances in AI enable automating parts of this screening process, with systems analyzing resumes for relevant keywords, required experience, and even personality traits.

This one example illustrates only a fraction of what AI is poised to transform as the technology continues advancing.

The Threat to Traditional SaaS

However, the move towards ubiquitous AI in HR software potentially poses a threat to conventional SaaS vendors. Traditionally, these vendors thrived on a user-based monthly licensing paradigm.

With AI replacing the need for humans to perform many tasks, the question arises: Is the SaaS business model still viable? In fact, with AI poised to eliminate or simplify significant tasks, will the end-user be reduced to a mere spectator, merely watching the machine at work?

Disrupting their own successful legacy business models is inherently risky. But if they delay too long, nimble AI startups could establish dominance with innovative pricing directly tied to recruiter productivity, time-to-hire, and other metrics not dependent on human users.

This represents an innovator’s dilemma for SaaS companies. As the technology landscape evolves, they must adapt their business models to align with AI capabilities while preserving revenue.

The Future of Task Enhancement

The first phase of AI implementation by existing vendors is already starting to enhance or improve tasks, rather than completely reinvent the role of recruiters.

Algorithms now help with writing job descriptions, analysing resumes, and managing responses. However, the human element – the intuitive, empathic act of human engagement – remains a necessary part of the mix.

Pricing – The Challenge Awaits

These changes forecast a challenging landscape for AI service pricing that started 5 years ago as more and more activities shifted to API calls. That’s set to accelerate now that human-like activities can be invoked via a single API call to a generative AI model allowing less humans to do more work.

Is usage-based pricing the ideal approach as AI handles more tasks autonomously? Or should vendors start considering a shift from usage-based pricing to an outcome-driven model, which aligns better with an automated future?

An outcome-driven pricing model rewards results rather than usage volume. For instance, a company could be billed based on the number of successful tasks completed by the AI service, rather than the total API calls or compute time consumed.

As AI systems take on more roles independently, charging by measurable business impact rather than low-level resource usage may become the pricing model that makes the most sense.

Conclusion

The key for AI in recruitment is determining how human expertise can add the most value alongside automation. The expanding role of AI leads to questions around current pricing models like monthly licenses and per-user subscriptions.

As AI handles more tasks autonomously, billing simply by user volume becomes less aligned. Usage-based pricing on API calls also misses the full picture. An outcome-driven model tailored to recruitment may ultimately make the most sense.

Rather than user licenses or API calls, an AI recruiter could be priced on tangible hiring outcomes achieved. Fees per candidate sourced, screened, or hired reflect business impact. As AI productivity grows, this aligns pricing to concrete recruiting results enabled by the AI system. The future pricing model for AI in recruitment could reward measurable contributions to hiring, not just usage.

Will traditional Enterprise SaaS and it’s pricing successfully adapt quickly enough for you in this AI-dominated tide?

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