Strategic AI Adoption: Why HR Product Leaders are Choosing to Buy, Not Build, Matching Intelligence
The seismic shifts caused by the AI era have grown more frequent, disrupting organizations as they scramble to augment people, products, services and ways of working. Respondents to Gartner’s 2024 AI Mandates for the Enterprise Survey report that the majority of their organizations are currently pursuing 5 or more AI projects, while 29% of respondents say their organizations are pursuing 10 or more. Given these numbers, product leaders must choose whether to build or buy artificial intelligence solutions as they look to push their businesses forward in this new AI era.
Mission Critical Decisions: AI Matching Solutions in HR Tech
What this means for HR and staffing product leaders is that the question of building or buying sophisticated AI matching solutions has become mission-critical. As platforms compete on the quality and fairness of their candidate-job matches, the build vs. buy analysis is no longer optional, it’s essential to a winning product strategy.
Balancing Built and Purchased Solutions
When we look at our own customer base of HR/Talent tech tools, we see that modern application portfolios are built through an intentional combination of different application development and delivery methods, spanning purchased, built, and integrated applications. When organizations aim to develop a build vs. buy strategy, they are trying to determine the most effective means of enabling business capabilities (from both a speed to market perspective and cost perspective). We find that our partners tend to adhere to a framework similar to what Gartner has outlined below.
API-First Approach: Accelerating AI Integration in HR Platforms
When it comes to developing specialized AI features within a larger platform, organizations are rapidly embracing the “buy strategy” purchasing API-first AI solutions to streamline integration, product development, and significantly accelerate product delivery timelines.
Hidden Costs of In-House AI Development
For product teams considering building matching intelligence in-house, it’s important to fully scope not just initial development, but ongoing costs associated with AI expertise, data acquisition, bias mitigation, compliance, and the relentless need to update models as the labor market evolves. Forrester Research notes that many organizations severely underestimate these long-term operational costs, sometimes two to three times compared to off-the-shelf solutions.
Competitive Advantage Through Buying
In fast-changing markets, “plugging in” and buying a trusted AI matching API like AdeptID means you can go to market faster, stay focused on your differentiating core features, and deliver proven, fair, and explainable matching experiences your users expect, leaving your product team to focus on the other 5 to 10 AI projects you have in the pipeline.