Why legacy tech prevents TA from being AI-first

The Gem team spoke with recruiting leaders Michael Adair, Jeremy Lyons, and Dinaaz Tawileh to gather the latest in AI-first recruiting in this newest content series.

Fifty-eight percent of TA leaders say AI is imperative to stay competitive, but there’s still some hesitation. What’s holding the rest back from full adoption?

Talent Acquisition teams have traditionally relied on singular-point solutions to address specific challenges. While effective in isolation, this approach has led to a fragmented tech ecosystem that slows innovation and limits AI’s full potential. Outdated systems create barriers, making it harder for recruiting teams to work efficiently and stay competitive.

The rise of AI-first TA tech stacks

Many recruiting tools claim to have 'AI-powered' features, but AI-first platforms are built differently. Instead of layering AI onto existing systems, an AI-first approach integrates it into the platform's core. This deep integration allows AI to access complete candidate data throughout hiring, learning from every interaction. As a result, it delivers smarter, more contextual recommendations rather than relying on fragmented data points.

One of AI's most significant changes in revolutionizing TA tech stacks is that sourcing has become more proactive. AI-powered tools scan millions of profiles instantly to identify passive or diverse candidates that recruiters might overlook. Screening and matching are also improving, as AI analyzes job descriptions and candidate profiles to rank potential matches, though bias remains a persistent challenge. 

Additionally, AI makes interview scheduling easier, reducing recruiters' time coordinating logistics. Beyond the hiring phase, AI also streamlines the transition from candidate to employee, ensuring a smoother onboarding experience. A newer development is shared post-hire analytics — where recruiting teams, once cut off from candidate data after day one, can now track employee success and refine future hiring strategies based on performance insights.

Despite these advancements, AI doesn’t fix broken hiring practices. AI will only reinforce those issues at scale if a company has flawed job descriptions, entrenched biases, or inconsistent processes. Too often, organizations assume they can refine their hiring approach later. However, failing to address these challenges upfront leads to long-term inefficiencies. The real power of AI lies in enhancing well-structured hiring processes — not compensating for poor ones.

Why TA teams haven’t simplified outdated and complex recruiting tech stacks with AI-powered TA tools

One of the most significant barriers to AI adoption in recruiting has nothing to do with the technology itself. It’s rooted in human nature. While AI is often marketed as a tool that enhances efficiency and unlocks new capabilities, much of the broader conversation has focused on workforce reductions. 

Headlines frequently highlight how companies invest in AI to replace human workers, reinforcing the perception that people are inefficient and inconsistent compared to AI’s precision. Naturally, this triggers a survival instinct, making employees more protective of their roles and less willing to embrace AI-driven changes fully.

This fear manifests in subtle but impactful ways. Recruiters may hesitate to document every detail of their processes or log data as thoroughly as they should, fearing that too much transparency could make them replaceable. Instead, they hold onto their “secret sauce,” the unique knowledge and strategies that have helped them succeed. 

This approach has allowed some recruiting professionals to climb the career ladder for years. However, as AI continues to reshape the industry, clinging to these old habits may ultimately limit growth rather than protect job security.

Beyond that, there are also technical reasons, such as:

• Data & system readiness – Many companies still operate with outdated ATSs and disconnected tools, making it challenging to integrate AI effectively. Without clean, structured data, AI can’t function properly. Classis bad in, bad out.

• Bias & compliance concerns – AI in hiring has faced scrutiny over potential biases. TA leaders worry that automated decision-making could lead to discrimination risks if AI models are trained on flawed historical data.

• Change management – Implementing AI requires shifts in workflows, training, and buy-in across teams. Recruiters may resist AI if they fear it will replace them rather than enhance their work.

• Proven ROI – While AI promises efficiency, many teams struggle to measure real impact. Some companies hesitate to invest without proof that AI will improve hiring quality, not just speed.

Cost & prioritization – With tighter budgets, many companies are focused on maintaining core recruiting operations rather than investing in new AI-driven tools.

The challenges of legacy technology in TA

Fragmentation has been a consistent challenge in TA tech. Many recruiting teams have assembled various tools to stay competitive, yet they remain tied to legacy ATSs and HR systems that weren’t designed to integrate with modern AI solutions. 

This fragmentation isn’t just an operational challenge. It’s a costly one. Companies with disconnected recruiting technologies often spend 30-50% more on their tech stack than those using unified platforms. These expenses stem from multiple subscriptions, overlapping features, and the hidden costs of integrations and technical troubleshooting.

This disconnect leads to several issues:

1. Outdated systems and data: Many TA teams still rely on outdated applicant tracking systems (ATS), sourcing tools, and analytics platforms that weren’t built with AI in mind. These systems are often siloed, preventing seamless data integration and limiting AI-driven insights. Additionally, legacy tech stacks tend to store data in fragmented formats, making it difficult for AI to extract meaningful patterns. Without clean, structured, and real-time data, AI struggles to provide timely and accurate recommendations, ultimately slowing hiring decisions and reducing recruiter efficiency.

2. Limited automation and scalability: AI-powered TA processes, like automated resume screening and chatbot-supported candidate engagement, require flexible and scalable infrastructure. Legacy tools, built with outdated architectures, struggle to support these capabilities. As a result, companies are stuck with manual, time-consuming processes that hinder recruiter efficiency and candidate experience.

3. Reduced productivity: Fragmented systems don’t just drive up costs. They also hinder recruiter productivity. Recruiters using unified platforms can be up to 5x more efficient than those juggling disconnected tools. By minimizing context-switching, reducing manual data entry, and freeing up time to focus on relationships instead of technology, a streamlined system enables recruiters to work smarter and faster.

4. Resistance to change and high migration costs: Transitioning from legacy systems to AI-powered solutions requires a cultural and financial commitment. Many organizations hesitate to overhaul their TA tech stack due to concerns around costs, employee resistance, and fear of hiring process disruption. However, delaying this transformation only disadvantages companies in a competitive talent market.

5. Limited real-time insights: AI relies on clean, structured data for accurate recommendations, but most recruiting data remains unstructured or outdated.

6. Integration hurdles: Many AI tools depend on API connections, yet legacy systems often lack native support, requiring costly and complex workarounds.

7. Waiting for the roadmap: Legacy providers promise AI-driven features but offer no clear timeline or transparency on whether they deliver true AI or just automation with an AI label

As a result, companies often struggle to implement AI effectively, even after investing in it, because their core systems aren’t built to support automation and machine learning. To fully leverage AI, recruiting teams must advocate for interoperable tech stacks—systems that integrate seamlessly rather than attempting to retrofit AI into outdated processes.

Michael Adair, Managing Partner at Growth by Design Talent

How organizations overcome these barriers

Here’s how organizations can ensure they’re integrating AI-powered TA tools effectively:

• Adopt a phased approach: Instead of a complete system overhaul, businesses can implement AI in stages, integrating modern tools with existing systems before a full transition. At Gem, we offer a modular platform built from the ground up with AI at its foundation—not as an afterthought. This AI-first architecture allows our customers to start with the solutions they urgently need while adding additional capabilities as their requirements evolve, all while maintaining a consistent AI layer that learns from every interaction across the platform.

• Collaborate with AI-first vendors: Partnering with AI-powered TA technology providers like Gem can accelerate transformation while reducing the burden on internal IT teams.

• Invest in training & enablement – Adoption won’t happen unless teams feel comfortable using the new tools effectively.

• Get cross-functional buy-in early – Involve recruiters, HR, IT, and hiring managers to ensure alignment.

• Adopt a phased approach – Rather than an entire overhaul, start by modernizing key areas (e.g., sourcing, scheduling) and expand from there.

• Prioritize integrations – Ensure any new tool works seamlessly with existing systems to avoid data silos and inefficiencies.

Once organizations integrate AI into their TA tech stack, they’ll notice significant benefits, including:

• Workflow automation – AI reduces tool-hopping by integrating functions (e.g., auto-scheduling, automated follow-ups, and pre-screening).

• Smart candidate insights – AI surfaces past interactions, interview feedback, and sourcing history in one place instead of forcing recruiters to dig through systems.

• Automated reporting – Instead of manually pulling data, AI-powered dashboards provide real-time hiring insights without extra effort.

• Improved candidate experience and DEI outcomes — AI-first systems help create seamless, equitable hiring experiences, keeping candidates engaged while removing bias. 

• More impactful AI results — It becomes exponentially more valuable over time when AI accesses unified data across the entire recruiting process. Every candidate interaction, every hiring decision, and every engagement metric enriches the AI's understanding, allowing it to make increasingly intelligent recommendations. Unlike point solutions,, whose AI can only learn from limited data, platforms with unified data continuously improve —predicting which candidates are likely to succeed, personalizing outreach based on past interactions, and identifying patterns humans might miss.

AI won’t eliminate the need for multiple tools but can help connect them better, turning a scattered workflow into a unified process.

Embrace the future of TA with AI-powered hiring success

Outdated legacy technology is one of the biggest roadblocks preventing TA from becoming AI-first. Organizations must modernize their tech stack, prioritize data-driven decision-making, and embrace AI’s potential to stay ahead of the competition. 

This approach isn't just about integrating AI features into existing workflows—it's about adopting platforms built with AI at their core. Organizations that merely bolt AI onto legacy systems will continue to face limitations in data quality, integration capabilities, and overall effectiveness. The future of talent acquisition consists of integrating AI into hiring strategies. That starts with leaving outdated systems and solutions behind.

Coming up next in this knowledge series:

•  How an AI-first AIO facilitates proactive hiring – Learn how AI-powered AIOs transform reactive hiring into a strategic advantage.

•  The AI mindset for recruiting and ROI – Shift your perspective to maximize AI’s impact and measure tangible business outcomes.

Stay tuned as we explore how to build a sustainable, successful AI-first recruiting strategy for the long haul.