How do you bridge vendor promises with actual hiring results?. HR leaders return from conferences with AI roadmaps, but translating those promises into real-world outcomes remains a challenge.

During a live broadcast from HR Tech in Las Vegas, Devin Foster, Host of Talent Experience Live, and Mike DeMarco, Product Marketing Manager at Phenom, unpacked why even the most advanced AI investments sometimes fall short of solving day-to-day hiring challenges.

The conversation pointed out why success with AI depends on selecting the right fit. They drew a line between general AI solutions and Applied AI built for industry-specific needs — highlighting how challenges in manufacturing differ from retail, and how tailored solutions create impact.

Catch the full episode here or explore the key insights below!

Understanding Applied AI for HR

The difference between general AI solutions and applied AI lies in how the technology is trained, implemented, and integrated into existing HR workflows. "AI understands everything about the world, but it's such a wide swath of data. It's not focused on what you need," DeMarco explained. "Infrastructure is a massive thing that people need to ask their vendor: how are you building this AI, and how is it personalized for me?"

The differences between these methods become clear when comparing their core characteristics:

General AI Solutions Applied AI for HR

Trained on broad, general datasets Built on HR-specific data: job descriptions, skill taxonomies, industry requirements

Provides general responses Delivers contextual insights for talent acquisition and talent management challenges

May struggle with compliance nuances Understands HR regulations and industry requirements

One-size-fits-all approach Customized for organizational needs and workflows

Limited integration capability Designed to enhance existing HR systems

Applied AI for HR is built on relevant datasets that understand the nuances of talent acquisition and talent management. This targeted training enables more accurate responses to HR-specific queries and a better understanding of the organizational context.

The infrastructure difference becomes clear when evaluating system responses. While broader AI tools may provide general guidance, Applied AI systems understand the context behind HR queries and provide actionable insights tailored to organizational needs.

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Identifying Your Implementation Entry Point

Many organizations approach AI implementation with comprehensive rollout plans, but successful deployment requires strategic thinking about where AI can create immediate, measurable impact. DeMarco advised:

"Instead of implementing AI across everything at once, find your product champions, take a specific role and watch it grow"

The most effective implementations begin with identifying specific pain points rather than seeking broad AI adoption. Consider the following starting points:

Candidate attraction challenges: Deploy personalized career sites that match candidates to relevant opportunities and capture passive leads through interactive experiences.

Interview scheduling bottlenecks: Implement automated scheduling systems that eliminate back-and-forth coordination while maintaining candidate experience quality.

Structured interview gaps: Use AI to build evergreen, role-specific interview questions that ensure consistent candidate evaluation across hiring teams.

Pipeline visibility issues: Focus on analytics that provide clear insights into where candidates drop off and which sources deliver quality hires.

This targeted approach enables teams to show tangible outcomes — faster screening, personalized candidate engagement, reduced time-to-hire, and better skill matching at scale. And because applied AI works within existing tech ecosystems, it enhances current workflows rather than requiring complete overhauls.

Adapting AI for Different Industries

The discussion highlighted how different industries require distinct AI strategies, demonstrating that effective AI adoption must align with sector-specific operational realities.

Retail organizations experience rapid scaling challenges, particularly during peak seasons or expansion phases. DeMarco referenced how Rally House scaled from 70 to 300 stores using automation-focused AI technology, noting: "In retail, you have to get people in the door fast." For retail, AI implementation emphasizes speed and volume through automated screening, rapid candidate matching, and streamlined onboarding processes that can efficiently process large candidate pools while maintaining quality standards.

Related: Hitting Home Runs in Hiring: Rally House's Championship Formula for Retail Recruitment

Manufacturing presents different requirements despite appearing traditional on the surface. "Manufacturing is high intelligence and automation," DeMarco explained. "The industry is changing. There are many emerging skills in robotics, and how to manage the floor has changed. You need to be highly technical."

Businesses in this industry require AI that understands technical skill evolution, certification requirements, and safety protocols. Plant managers working 12-hour shifts need mobile-ready solutions that integrate with their workflows rather than adding an administrative burden.

These industry differences highlight why context-specific AI solutions provide better results than broad-based approaches. Understanding sector-specific pain points allows AI systems to deliver relevant recommendations and automate appropriate processes for each organizational environment.

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Extending Beyond Recruitment

While many organizations focus AI efforts on recruitment, the technology's impact often comes from talent management and retention initiatives.

"You can't go from 0 to 100 all at once. You have to start by taking bites of the apple slowly and walking through this process step by step," DeMarco observed. This incremental method allows organizations to build AI capabilities across the entire talent lifecycle.

This emphasized how AI can connect business strategy to HR planning through workforce analytics. By analyzing current skills, market trends, and organizational goals, AI systems can help leaders identify skill gaps before they become critical shortages.

This moves AI beyond administrative tasks to strategic planning. Organizations can utilize AI to evaluate which employees are ready for advancement, where skill development efforts should focus, and how internal mobility can address retention difficulties. This also addresses a common oversight in talent strategy. AI can assist organizations in identifying and developing existing talent rather than constantly seeking external hires.

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Building Secure Frameworks

While expanding AI across talent management delivers significant benefits, enterprise adoption requires addressing security and compliance concerns that companies often underestimate during planning. "Your ATS systems carry large amounts of data and PII. You need to ensure this information exists within a secure infrastructure," DeMarco emphasized.

The security challenge intensifies as multiple departments pursue AI solutions simultaneously. Sales teams, finance departments, and HR all evaluate different AI platforms, creating complexity for CIOs and CTOs who must develop comprehensive evaluation frameworks. These technology leaders need to assess AI capabilities across various organizational functions while maintaining consistent security standards.

Companies must also prepare for an uncomfortable reality: employees will use AI tools regardless of official policies. "If you try to put this into a black box that we never talk about, recruiters are going to use it anyway, and they are going to share some of that personal information that may be confidential," DeMarco warned.

Related: Introducing the World’s Most Advanced Applied AI: Solving Real HR Challenges Across Industries, Eliminating the Gap Between Business Goals & HR Strategies

This makes it critical for companies to implement secure, approved AI functionality that meets employee needs while protecting sensitive information.

Forward-thinking companies establish AI governance frameworks that include bias mitigation strategies, human oversight requirements, and clear data handling protocols. These frameworks provide guidance for AI deployment while ensuring compliance with evolving regulations.

Looking Forward: Applied AI as Competitive Advantage

The shift toward Applied AI represents more than a technology upgrade — it's a strategic transformation that will define competitive advantage in talent acquisition and management.

Organizations that embrace targeted AI implementation while maintaining focus on solving specific business problems will create significant advantages over competitors still struggling with generic AI tools or avoiding AI adoption entirely.

The key lies in understanding that AI success isn't measured by the sophistication of the technology but by its impact on business outcomes. Applied AI provides the framework for achieving that through strategic, industry-specific implementation that addresses real organizational challenges.

As AI capabilities continue advancing, the organizations that start with focused, problem-solving implementations today will be best positioned to scale those successes across broader HR operations tomorrow.

Learn how Applied AI solves specific HR challenges across your talent lifecycle.

Watch the on-demand HR Innovation Showcase to discover industry-tailored AI implementations that drive measurable results.

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