Future Workplaces are Where Automation Meets Human Ingenuity

A Deloitre poll of Indian businesses found that 42% of respondents see productivity boost as a key benefit of generative artificial intelligence. The findings underscore what many people outside of the tech space may not be aware of: Technology is amplifying intelligence and automation to make productivity – and ultimately experiences – even stronger.

That’s worth a closer look, especially since every function within an organisation (marketing, sales, customer service, etc.) is up for disruption. None more so than human resources because of the sheer volume of repetitive tasks.

HR teams need more than just traditional HR tech. They need AI-first products, and people-first solutions. The key lies in leveraging intelligent, design-centric platforms that offer deep insights into the nuances of users, customers and industries to deliver hyper-personalized experiences that transform hiring, developing and retention.

These platforms must be capable of understanding unique preferences and requirements, going beyond surface-level data.

As technology amplifies productivity, a deep understanding of how AI and automation can be applied today is critical. But it starts with asking the right questions.

As one CEO put it: “It all depends on the particular job you’re hiring for and where it’s located. You need to think about three things: what is automation for, what type of automation can be deployed and what are the roles where intelligence can be most helpful.”

These are important issues to consider since most chief executives in India expect their workforces to grow over the next three years, and they plan to invest in AI.

Let’s take a look at a few practical HR use cases.

Generating efficiencies

Whether it’s scheduling meetings or summarizing interviews, AI brings speed, accuracy and context to every stage of the hiring process.

Intake Meetings: AI-powered intake agents streamline the traditionally time-consuming process of aligning expectations between recruiters and hiring managers. With a single click, recruiters can schedule meetings, automatically sync calendars and create invites.

Drawing on historical data and past feedback, AI curates personalized questions, takes detailed notes during the meeting, ensures all key points are addressed, generates a summary, and suggests updates to the job description based on new insights. This reduces weeks of work to just a few quick steps, allowing recruiters to post job openings faster than ever.

Since HR is largely centered around knowledge sharing and exchanging information, agents can quickly assemble information from multiple sources to provide personalized and automated services such as event and campaign creation.

On sourcing emails alone, GenAI can save almost one hour per day by creating and sending high quality, personalized emails that average a 50% response rate. This is compared to a standard form-type email that returns an anemic 14% response rate. Email is just one of a dozen everyday tasks that agents can perform for recruiters.

Voice AI Screening: Often, coordinating calls with hard-to-reach candidates, like night shift nurses or drivers, leads to delays. Voice AI Screening solves this problem by engaging with candidates directly, and asking fluid, conversational questions to gather essential information. This eliminates scheduling delays and ensures that critical talent isn't lost due to inefficiencies.

Ask Anything for In-Depth Insights: Instead of manually sifting through profiles or interview notes, recruiters can ask the AI agent specific questions, such as which jobs a candidate is best suited for or details from a particular interview. The AI agent curates all relevant information, providing a comprehensive, instant response that helps recruiters make better, faster decisions.

AI Architectures Are Not the Same

Of course, the goal of any AI architecture is to be more productive across the entire talent ecosystem. But this requires a whole new generation of architectures to support meaningful AI adoption in the enterprise.

Large Language Model (LLM) based apps today are just wrappers. What is missing is the enterprise context that needs to feed into these prompts while balancing data privacy concerns. The challenge is less than 10% of the hiring data is digitized today and hence not fully AI-ready.

LLMs are evolving at a rapid pace. They know everything about the world but they don't know much about an enterprise. Bridging that gap requires a centralized data system that captures all talent interactions and past data, allowing HR to represent workflows and hiring processes effectively.

By integrating these layers of information, bottlenecks can be identified and AI solutions deployed that learn from specific data, tailoring insights to an organization's unique needs. 

Ultimately, it’s all about how the platform can make organisations become more productive across the entire talent ecosystem.

If an enterprise doesn’t have a clear goal, however, applying AI and automation is difficult. Companies will often have goals but they don’t know how to really make it all work.

Any Old HR System Won’t Do

Should a company run out and buy the first platform it sees? No. It doesn’t work like that. There are crucial steps to consider when building a safe AI architecture.

Proof Lies in the ROI

A global logistics giant with 80,000 employees across more than 100 countries turned to automation to transform its approach to internal mobility, creating a system that not only retains top talent but helps them thrive. The positive impact on employee retention and engagement was significant:

● Internal hires increased by 10% annually

AI helped a big U.S. hospital network contend with rising labor costs and recruiters spending too much time on low-value repetitive tasks. A revamped career site features a chatbot that asks “Are you looking for a job?” Candidates type in a desired role and up comes a list of opportunities that fit what they are looking for. There have been more than 287,000 interactions (i.e. candidates asking questions) since the chatbot went live in January 2023. From those interactions, more than 2,000 people were hired. These were essentially 2,000 candidates the company would have missed out on, since an earlier version of the career site was clunky and time-consuming. Chatbots entirely changed the hiring dynamic.

Other success stories abound of AI being a difference-maker for employers.

As promising as all of this sounds, the best is yet to come. Agents will become even smarter and faster. It’s not about having technology for technology’s sake, but about having the depth of intelligence and automation that aids in better decisionmaking for enterprises.

By Kumar Ananthanarayana, Vice President, Product Management, Phenom