New age technology reshaping HR processes in the IT industry

25 Oct, 2017
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IT companies are increasingly segregating skills as made to stock and made to order skills based on business projections, skill value and skill availability.

The interesting phase that the IT services industry now is that it is influenced by three key factors on the supply side (a) Need for re / up / cross skilling of existing people on a massive scale to build new technical, functional, domain and behavioral competencies to respond to customer need for digital transformation of their businesses (b) Significantly reduce cost of service delivery by increasing billable utilization, right staffing projects with cost-effective role pyramids, eliminating / reducing bench scenarios (c) deal with immediate job redundancies triggered by automation of repetitive tasks, obsolescence of legacy technical skills and functional roles. 

The response by IT services companies determines whether these factors are an opportunity or a threat.

"This phase is best responded to in three ways (a) Shift focus from hiring to skilling internally, right-sourcing the hiring process and adopting technology that enables more qualified hiring (b) Qualify future workforce by adopting liquid workforce models (c) Use HR technology to engage better with people. We will now focus on HR technology interventions that will help companies hire efficiently for impact and effectiveness thereby creating time required to focus on skilling and repurposing people.

Data management, sourcing automation, matching and hiring process automation are four primary areas in which companies use disruptive technologies to transform their hiring process. In the data management space, companies use open source frameworks such as Hadoop to use big data to build ideal job descriptions that assure desired job outcomes, build ideal candidate profiles that are the closest fit for such job descriptions and to decide on sourcing channels and methods that are most likely to shortlist such candidates for the said jobs. Since companies also prefer to fulfill demand internally, they are increasingly using big data techniques to organize, retrieve, process and employ employee data for internal demand fulfillment on a large scale. These technologies help companies be prepared with the basic instrumentation of right job descriptions and ideal candidate profiles for the next stage in the hiring process – sourcing automation.

Just in time hiring has given way to predictive hiring. To be able to do this, IT companies are increasingly segregating skills as made to stock and made to order skills based on business projections, skill value and skill availability. Hiring specialists add more value to the business by focusing on made to order skills; this becomes possible only when made to stock skills are hired using predictive hiring processes. Predictive hiring, in turn, calls for large-scale sourcing automation. Bots that use structured job descriptions and ideal candidate profiles are increasingly being employed to crawl job boards, assessment platforms, tech communities and just about any other ecosystem to trace digital footprints of potential hires, build a candidate profile and parse the information into internal systems for creating a qualified talent inventory.

"Meanwhile, deep learning techniques similar to those used by marketing specialists to predict and influence consumer behavior are now increasingly being used by hiring specialists to design, launch and manage campaigns to attract right candidates for made to order skills or combination of adjacent skills. Information is the new oil; hence any information about candidates in form of digital profiles or resumes are now being appended into internal company databases and updated continuously using parsing engines. Such parsed information is then used to execute the next big area – matching.

Lower cost of search and onboard can be achieved only when hiring teams run efficient matching engines. Large volumes of unstructured data, fatigue, and bias lead to inefficient matching resulting in sins of both omission and commission. Therefore, matching engines are the first priority area where companies commit investments to add on to their existing applicant tracking systems. Natural language processing techniques are used extensively to make sense out of unstructured job descriptions on the demand side and resumes characterized by very high standard deviation and large volumes of parsed data on the supply side to identify, assess, stack rank and present best fit candidates for a given job with high levels of accuracy and consistency. Over a period of time, machine learning technology improves the accuracy and consistency even further as they integrate with performance, engagement and attrition data.

The fourth area of investment, hiring process automation refers to a suite of interdependent processes being automated to increase the throughput of supply to cater to demand. As hiring transforms from being a process execution job to a sales role, the repetitive, logistical aspects of the hiring process are getting automated.

First, the initial calls to prospective candidates to check interest and basic qualifying criteria are now being handled by synchronous two communication methods like automated voice response systems and asynchronous one-way communication methods such as messaging. Second, much more qualitative profiling of candidates is being done by companies by partnering with assessment companies that specialize in testing candidates for specified proficiency levels of desired skills. Third, the first level in-person interviews of candidates involving heavy logistics are being replaced by either live video interviews or proctored video recordings that can be used for reviews by multiple hiring managers. A combination of automation of initial calls, industry certified, baselined, platform-based technical profiling and video interviewing not only increases the throughput of candidates but also brings consistency in the first level process. Finally, block-chain based technologies accelerate the background verification process and make it far cheaper over the long run, thereby reducing time to onboard a lot further.

Most of the technologies mentioned above are not disruptive in the real sense but have been used in multiple industry verticals for at least a decade now. However, the adoption of these technologies in HR is late but definitive. In the next few years, integrated hiring processes that incorporate these technologies are expected to increase the productivity of recruiters by 3 times, increase the throughput by 3 times and halve the cost per hire.

"While companies adopt these technologies, job seekers are expected to respond to this adoption by way of increasing their digital footprint in forums that matter, invest in their own skill building to be able to differentiate themselves, constantly seek to be mile wide in familiarity with technologies while being mile deep in specializing in chosen areas of interest and be open to diverse experiences across employers.

Finally, as discussed earlier, companies are better off focusing on their core work of transforming their clients’ businesses by building their people’s capabilities to innovate and deliver. As liquid workforce models become more and more prevalent as a legitimate employment mode in IT companies, the primary employers of the liquid workforce should now be committed to building a technology led talent supply chain ecosystem using new age technologies.


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