Conversations with HR and technology leaders across a variety of industries over the past year have revealed several common challenges. These include the need to capture skills, identify gaps in the organization, and leverage technology to upskill and reskill existing employees to fill those gaps. The increased investment in artificial intelligence is seen as a promising opportunity for organizations to tackle these challenges. However, after these issues are identified, there is often a question of where to start and how the latest solutions can be leveraged to facilitate a collaborative process.
Technology professionals are all too familiar with the concept of agile development cycles. We build tools and products in ever shorter time frames, iterating upon prior versions and constantly improving overall output. This shift took us away from antiquated approaches that saw long design cycles and even longer development cycles, delaying the opportunities for critical feedback from end users. The embrace of agile development has meant that products reach the market quicker and receive feedback earlier, resulting in minimum viable products that can continually be improved rather than a polished product that kept business users waiting, and may ultimately miss the mark by time it gets to production.
We encourage that HR leaders take a similar approach to addressing the skills issue. It is critical that they don’t get stuck in a state of analysis paralysis when reviewing options for skills configurations and tracking progress. The quicker the relevant data can be put in front of users, the better.
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The sooner that these advanced tools are enabled and pushed to end users, the sooner your enterprise analytics team can accurately track and report on skills across the workforce, producing valuable insight that allows HR leaders to make informed decisions to address their skills gaps. The greater the data pool for this tool, the more accurate these recommendations will become.
Development plans, talent reviews, and learning programs can all be set up with direct ties to skills to enable coaching and development in areas of need. Customized development plans and talent reviews allow for regular checkpoints, self-assessments, and longer-term planning between employees and managers. Content developed by in-house learning and development teams, as well as third-party content from providers like LinkedIn Learning and Skillsoft can be made available and linked to needs in skills, development items and competencies. This enables employees to discover relevant content quickly, which will benefit their professional development journeys. The skills journey can be a long one, but by adopting a smart and integrated approach to measure your workforce, any gap can be identified, addressed, and closed.
Recognizing this need for skills training, SaaS solutions have expanded the capabilities of their platforms to better meet business needs. For instance, some platforms now offer tools that allow customers and partners to build custom applications on their existing technology infrastructure. The use cases for these custom applications are nearly limitless. They can range from job-mapping tools that facilitate the rapid integration of acquired companies to mobility questionnaires that track employee willingness for internal relocation, and much more.
All these developments point squarely in the direction of increased custom development on top of platforms’ native capabilities and more importantly, the further democratization of that development with the assistance of low/no-code tooling combined with generative AI. Low/no-code development will also dramatically increase in use as organizations are empowered to quickly build business cases for new applications as the time to develop and push to production continually decreases.
Additionally, generative AI can be leveraged for application inspiration and business use. One such customer use case involves automated reviews of absence data, including types of absence, frequency, and time of year taken. This data is then regularly compared against current policies to allow HR leadership to review and update as necessary. This use case can be extrapolated to other policy scenarios that analyze historical data, generate recommendations, and enable enterprises to plan for potential future changes and make informed decisions in a proactive manner. These intelligent applications provide one of the most compelling arguments for increased generative AI investment in the human capital management (HCM) technology space: that significant enterprise impacts can be achieved by automating previously mundane tasks such as policy reviews and opening bandwidth for more strategic work.
From workforce and skills transformations to generative AI and custom application development, the HCM and finance SaaS spaces continue to expand and adapt to broader enterprise trends. The movements discussed here will continue to increase the ROI of these technologies, while simultaneously adding strategic value to both HR and finance organizations across industries.
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