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"Configurable" has historically been used to describe the degree to which enterprise software, such as HCM systems, can readily adapt to a customer’s business and process requirements, ideally with no (or only modest) involvement from software experts or IT teams. The term will likely always have considerable value when evaluating HCM systems because, while not always top of mind with buyers, the level of configurability in applications is essential for achieving key strategic goals, such as elevating organizational agility. Configurability is the means, but when an enterprise can react or adapt to indications of potential business risks or opportunities with quick, decisive workforce actions and decisions, this is the true business opportunity in the configurability and flexibility equation. Organizational agility is one of the most reliable paths to sustaining competitive advantage. Think of the situation where a large consultancy has determined they can successfully bring a new service offering to the market. They must quickly and effectively execute a broad range of workforce-related activities including, in some cases, conducting a type of analysis or tracking some information for the first time. Their agility is clearly aided by having an adaptable HCM system.
With that as context, allow me to introduce a concept I call “adaptive HCM” and define as the ability of organizations to quickly take appropriate workforce-related actions based on the presence of certain conditions or indications. These triggers are detected by HCM applications in an intelligent, automated manner based on machine learning (ML) data patterns and their implications. One example might be after a costly or disruptive mistake is made and the worker is notified about both how to correct it and how to prevent it in the future. This might involve watching a short how-to video, reading an instructional checklist or even getting some spot coaching. It is most impactful when specific guidance or a recommendation accompanies the communication, nudge or alert. Another example might be when a manager is notified of a key employee that is considered a retention risk and the system recommends certain mitigation steps.
But, in the age of intelligent applications using digital innovation, signals and cues warranting an important response no longer have to be this tangible, nor related to particular individuals. Predictive analytics within HCM systems can be used to highlight the likelihood of employee engagement or a dip in productivity based on certain conditions or triggers; and employing anonymized and aggregated “listening” methods and tools—such as pulse surveys and sentiment-analysis technology—to call out themes that are, should (or will) be increasingly top of mind are such examples. Again, the ideal situation is when this “intel” is accompanied by some best-practice guidance, perhaps rooted in the ML from past data patterns and correlations. And, as mentioned, adapting can be in relation to both business risks and opportunities. For example, the less-than-obvious opportunity to deliver considerably better overall business results in a new store opening, even if it requires being 25% overstaffed, would fall on that side of the adaptive HCM coin.
There are dozens of other high-impact use cases that would fall under the category of adaptive HCM and what an organization can use to assess current or potential vendors. Here are some specific use cases:
In learning: A change in work objectives or priorities triggers the HCM system to emphasize learning experiences around the skills and competencies that will become more important in the new operating context and, conversely, de-emphasize training that will become less vital to business success going forward.
In workforce planning: Monthly business results / financial reporting indicates that hiring plans must be significantly reduced for the quarter, and managers with open requisitions are system-guided in their options for managing through the situation, such as potentially procuring short-term gig workers from preferred staffing vendors.
In recruiting: A certain manager is experiencing a high percentage of candidates dropping off or opting out when deep into the interview cycle, and after much investment has been made in that candidate, so the system initiates appropriate actions to diagnose further and coach that manager around what to change in their approach or process. Candidates pick up on positive and negative signals in the hiring process too, not just hiring managers, and these often influence their decision to remain in the process or not.
In total rewards: When financial results come in under plan, the system guides a manager in incentivizing those employees with upcoming salary increases to hopefully transition to more of a leveraged compensation package, or more upside potential from a variable compensation plan, instead of a very modest increase which could lead to disengagement.
In succession planning: The system notices that all of the successor candidates to a critical role are “ready now” vs. having a more distributed and diverse group of ready now and “ready later” candidates. This can lead to some of the most immediately promotable and valuable successor candidates leaving at some point. The system therefore guides management in identifying other ready later candidates and also encourages transparency with affected employees, or merely suggests a check-in meeting, to explore mutually beneficial options that can ameliorate this type of key employee retention risk situation.
As detailed in my HCM Market Agenda, three-quarters of organizations will identify organizational agility as an HR-led business imperative, with one-half adopting an examination of worker types, such as gig workers, as an integral priority. These workers are increasingly becoming a key element in any meaningful strategic workforce-planning activity, integrated with comparable precision to how employees are accounted for. This is basically an extension of how many enterprises responded to business challenges in 2020 and are still responding, often due to more unpredictable and fluid operating conditions. This extra precision will ultimately include the leveraging of artificial intelligence and ML (AI/ML) to guide organizations as to when to address a resourcing need with a regular employee, a gig worker or contractor, by transferring an incumbent, or even through a training intervention.
In adaptive HCM scenarios and use cases, systems might prove themselves to be highly adaptable in the form of automatically modifying the user experience to align with different contexts or goals, or seek the tracking of additional data items, or apply different processing or reporting rules, but again, my primary focus here is on the organization not the system adapting. Organizations that routinely and optimally react to workforce-related business risks and opportunities conveyed within a variety of cues, such as signals or data points, are much more apt to ascend the ranks within their industry peer groups.