EX in Rewards : A use case to leverage Technological Intelligence for decision making

A few years ago, I shared a few thoughts that mulled around leveraging the 'Career Anchors' framework to custom-design 'Total Rewards' for enterprises. Over the years, Organizations were seen to adapt to the 'personalization' element when devising and driving their Rewards programs, while still being inclusive in approach at an aggregate.

Fast forward to the day, Talent Management as a professional thread has weathered (and still continues to) many a narrative regarding shifts and transformations at a business level - a pandemic-induced work model change, a push to decentralize data management, virtual collaboration with virtual avatars, shifting macroeconomic developments globally, what have you!

Amongst such developments, one paradigm that's evolving and is - in all probability, here to stay is machine-induced intelligence. Call it many terms at various phases of technology maturity - Machine Learning (ML), Artificial Intelligence (AI), Learning Models (LM), et al., the approach of having technology toolkit as a shotgun for a driver is gradually turning into a marksman for a sniper (analogically!) - one needs to complement the other to get things done.

All the narrative around the fad-versus-fact and the displacement-of-professions aside, as we step into a new calendar year ahead that may see a few impacts in continuum and a few afresh, I have a few thoughts on how may this newly-strengthening intelligence be leveraged to better drive some of the talent-related imperatives at an enterprise level.

Setting some fundamental reflections here - Machine-generated intelligence has now gained momentum in its leaping journey from being a mere reactive insight generator to a task handler under supervision to a key autonomous influencer into insights for decision making. That being said, the onus to 'train' such intelligence as it evolves is still the onerous duty of humankind. Speaking for the enterprise world, for the machine to be able to credibly deliver on any element - either a menial task or a major transformation, it needs to understand the underlying moving parts that are today the various functions and structures that make the Organization, the hard and soft flows therein and more importantly, what's known to the machine and what's unknown. In that, the professionals holding those portfolios will be a key set of contributors for the machine to learn its ropes. That being said, this is to be visualized more as a new-age apprentice being groomed to bring in more efficiencies and maybe a few sparks along the way, but the master is here to stay - probably to be the Sherpa for the larger picture or the Guru to guide the enterprise. Roles may transform, but technology-introduced intelligence, if harnessed right and leveraged efficiently, shall be a powerful strength to define business of the future.

With that premise, here is a quick envisioning of how may Organizations better decide on their approach towards Total Rewards by leveraging Artificial Intelligence. Three 'first principles' to note here : one - AI isn't merely about generating ideas using prompts, but a wider ambit that has data and predictability at its core; two - the element of human cognition needs to be continued to be exercised; and three - more importantly, any digital transformation to be effective needs to be exercised across all the moving parts of an enterprise and not as a sample for a specific lane.

Adopting AI would not be any exception to this. With the inherent nature of the system's credibility being how and what has it learnt to deliver, while the current reflections pertain to Total Rewards, the success would hinge on the overall digital journey of the Talent Management function.

Specific to the Total Rewards part, it is a well-observed phenomena that enterprises across have, in some form and sense, accepted the personalization imperative to suite a diverse set of team compositions. More than the demographic and data-provided personas of what may anchor workforce to their Organizations, there are a few inherent soft-parts to every enterprise that drive the motive to stay.

Plugging the various aspects of the employee's enterprise journey into a seamless theatre act (read as EX - Employee Experience) and deriving calculated predictions on the behaviors that may show up if the protagonist isn't getting the limelight that one's seeking for, powered by the decisions around tailor-stitching the available trick-box will enhance the Organization's ability to show purposefulness to the employee.

The technology backbone of Human Capital Management is seeing a promising shift to give an end-to-end experience to enterprise's employees - right from collaboration to skilling to performance to planning. Coupled with a machine-backed intelligence to integrate these touchpoints that moves from suggestions to advise, HCM platforms are increasingly being designed about the end-user (employee) than being about the enterprise.

At that, there is a huge potential to tap into the data or patterns that are being generated across such touchpoints. There are a fair set of interesting illustrations that show the journey of an employee when traversing within an enterprise, and of course as an extension, beyond.

Capturing such insights to train the marksman on how to read the winds, coupled by sharpening the sniper's scope and shot, will give enterprises a better comfort in packaging talent-rewards decisions.

The rear view mirror clearly shows a shift of narrative from Enterprise HCM to EX in large parts, of course, coupled with a few AI elements being incubated. It would be interesting to observe in the months ahead as to what may the next EX application release look like.

Here's to an interesting year ahead!

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Views expressed in this article are in my individual and personal capacity, and are not to be affiliated to any organisation, business or body of people.

No ownership or association is being claimed whatsoever to the quoted resources, entities or models.

Neither criticism nor corroboration intended.