Going by definitions, which are necessary but really aren’t that popular, we know management to be a job function or task set that is responsible for planning, coordinating, enabling and control of activities within the organisation. Mintzberg saw it as a cluster or roles that have to be assumed. All allude to a science of effort – at least in theory. Management has not yet been recognised as a profession in many of our jursidictions.

From a practical viewpoint, it is even less structured, since we see management as a position within organsiations, not a profession or specific skill set – with good reason; being a position, anyone sitting in that coveted spot (the corner office?) is a manager, regardless of whether that individual has any of the required abilities to [plan, coordinate, enable and control] or not. In fact, I have seen in some instances the only real skill being brought to the management position is complete compliance with those higher up the ladder. No room for planning etc. here – you have one job, and that is to tow the line.

Notwithstanding, the role or task-set spans the the entire gamut of planning through control, and manifests within organisation indifferent layers across the various functional and location divisions – straddling in each the layers of supervisory, management and executive positions. These we have heard in the context of operational, tactical and strategic level issues, in ascending degrees of importance respectively (if we go by the accompanying remuneration as a guide).

In terms of management roles today into tomorrow, we are seeing more and more the diffusion of intelligence technologies playing a supporting and in a growing number of instances an ‘advisory’ role to managers’ job functions and task-requirements. Our enterprise-wide applications are able to share information in a process that renders time and location irrelevant. The emergence of drone and robotic automation processes within operational functions is encroaching on large segments of supervisory roles. Programmed flags or notifications against established performance standards are rendering reporting and human intervention in the process of supervision comparatively expensive, inefficient and to a large extent unnecessary. It is driving firms to be flatter and leaner in their operating structures today. And that was only the beginning.

Looking ahead, the advent of Big Data, Analytics, Business Intelligence or any other term used to refer to data-intensive artificial intelligence, is poised to only amplify this trend, and distill the diffusion of technology further upwards through the layers of management-oriented positions. That software can on one part compile, collate and articulate data from various divisions in incomparable time is profound, and on the other part personalise and customise communication to various individuals – again in real time – on demand, is equally important to note. The supporting infrastructural developments – cloud and mobile computing in particular – are poised to deliver this intelligence to central decision makers as required. In fact, automated reporting posits a degree of consistency that is rarely emulated by human beings.

The computer-based learning systems – typically algorithms today – are moving into the space of assessing data, making decisions, executing automated functions based on the decisions, providing further detail and information access to persons using the system, and recording (and reporting) on performance and exceptions.
So how can this work in the management arena? In a pool of 5000 job applicants, a software can filter academic qualifications and performance, past experience, social media activities and other data streams to short-list candidates. It can provide online, remote simulations to prospective candidates and rank them accordingly. It can drive the orientation and training components to which candidates are exposed. With a pool of historical data on performance of different persons on a variety of tasks, an algorithm can select the best persons for the performance of a particular job based on their past experiences and performance on related tasks. Work schedules, performance registers and quota management are all automated computerised systems with which the candidate can interact and report. Meetings are already virtual, and the supporting documentation and ‘tangibles’ are accessible synchronously or asynchronously by anyone with approved access (Access is automatically assigned by employee rank and job description).

Financial, information access and even facility resources can be allocated (or booked) automatically on job assignment per candidate. Their performances are tracked by a system against milestones and comparative benchmarks. Bonuses, penalties and issuing of payments can be automatically configured and channel resources to the supporting structures (perhaps an employee’s bank account or company-issued credit card). At any (every?) point throughout execution the response expectations and projections based on the established plans and objectives (and environmental conditions) can be evaluated and adjusted – with the resulting changes communicated to the affected staff on their mobile devices.

All this to say that there are many management-centric functions which are being supported (or driven) by technology today. Which would be good news for larger organisations seeking to become more agile and responsive to market dynamics. It is also expected to be welcomed by entrepreneur-led and small and growing enterprises, if Greiner’s challenges throughout his Life Cycle Model is anything to go by. Firms facing severe shortage of skilled labour, or economies with an ageing workforce would also stand to benefit from this trend.
However, the shift is expected to be gradual from all indications. And it is currently difficult to see it as a replacement entirely – what with issues of diversity, capta, intuition and creativity still being core human tasks within management (or any) job functions. Yet from considering the tasks associated with management functions and the deployment of technology, the relationship seems set to become only more intertwined.

I wonder if IBM’s Watson would agree?