Skip to main content

Why digital transformation is so important for smaller companies?

Major companies are not easy to transform digitally due to inertia of a large structure and deeply rooted traditions. Diverse IT landscape and distributed faculties make it especially difficult and expensive to migrate established business routine.

On the contrary, smaller companies can quickly transform their operations into digital field due to limited footprint and simpler workflows. For the same reason, an effect of innovation appears much quicker, is easier to measure and correct depending on obtained results.

Smaller companies simply cannot afford being inefficient, unlike their bigger counterparts, which have at least a temporal security belt of size and established market share. Quick technical and operational optimization offered by digital tools is the primary factor of growth for an emerging business.

Digital transformation offers to smaller companies unique competitive advantages by effectively replacing costly manual operations by affordable and adaptive automation of business processes. Digital operations in smaller companies are also much easier to control due to limited scale and complexity.

All these factors explain explosive growth of digital automation in smaller organizations far superseding respective evolution of large corporations and creating key competitive advantage of modern small business.

Comments

Popular posts from this blog

CaseAgile Will Show BPMN Data Exchange between Visio and Leading BPMN Tools

CaseAgile announces a new release of Enterprise Composer™ add-on for Microsoft® Visio, which supports transparent exchange of BPMN diagrams designed in Visio with leading BPMN tools. CaseAgile will demonstrate capabilities of Enterprise Composer™ on upcoming "BPMN IN ACTION" event organized by The Object Management Group (OMG) in Seattle at December 10, 2018. Enterprise Composer™ offers a seamless and efficient way for every Visio® user into the world of professional business process management (BPM) systems and process automation. By using Enterprise Composer™, Microsoft® Visio can create executable processes fully compatible with BPMN™ 2.0 standard from OMG®, which is the global de-facto etalon for modeling business processes and can be used both by businesses and IT. Every BPM practitioner working with Microsoft® Visio now can import BPM models created in most popular BPM suites directly into Visio® and can export models cre...

The role of BPM in the future of AI

BPM will become a key driving force behind further development of AI. Machine learning in business applications is blind without guiding role of BPM, similarly as a company is blind without its management. Despite impressive success, AI is still in its infancy in respect to its literal involvement into high level business processes. Concurrently with the increase of the role of AI in principal business contexts, there will grow demand for BPM in coordination and structuring of AI for business purposes . As in any other business domain, BPM is suitable and responsible for proper AI positioning in business. Without precise BPM governance, AI bears excessive risks for a business as any important but poorly managed and not transparent part of ongoing operations in an organization.

Gap between process model and real process

There always exists a discrepancy between a model of business process, however well designed and accurate, and real execution of this process in a business environment. The reason for this gap is an unforeseen depth and hidden details inherent to any real process. Real business model of organization is ultimately unlimited in its depth. Going from highest management levels, it descends to individual departments, client relations, production units, technical code of equipment and controllers etc. In vast majority of cases, it is impossible and senseless to build a complete model covering all and every fine detail of the business. Omitted lower layers of the model create (pseudo) random fluctuations during execution of the model. Real execution paths of a process never follow its model exactly. However, in case of the correct model, we can expect to see that an ensemble of execution paths statistically converges to the model as to its average path over a significant set of observation...